{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T14:54:56Z","timestamp":1785855296748,"version":"3.56.0"},"reference-count":273,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T00:00:00Z","timestamp":1663113600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T00:00:00Z","timestamp":1663113600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,12]]},"DOI":"10.1007\/s00521-022-07744-x","type":"journal-article","created":{"date-parts":[[2022,9,14]],"date-time":"2022-09-14T17:03:33Z","timestamp":1663175013000},"page":"20539-20573","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":62,"title":["Deep learning approaches and interventions for futuristic engineering in agriculture"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1560-1728","authenticated-orcid":false,"given":"Subir Kumar","family":"Chakraborty","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4850-4702","authenticated-orcid":false,"given":"Narendra Singh","family":"Chandel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1267-6043","authenticated-orcid":false,"given":"Dilip","family":"Jat","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mukesh Kumar","family":"Tiwari","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yogesh A.","family":"Rajwade","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"A.","family":"Subeesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2022,9,14]]},"reference":[{"key":"7744_CR1","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1038\/s41893-020-00622-1","volume":"3","author":"AT Stathers","year":"2020","unstructured":"Stathers AT, Holcroft D, Kitinoja L, Mvumi BM, English A, Omotilewa O, Kocher M, Ault J, Torero M (2020) A scoping review of interventions for crop postharvest loss reduction in sub-Saharan Africa and South. Nat Sustain 3:821\u2013835. https:\/\/doi.org\/10.1038\/s41893-020-00622-1","journal-title":"Nat Sustain"},{"key":"7744_CR2","unstructured":"World Population Prospects 2019 (UNDESA, 2019)"},{"key":"7744_CR3","unstructured":"Alexandratos N, Bruinsma J (2012) World agriculture towards 2030\/2050: the 2012 revision ESA Working Paper No. 12\u201303 FAO. https:\/\/www.fao.org\/3\/ap106e\/ap106e.pdf"},{"key":"7744_CR4","unstructured":"World Water Assessment Programme (Nations Unies) (2018) The united nations world water development report 2018 (United Nations Educational, Scientific and Cultural Organization, New York, United States) www.unwater.org\/publications\/world-water-development-report-2018"},{"key":"7744_CR5","unstructured":"FAO (2017) The future of food and agriculture-Trends and challenges. Rome"},{"key":"7744_CR6","unstructured":"Kray HA (2012) Farming for the future. The environmental sustainability of agriculture in a changing world pubdocs.worldbank.org\/en\/862271433768092396\/Holger-Kray-RO-SustainableAg-hkray ENG.pdf"},{"key":"7744_CR7","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1109\/JSTARS.2009.2037163","volume":"3","author":"JD Bolten","year":"2009","unstructured":"Bolten JD, Crow WT, Zhan X, Jackson TJ, Reynolds CA (2009) Evaluating the utility of remotely sensed soil moisture retrievals for operational agricultural drought monitoring. IEEE J Sel Top Appl Earth Obs Remote Sens 3:57\u201366. https:\/\/doi.org\/10.1109\/JSTARS.2009.2037163","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"7744_CR8","doi-asserted-by":"publisher","first-page":"144","DOI":"10.1080\/15481603.2017.1286725","volume":"54","author":"SK Padhee","year":"2017","unstructured":"Padhee SK, Nikam BR, Dutta S, Aggarwal SP (2017) Using satellite-based soil moisture to detect and monitor spatiotemporal traces of agricultural drought over Bundelkhand region of India. Giscience Remote Sens 54:144\u2013166","journal-title":"Giscience Remote Sens"},{"key":"7744_CR9","doi-asserted-by":"publisher","unstructured":"Osakabe Y, Osakabe K, Shinozaki K, Tran LS (2014) Response of plants to water stress. Front Plant Sci 5. https:\/\/doi.org\/10.3389\/fpls.2014.00086","DOI":"10.3389\/fpls.2014.00086"},{"key":"7744_CR10","doi-asserted-by":"publisher","first-page":"1403","DOI":"10.3390\/app11041403","volume":"11","author":"MH Kamarudin","year":"2021","unstructured":"Kamarudin MH, Ismail ZH, Saidi NB (2021) Deep Learning sensor fusion in plant water stress assessment: a comprehensive review. Appl Sci 11:1403. https:\/\/doi.org\/10.3390\/app11041403","journal-title":"Appl Sci"},{"key":"7744_CR11","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s13593-015-0338-6","volume":"36","author":"Q Chai","year":"2016","unstructured":"Chai Q, Gan Y, Zhao C, Xu HL, Waskom RM, Niu Y, Siddique KH (2016) Regulated deficit irrigation for crop production under drought stress. A Rev Agron Sustain Dev 36:3","journal-title":"A Rev Agron Sustain Dev"},{"key":"7744_CR12","doi-asserted-by":"publisher","first-page":"277","DOI":"10.1007\/s00521-018-3737-1","volume":"3131","author":"B Keswani","year":"2019","unstructured":"Keswani B, Mohapatra AG, Mohanty A, Khanna A, Rodrigues JJPC, Gupta D, Albuquerque VHC (2019) Adapting weather conditions based IoT enabled smart irrigation technique in precision agriculture mechanisms. Neural Comput Appl 3131:277\u2013292","journal-title":"Neural Comput Appl"},{"key":"7744_CR13","doi-asserted-by":"publisher","first-page":"68","DOI":"10.7324\/JABB.2016.40609","volume":"4","author":"V Hakkim","year":"2016","unstructured":"Hakkim V, Joseph E, Gokul A, Mufeedha K (2016) Precision farming: the future of Indian agriculture. J Appl Biol Biotechnol 4:68\u201372","journal-title":"J Appl Biol Biotechnol"},{"key":"7744_CR14","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1016\/j.future.2013.01.010","volume":"29","author":"J Gubbi","year":"2013","unstructured":"Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener Comput Syst 29:1645\u20131660","journal-title":"Future Gener Comput Syst"},{"key":"7744_CR15","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1016\/j.compag.2004.03.002","volume":"44","author":"VI Adamchuk","year":"2004","unstructured":"Adamchuk VI, Hummel JW, Morgan MT, Upadhyaya SK (2004) On-the-go soil sensors for precision agriculture. Comput Electron Agric 44:71\u201391","journal-title":"Comput Electron Agric"},{"issue":"18","key":"7744_CR16","doi-asserted-by":"publisher","first-page":"4613","DOI":"10.1073\/pnas.1716999115","volume":"115","author":"S Ghosal","year":"2018","unstructured":"Ghosal S, Blystone D, Singh AK, Ganapathysubramanian B, Singh A, Sarkar S (2018) An explainable deep machine vision framework for plant stress phenotyping. Proc Natl Acad Sci USA 115(18):4613\u20134618","journal-title":"Proc Natl Acad Sci USA"},{"key":"7744_CR17","doi-asserted-by":"publisher","first-page":"4843","DOI":"10.1109\/ACCESS.2020.3048415","volume":"9","author":"A Sharma","year":"2021","unstructured":"Sharma A, Jain A, Gupta P, Chowdary V (2021) Machine learning applications for precision agriculture: a comprehensive review. IEEE Access 9:4843\u20134873","journal-title":"IEEE Access"},{"issue":"4","key":"7744_CR18","doi-asserted-by":"publisher","first-page":"433","DOI":"10.3390\/su12041433","volume":"12","author":"XB Jin","year":"2020","unstructured":"Jin XB, Yu XH, Wang XY, Bai YT, Su TL, Kong JL (2020) Deep learning predictor for sustainable precision agriculture based on Internet of things system. Sustainability 12(4):433.","journal-title":"Sustainability"},{"key":"7744_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12518-013-0120-x","volume":"6","author":"F Nex","year":"2014","unstructured":"Nex F, Remondino F (2014) UAV for 3D mapping applications: a review. Appl Geomat 6:1\u201315","journal-title":"Appl Geomat"},{"key":"7744_CR20","first-page":"1","volume-title":"Advances in Agronomy","author":"FJ Pierce","year":"1999","unstructured":"Pierce FJ, Nowak P (1999) Aspects of precision agriculture. Advances in Agronomy, vol 67. Academic, New York, NY, USA, pp 1\u201385"},{"key":"7744_CR21","doi-asserted-by":"publisher","first-page":"e318","DOI":"10.1016\/S2542-5196(19)30095-6","volume":"3","author":"D Mason-D\u2019Croz","year":"2019","unstructured":"Mason-D\u2019Croz D, Bogard JR, Sulser TB, Cenacchi N, Dunston S, Herrero M, Wiebe K (2019) Gaps between fruit and vegetable production, demand, and recommended consumption at global and national levels: an integrated modelling study. Lancet Planet Health 3:e318\u2013e329","journal-title":"Lancet Planet Health"},{"key":"7744_CR22","unstructured":"Scialabba NE, Hoogeveen J, Turbe A, Tubiello FN (2013) Food wastage footprints: impact on natural resources. Summary Report (FAO) pp. 26"},{"key":"7744_CR23","doi-asserted-by":"publisher","first-page":"103138","DOI":"10.1016\/j.infrared.2019.103138","volume":"104","author":"Y Li","year":"2020","unstructured":"Li Y, Jin G, Jiang X, Yi S, Tian X (2020) Non-destructive determination of soluble solids content using a multi-region combination model in hybrid citrus. Infrared Phys Technol 104:103138. https:\/\/doi.org\/10.1016\/j.infrared.2019.103138","journal-title":"Infrared Phys Technol"},{"key":"7744_CR24","doi-asserted-by":"publisher","first-page":"106816","DOI":"10.1016\/j.foodcont.2019.106816","volume":"107","author":"DF Barbin","year":"2020","unstructured":"Barbin DF, Badaro AT, Honorato DCB, Ida EY, Shimokomaki M (2020) Identification of Turkey meat and processed products using near infrared spectroscopy. Food Control 107:106816. https:\/\/doi.org\/10.1016\/j.foodcont.2019.106816","journal-title":"Food Control"},{"key":"7744_CR25","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.foodchem.2019.05.060","volume":"294","author":"S Behkami","year":"2019","unstructured":"Behkami S, Zain SM, Gholami M, Khir MFA (2019) Classification of cow milk using artificial neural network developed from the spectral data of single and three detector spectrophotometers. Food Chem 294:309\u2013315. https:\/\/doi.org\/10.1016\/j.foodchem.2019.05.060","journal-title":"Food Chem"},{"key":"7744_CR26","doi-asserted-by":"publisher","first-page":"196","DOI":"10.1016\/j.foodchem.2017.09.058","volume":"242","author":"PS Sampaio","year":"2018","unstructured":"Sampaio PS, Soares A, Castanho A, Almeida AS, Oliveira J, Brites C (2018) Optimization of rice amylose determination by NIR-spectroscopy using PLS chemometrics algorithms. Food Chem 242:196\u2013204. https:\/\/doi.org\/10.1016\/j.foodchem.2017.09.058","journal-title":"Food Chem"},{"key":"7744_CR27","doi-asserted-by":"publisher","first-page":"431","DOI":"10.1016\/j.tifs.2021.04.008","volume":"112","author":"X Zhang","year":"2021","unstructured":"Zhang X, Yang J, Lin T, Ying Y (2021) Food and agro-product quality evaluation based on spectroscopy and deep learning: a review. Trends Food Sci Technol 112:431\u2013441","journal-title":"Trends Food Sci Technol"},{"key":"7744_CR28","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u2013444. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"key":"7744_CR29","doi-asserted-by":"publisher","first-page":"1058","DOI":"10.3390\/s19051058","volume":"19","author":"YY Zheng","year":"2019","unstructured":"Zheng YY, Kong JL, Jin XB, Wang XY, Su TL, Zuo M (2019) Cropdeep: the crop vision dataset for deep-learning-based classification and detection in precision agriculture. Sensors 19:1058","journal-title":"Sensors"},{"key":"7744_CR30","doi-asserted-by":"publisher","first-page":"2572","DOI":"10.1109\/TITS.2017.2755684","volume":"19","author":"J Xu","year":"2017","unstructured":"Xu J, Rahmatizadeh R, Boloni L, B\u00f6l\u00f6ni L, Turgut D (2017) Real-time prediction of taxi demand using recurrent neural networks. IEEE Trans Intell Transp Syst 19:2572\u20132581","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"7744_CR31","doi-asserted-by":"publisher","first-page":"6893","DOI":"10.1007\/s00521-018-3523-0","volume":"31","author":"CO Sakar","year":"2019","unstructured":"Sakar CO, Polat SO, Katircioglu M, Kastro Y (2019) Real-time prediction of online shoppers\u2019 purchasing intention using multilayer perceptron and LSTM recurrent neural networks. Neural Comput Appl 31:6893\u20136908","journal-title":"Neural Comput Appl"},{"key":"7744_CR32","doi-asserted-by":"publisher","first-page":"6085","DOI":"10.1038\/s41598-018-24271-9","volume":"8","author":"Z Che","year":"2018","unstructured":"Che Z, Purushotham S, Cho K, Sonta D, Liu Y (2018) Recurrent neural networks for multivariate time series with missing values. Sci Rep 8:6085","journal-title":"Sci Rep"},{"key":"7744_CR33","unstructured":"Salakhutdinov R, Hinton G (2009) Deep boltzmann machines. Aistats 1: 448-455"},{"key":"7744_CR34","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent P, Larochelle H, Lajoie I, Bengio Y, Manzagol P (2010) A stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion pierre-antoine manzagol. J Mach Learn Res 11:3371\u20133408","journal-title":"J Mach Learn Res"},{"key":"7744_CR35","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","volume":"147","author":"A Kamilaris","year":"2018","unstructured":"Kamilaris A, Prenafeta-Bold\u00fa FX (2018) Deep learning in agriculture: a survey. Comput Electron Agric 147:70\u201390","journal-title":"Comput Electron Agric"},{"key":"7744_CR36","doi-asserted-by":"publisher","first-page":"4312","DOI":"10.1039\/C9AN00913B","volume":"144","author":"X Chen","year":"2019","unstructured":"Chen X, Xie L, He Y, Guan T, Zhou X, Wang B, Feng G, Yu H, Ji Y (2019) Fast and accurate decoding of Raman spectra-encoded suspension arrays using deep learning. Analyst 144:4312\u20134319. https:\/\/doi.org\/10.1039\/C9AN00913B","journal-title":"Analyst"},{"key":"7744_CR37","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1186\/s13007-018-0273-z","volume":"14","author":"J Ubbens","year":"2018","unstructured":"Ubbens J, Cieslak M, Prusinkiewicz P, Stavness I (2018) The use of plant models in deep learning: an application to leaf counting in rosette plants. Plant Methods 14:6. https:\/\/doi.org\/10.1186\/s13007-018-0273-z","journal-title":"Plant Methods"},{"key":"7744_CR38","doi-asserted-by":"publisher","first-page":"1190","DOI":"10.3389\/fpls.2017.01190","volume":"8","author":"JR Ubbens","year":"2017","unstructured":"Ubbens JR, Stavness I (2017) Deep plant phenomics: a deep learning platform for complex plant phenotyping tasks. Front Plant Sci 8:1190. https:\/\/doi.org\/10.3389\/fpls.2017.01190","journal-title":"Front Plant Sci"},{"key":"7744_CR39","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3389\/fpls.2016.01419","volume":"7","author":"SP Mohanty","year":"2016","unstructured":"Mohanty SP, Hughes DP, Salath\u00e9 M (2016) Using deep learning for image-based plant disease detection. Front Plant Sci 7:1\u20137. https:\/\/doi.org\/10.3389\/fpls.2016.01419","journal-title":"Front Plant Sci"},{"key":"7744_CR40","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1093\/gigascience\/gix083","volume":"6","author":"MP Pound","year":"2017","unstructured":"Pound MP, Burgess AJ, Wilson MH, Atkinson JA, Grifths M, Jackson AS, Bulat A, Tzimiropoulos G, Wells DM, Murchie EH, Pridmore TP, French AP (2017) Deep machine learning provides state-of-the-art performance in image-based plant phenotyping. GigaScience 6:1\u201310. https:\/\/doi.org\/10.1093\/gigascience\/gix083","journal-title":"GigaScience"},{"key":"7744_CR41","doi-asserted-by":"publisher","first-page":"1015","DOI":"10.3745\/JIPS.04.0187","volume":"16","author":"C Ren","year":"2020","unstructured":"Ren C, Dae-Kyoo Kim DK, Jeong D (2020) A survey of deep learning in agriculture: techniques and their applications. J Inf Process Syst 16:1015\u20131033. https:\/\/doi.org\/10.3745\/JIPS.04.0187","journal-title":"J Inf Process Syst"},{"key":"7744_CR42","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1109\/MIM.2021.9400957","volume":"24","author":"G Thomas","year":"2021","unstructured":"Thomas G, Balocco S, Mann D, Simundsson A, Khorasani N (2021) Intelligent agricultural machinery using deep learning. IEEE Instrum Meas Mag 24:94\u2013100","journal-title":"IEEE Instrum Meas Mag"},{"issue":"3","key":"7744_CR43","doi-asserted-by":"publisher","first-page":"767","DOI":"10.3390\/smartcities3030039","volume":"3","author":"WH Su","year":"2020","unstructured":"Su WH (2020) Advanced machine learning in point spectroscopy, RGB-and hyperspectral-imaging for automatic discriminations of crops and weeds: a review. Smart Cities 3(3):767\u2013792. https:\/\/doi.org\/10.3390\/smartcities3030039","journal-title":"Smart Cities"},{"issue":"2","key":"7744_CR44","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1080\/05704928.2013.811081","volume":"49","author":"JM Soriano-Disla","year":"2014","unstructured":"Soriano-Disla JM, Janik LJ, Viscarra Rossel RA, Macdonald LM, McLaughlin MJ (2014) The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties. Appl Spectrosc Rev 49(2):139\u2013186","journal-title":"Appl Spectrosc Rev"},{"key":"7744_CR45","doi-asserted-by":"publisher","first-page":"e00198","DOI":"10.1016\/j.geodrs.2018.e00198","volume":"16","author":"J Padarian","year":"2019","unstructured":"Padarian J, Minasny B, McBratney AB (2019) Using deep learning to predict soil properties from regional spectral data. Geoderma Reg 16:e00198","journal-title":"Geoderma Reg"},{"key":"7744_CR46","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1016\/j.neucom.2015.09.116","volume":"187","author":"Y Guo","year":"2016","unstructured":"Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27\u201348. https:\/\/doi.org\/10.1016\/j.neucom.2015.09.116","journal-title":"Neurocomputing"},{"key":"7744_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.patcog.2017.05.015","volume":"71","author":"SH Lee","year":"2017","unstructured":"Lee SH, Chan CS, Mayo SJ, Remagnino P (2017) How deep learning extracts and learns leaf features for plant classification. Pattern Recognit 71:1\u201313. https:\/\/doi.org\/10.1016\/j.patcog.2017.05.015","journal-title":"Pattern Recognit"},{"key":"7744_CR48","doi-asserted-by":"publisher","first-page":"e7068349","DOI":"10.1155\/2018\/7068349","volume":"2018","author":"A Voulodimos","year":"2018","unstructured":"Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci 2018:e7068349. https:\/\/doi.org\/10.1155\/2018\/7068349","journal-title":"Comput Intell Neurosci"},{"key":"7744_CR49","doi-asserted-by":"publisher","first-page":"1793","DOI":"10.1111\/1541-4337.12492","volume":"18","author":"L Zhou","year":"2019","unstructured":"Zhou L, Zhang C, Liu F, Qiu Z, He Y (2019) Application of deep learning in food: a review. Compr Rev Food Sci Food Saf 18:1793\u20131811. https:\/\/doi.org\/10.1111\/1541-4337.12492","journal-title":"Compr Rev Food Sci Food Saf"},{"key":"7744_CR50","doi-asserted-by":"publisher","first-page":"e00938","DOI":"10.1016\/j.heliyon.2018.e00938","volume":"4","author":"OI Abiodun","year":"2018","unstructured":"Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H (2018) State-of-the-art in artificial neural network applications: a survey. Heliyon 4:e00938. https:\/\/doi.org\/10.1016\/j.heliyon.2018.e00938","journal-title":"Heliyon"},{"key":"7744_CR51","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/B978-0-323-90198-7.00005-7","volume-title":"Principles and Labs for Deep Learning","author":"SC Huang","year":"2021","unstructured":"Huang SC, Le TH (2021) Multi-category classification problem. In: Huang SC, Le TH (eds) Principles and Labs for Deep Learning. Academic Press, pp 81\u2013116. https:\/\/doi.org\/10.1016\/B978-0-323-90198-7.00005-7"},{"key":"7744_CR52","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86:2278\u20132324","journal-title":"Proc IEEE"},{"key":"7744_CR53","first-page":"1097","volume-title":"Advances in neural information processing systems 25","author":"A Krizhevsky","year":"2012","unstructured":"Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ (eds) Advances in neural information processing systems 25. Curran Associates Inc, New York, pp 1097\u20131105"},{"key":"7744_CR54","unstructured":"Simonyan K, Zisserman A (2015) Very deep convolutional networks for large-scale image recognition. ArXiv14091556 Cs"},{"key":"7744_CR55","doi-asserted-by":"crossref","unstructured":"Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. pp 2818\u20132826","DOI":"10.1109\/CVPR.2016.308"},{"key":"7744_CR56","doi-asserted-by":"crossref","unstructured":"Szegedy C, Liu W, Jia Y, (2014) Going deeper with convolutions. ArXiv14094842 Cs","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"7744_CR57","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2015) Deep residual learning for image recognition. ArXiv151203385 Cs","DOI":"10.1109\/CVPR.2016.90"},{"key":"7744_CR58","unstructured":"Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv"},{"key":"7744_CR59","doi-asserted-by":"crossref","unstructured":"Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. ArXiv161002357 Cs","DOI":"10.1109\/CVPR.2017.195"},{"key":"7744_CR60","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, van der Maaten L, Weinberger KQ (2018) Densely connected convolutional networks. ArXiv160806993 Cs","DOI":"10.1109\/CVPR.2017.243"},{"key":"7744_CR61","unstructured":"Redmon J, Farhadi A (2018) YOLOv3: An incremental improvement. ArXiv180402767 Cs"},{"key":"7744_CR62","doi-asserted-by":"publisher","unstructured":"Li Z, Liu F, Yang W, Peng S, Zhou J (2021) A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Trans Neural Netw Learn Syst 1\u201321 https:\/\/doi.org\/10.1109\/TNNLS.2021.3084827","DOI":"10.1109\/TNNLS.2021.3084827"},{"key":"7744_CR63","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1016\/j.csl.2014.09.005","volume":"30","author":"W De Mulder","year":"2015","unstructured":"De Mulder W, Bethard S, Moens M-F (2015) A survey on the application of recurrent neural networks to statistical language modeling. Comput Speech Lang 30:61\u201398. https:\/\/doi.org\/10.1016\/j.csl.2014.09.005","journal-title":"Comput Speech Lang"},{"key":"7744_CR64","doi-asserted-by":"publisher","first-page":"430","DOI":"10.1007\/s11704-018-8052-6","volume":"14","author":"G Zhang","year":"2020","unstructured":"Zhang G, Liu Y, Jin X (2020) A survey of autoencoder-based recommender systems. Front Comput Sci 14:430\u2013450. https:\/\/doi.org\/10.1007\/s11704-018-8052-6","journal-title":"Front Comput Sci"},{"key":"7744_CR65","doi-asserted-by":"publisher","first-page":"714","DOI":"10.3389\/fpls.2019.00714","volume":"10","author":"C Zhao","year":"2019","unstructured":"Zhao C, Zhang Y, Du J, Guo X, Wen W, Gu S, Wang J, Fan J (2019) Crop phenomics: current status and perspectives. Front Plant Sci 10:714","journal-title":"Front Plant Sci"},{"issue":"11","key":"7744_CR66","doi-asserted-by":"publisher","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","volume":"30","author":"ZQ Zhao","year":"2019","unstructured":"Zhao ZQ, Zheng P, Xu ST, Wu X (2019) Object detection with deep learning: a review. IEEE Trans Neural Netw Learn Syst 30(11):3212\u20133232","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7744_CR67","doi-asserted-by":"crossref","unstructured":"Jiang Y, Li C (2020) Convolutional neural networks for image-based high-throughput plant phenotyping: a review. Plant Phenomics, 2020","DOI":"10.34133\/2020\/4152816"},{"key":"7744_CR68","doi-asserted-by":"publisher","first-page":"108650","DOI":"10.1016\/j.measurement.2020.108650","volume":"173","author":"S Azimi","year":"2021","unstructured":"Azimi S, Kaur T, Gandhi TK (2021) A deep learning approach to measure stress level in plants due to nitrogen deficiency. Meas 173:108650. https:\/\/doi.org\/10.1016\/j.measurement.2020.108650","journal-title":"Meas"},{"key":"7744_CR69","doi-asserted-by":"publisher","first-page":"102353","DOI":"10.1016\/j.jag.2021.102353","volume":"101","author":"H Wang","year":"2021","unstructured":"Wang H, Seaborn T, Wang Z, Caudill CC, Link TE (2021) Modeling tree canopy height using machine learning over mixed vegetation landscapes. Int J Appl Earth Obs Geoinf 101:102353. https:\/\/doi.org\/10.1016\/j.jag.2021.102353","journal-title":"Int J Appl Earth Obs Geoinf"},{"issue":"1","key":"7744_CR70","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13007-017-0265-4","volume":"13","author":"M \u0160ulc","year":"2017","unstructured":"\u0160ulc M, Matas J (2017) Fine-grained recognition of plants from images. Plant Methods 13(1):1\u201314. https:\/\/doi.org\/10.1186\/s13007-017-0265-4","journal-title":"Plant Methods"},{"issue":"11","key":"7744_CR71","doi-asserted-by":"publisher","first-page":"giz123","DOI":"10.1093\/gigascience\/giz123","volume":"8","author":"R Yasrab","year":"2019","unstructured":"Yasrab R, Atkinson JA, Wells DM, French AP, Pridmore TP, Pound MP (2019) RootNav 2.0: Deep learning for automatic navigation of complex plant root architectures. GigaScience 8(11):giz123. https:\/\/doi.org\/10.1093\/gigascience\/giz123","journal-title":"GigaScience"},{"key":"7744_CR72","doi-asserted-by":"publisher","unstructured":"O\u2019Mahony N, Campbell S, Carvalho A, Harapanahalli S, Hernandez GV, Krpalkova L, Riordan D, Walsh J (2019, April) Deep learning vs. traditional computer vision. Sci Info Conf (pp. 128-144). Springer, Chamhttps:\/\/doi.org\/10.1007\/978-3-030-17795-9_10","DOI":"10.1007\/978-3-030-17795-9_10"},{"issue":"3","key":"7744_CR73","doi-asserted-by":"publisher","first-page":"430","DOI":"10.3390\/agriengineering2030029","volume":"2","author":"Z Gao","year":"2020","unstructured":"Gao Z, Luo Z, Zhang W, Lv Z, Xu Y (2020) Deep learning application in plant stress imaging: a review. Agri Eng 2(3):430\u2013446. https:\/\/doi.org\/10.3390\/agriengineering2030029","journal-title":"Agri Eng"},{"key":"7744_CR74","doi-asserted-by":"publisher","unstructured":"Dias C A, Bueno J C, Borges E N, Botelho S S, Dimuro G P, Lucca G, Fernand\u00e9z J, Bustince H, Junior P L J D (2018) Using the Choquet integral in the pooling layer in deep learning networks. In\u00a0North american fuzzy information processing society annual conference\u00a0(pp. 144\u2013154). Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-95312-0_13","DOI":"10.1007\/978-3-319-95312-0_13"},{"issue":"4","key":"7744_CR75","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/s40030-019-00390-y","volume":"100","author":"W Qiufeng","year":"2019","unstructured":"Qiufeng W, Zhang K, Meng J (2019) Identification of soybean leaf diseases via deep learning. J Inst Eng (India) Series A 100(4):659\u2013666. https:\/\/doi.org\/10.1007\/s40030-019-00390-y","journal-title":"J Inst Eng (India) Series A"},{"issue":"6","key":"7744_CR76","doi-asserted-by":"publisher","first-page":"691","DOI":"10.3390\/rs11060691","volume":"11","author":"J Wu","year":"2019","unstructured":"Wu J, Yang G, Yang X, Xu B, Han L, Zhu Y (2019) Automatic counting of in situ rice seedlings from UAV images based on a deep fully convolutional neural network. Remote Sens 11(6):691","journal-title":"Remote Sens"},{"issue":"1","key":"7744_CR77","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13007-020-00698-y","volume":"16","author":"Y Jiang","year":"2020","unstructured":"Jiang Y, Li C, Xu R, Sun S, Robertson JS, Paterson AH (2020) DeepFlower: a deep learning-based approach to characterize flowering patterns of cotton plants in the field. Plant Methods 16(1):1\u201317","journal-title":"Plant Methods"},{"issue":"1","key":"7744_CR78","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13007-019-0510-0","volume":"15","author":"W Wu","year":"2019","unstructured":"Wu W, Liu T, Zhou P, Yang T, Li C, Zhong X, Sun C, Liu S, Guo W (2019) Image analysis-based recognition and quantification of grain number per panicle in rice. Plant Methods 15(1):1\u201314","journal-title":"Plant Methods"},{"issue":"14","key":"7744_CR79","doi-asserted-by":"publisher","first-page":"2822","DOI":"10.3390\/rs13142822","volume":"13","author":"Z Lin","year":"2021","unstructured":"Lin Z, Guo W (2021) Cotton stand counting from unmanned aerial system imagery using Mobilenet and Centernet deep learning models. Remote Sens 13(14):2822","journal-title":"Remote Sens"},{"key":"7744_CR80","doi-asserted-by":"publisher","first-page":"1929","DOI":"10.3389\/fpls.2020.541960","volume":"11","author":"H Lu","year":"2020","unstructured":"Lu H, Cao Z (2020) Tasselnetv2+: A fast implementation for high-throughput plant counting from high-resolution RGB imagery. Front Plant Sci 11:1929","journal-title":"Front Plant Sci"},{"key":"7744_CR81","doi-asserted-by":"publisher","first-page":"225","DOI":"10.1016\/j.agrformet.2018.10.013","volume":"264","author":"S Madec","year":"2019","unstructured":"Madec S, Jin X, Lu H, De Solan B, Liu S, Duyme F, Heritier E, Baret F (2019) Ear density estimation from high resolution RGB imagery using deep learning technique. Agric Meteorol 264:225\u2013234","journal-title":"Agric Meteorol"},{"issue":"2","key":"7744_CR82","doi-asserted-by":"publisher","first-page":"338","DOI":"10.3390\/rs12020338","volume":"12","author":"Y Liu","year":"2020","unstructured":"Liu Y, Cen C, Che Y, Ke R, Ma Y, Ma Y (2020) Detection of maize tassels from UAV RGB imagery with faster R-CNN. Remote Sens 12(2):338","journal-title":"Remote Sens"},{"issue":"3","key":"7744_CR83","doi-asserted-by":"publisher","first-page":"1754","DOI":"10.1111\/gcb.14885","volume":"26","author":"H Jiang","year":"2020","unstructured":"Jiang H, Hu H, Zhong R, Xu J, Xu J, Huang J, Wang S, Ying Y, Lin T (2020) A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: a case study of the US Corn Belt at the county level. Global Change Boil 26(3):1754\u20131766","journal-title":"Global Change Boil"},{"key":"7744_CR84","doi-asserted-by":"publisher","first-page":"1750","DOI":"10.3389\/fpls.2019.01750","volume":"10","author":"S Khaki","year":"2020","unstructured":"Khaki S, Wang L, Archontoulis SV (2020) A CNN-RNN framework for crop yield prediction. Front in Plant Sci 10:1750","journal-title":"Front in Plant Sci"},{"issue":"1","key":"7744_CR85","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-020-80820-1","volume":"11","author":"M Shahhosseini","year":"2021","unstructured":"Shahhosseini M, Hu G, Huber I, Archontoulis SV (2021) Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt. Sci Rep 11(1):1\u201315","journal-title":"Sci Rep"},{"key":"7744_CR86","doi-asserted-by":"publisher","unstructured":"Sobayo R, Wu HH, Ray R, Qian L (2018, April) Integration of convolutional neural network and thermal images into soil moisture estimation. In\u00a02018 1st international conference on data intelligence and security (ICDIS) pp. 207\u2013210. IEEE. https:\/\/doi.org\/10.1109\/ICDIS.2018.00041","DOI":"10.1109\/ICDIS.2018.00041"},{"issue":"10","key":"7744_CR87","doi-asserted-by":"publisher","first-page":"5353","DOI":"10.1007\/s00521-020-05325-4","volume":"33","author":"NS Chandel","year":"2021","unstructured":"Chandel NS, Chakraborty SK, Rajwade YA, Dubey K, Tiwari MK, Jat D (2021) Identifying crop water stress using deep learning models. Neural Comput Appl 33(10):5353\u20135367. https:\/\/doi.org\/10.1007\/s00521-020-05325-4","journal-title":"Neural Comput Appl"},{"issue":"9","key":"7744_CR88","doi-asserted-by":"publisher","first-page":"2022","DOI":"10.3390\/s17092022","volume":"17","author":"A Fuentes","year":"2017","unstructured":"Fuentes A, Yoon S, Kim SC, Park DS (2017) A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sens 17(9):2022. https:\/\/doi.org\/10.3390\/s17092022","journal-title":"Sens"},{"issue":"2","key":"7744_CR89","doi-asserted-by":"publisher","first-page":"256","DOI":"10.3390\/sym11020256","volume":"11","author":"J An","year":"2019","unstructured":"An J, Li W, Li M, Cui S, Yue H (2019) Identification and classification of maize drought stress using deep convolutional neural network. Symmetry 11(2):256. https:\/\/doi.org\/10.3390\/sym11020256","journal-title":"Symmetry"},{"key":"7744_CR90","doi-asserted-by":"crossref","unstructured":"Aich S, Stavness I (2017) Leaf counting with deep convolutional and deconvolutional networks. In proceedings of the IEEE international conference on computer vision workshops, pp. 2080\u20132089","DOI":"10.1109\/ICCVW.2017.244"},{"key":"7744_CR91","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.knosys.2017.07.028","volume":"134","author":"Y Kaneda","year":"2017","unstructured":"Kaneda Y, Shibata S, Mineno H (2017) Multi-modal sliding window-based support vector regression for predicting plant water stress. Knowl Based Syst 134:135\u2013148. https:\/\/doi.org\/10.1016\/j.knosys.2017.07.028","journal-title":"Knowl Based Syst"},{"key":"7744_CR92","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1016\/j.aiia.2020.03.001","volume":"4","author":"BS Anami","year":"2020","unstructured":"Anami BS, Malvade NN, Palaiah S (2020) Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images. AI Agri 4:12\u201320. https:\/\/doi.org\/10.1016\/j.aiia.2020.03.001","journal-title":"AI Agri"},{"key":"7744_CR93","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.compag.2016.07.003","volume":"127","author":"GL Grinblat","year":"2016","unstructured":"Grinblat GL, Uzal LC, Larese MG, Granitto PM (2016) Deep learning for plant identification using vein morphological patterns. Comput Electron Agric 127:418\u2013424. https:\/\/doi.org\/10.1016\/j.compag.2016.07.003","journal-title":"Comput Electron Agric"},{"issue":"1","key":"7744_CR94","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13007-017-0224-0","volume":"13","author":"H Lu","year":"2017","unstructured":"Lu H, Cao Z, Xiao Y, Zhuang B, Shen C (2017) TasselNet: counting maize tassels in the wild via local counts regression network. Plant Methods 13(1):1\u201317. https:\/\/doi.org\/10.1186\/s13007-017-0224-0","journal-title":"Plant Methods"},{"issue":"1","key":"7744_CR95","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13007-020-00651-z","volume":"16","author":"H Zou","year":"2020","unstructured":"Zou H, Lu H, Li Y, Liu L, Cao Z (2020) Maize tassels detection: a benchmark of the state of the art. Plant Methods 16(1):1\u201315","journal-title":"Plant Methods"},{"key":"7744_CR96","doi-asserted-by":"publisher","unstructured":"Alzadjali A, Alali MH, Sivakumar ANV, Deogun JS, Scott S, Schnable JC, Shi Y (2021) Maize tassel detection from UAV imagery using deep learning. Front Robot AIhttps:\/\/doi.org\/10.3389\/frobt.2021.600410","DOI":"10.3389\/frobt.2021.600410"},{"key":"7744_CR97","doi-asserted-by":"crossref","unstructured":"Baweja HS, Parhar T, Mirbod O, Nuske S (2018) Stalknet: A deep learning pipeline for high-throughput measurement of plant stalk count and stalk width. In Field and service robotics (pp. 271-284). Springer, Cham","DOI":"10.1007\/978-3-319-67361-5_18"},{"issue":"24","key":"7744_CR98","doi-asserted-by":"publisher","first-page":"2939","DOI":"10.3390\/rs11242939","volume":"11","author":"L Malambo","year":"2019","unstructured":"Malambo L, Popescu S, Ku NW, Rooney W, Zhou T, Moore S (2019) A deep learning semantic segmentation-based approach for field-level sorghum panicle counting. Remote Sens 11(24):2939","journal-title":"Remote Sens"},{"key":"7744_CR99","doi-asserted-by":"crossref","unstructured":"Teramoto S, Uga Y (2020) A deep learning-based phenotypic analysis of rice root distribution from field images. Plant Phenomics, 2020","DOI":"10.34133\/2020\/3194308"},{"issue":"4","key":"7744_CR100","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1080\/08839514.2017.1315516","volume":"31","author":"M Brahimi","year":"2017","unstructured":"Brahimi M, Boukhalfa K, Moussaoui A (2017) Deep learning for tomato diseases: classification and symptoms visualization. Appl Artif Intell 31(4):299\u2013315","journal-title":"Appl Artif Intell"},{"key":"7744_CR101","doi-asserted-by":"publisher","first-page":"369","DOI":"10.1016\/j.compag.2017.09.012","volume":"142","author":"J Lu","year":"2017","unstructured":"Lu J, Hu J, Zhao G, Mei F, Zhang C (2017) An in-field automatic wheat disease diagnosis system. Comput Electron Agric 142:369\u2013379. https:\/\/doi.org\/10.1016\/j.compag.2017.09.012","journal-title":"Comput Electron Agric"},{"key":"7744_CR102","doi-asserted-by":"publisher","first-page":"30370","DOI":"10.1109\/ACCESS.2018.2844405","volume":"6","author":"X Zhang","year":"2018","unstructured":"Zhang X, Qiao Y, Meng F, Fan C, Zhang M (2018) Identification of maize leaf diseases using improved deep convolutional neural networks. IEEE Access 6:30370\u201330377. https:\/\/doi.org\/10.1109\/ACCESS.2018.2844405","journal-title":"IEEE Access"},{"key":"7744_CR103","doi-asserted-by":"publisher","first-page":"378","DOI":"10.1016\/j.neucom.2017.06.023","volume":"267","author":"Y Lu","year":"2017","unstructured":"Lu Y, Yi S, Zeng N, Liu Y, Zhang Y (2017) Identification of rice diseases using deep convolutional neural networks. Neurocomputing 267:378\u2013384. https:\/\/doi.org\/10.1016\/j.neucom.2017.06.023","journal-title":"Neurocomputing"},{"key":"7744_CR104","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.compind.2019.02.003","volume":"108","author":"S Coulibaly","year":"2019","unstructured":"Coulibaly S, Kamsu-Foguem B, Kamissoko D, Traore D (2019) Deep neural networks with transfer learning in millet crop images. Comput Ind 108:115\u2013120. https:\/\/doi.org\/10.1016\/j.compind.2019.02.003","journal-title":"Comput Ind"},{"key":"7744_CR105","doi-asserted-by":"publisher","unstructured":"Militante SV, Gerardo BD, Medina RP (2019) Sugarcane disease recognition using deep learning. In\u00a02019 IEEE Eurasia conference on IOT, communication and engineering (ECICE) pp. 575\u2013578. IEEE. https:\/\/doi.org\/10.1109\/ECICE47484.2019.8942690","DOI":"10.1109\/ECICE47484.2019.8942690"},{"issue":"8","key":"7744_CR106","first-page":"579","volume":"9","author":"H Avila-George","year":"2018","unstructured":"Avila-George H, Valdez-Morones T, P\u00e9rez-Espinosa H, Acevedo-Ju\u00e1rez B, Castro W (2018) Using artificial neural networks for detecting damage on tobacco leaves caused by blue mold. Int J Adv Comput Sci Appl 9(8):579\u2013583","journal-title":"Int J Adv Comput Sci Appl"},{"key":"7744_CR107","doi-asserted-by":"publisher","unstructured":"Jenifa A, Ramalakshmi R, Ramachandran V (2019) Cotton leaf disease classification using deep convolution neural network for sustainable cotton production. In 2019 IEEE international conference on clean energy and energy efficient electronics circuit for sustainable development (INCCES) pp. 1\u20133. IEEE. https:\/\/doi.org\/10.1109\/INCCES47820.2019.9167715","DOI":"10.1109\/INCCES47820.2019.9167715"},{"key":"7744_CR108","doi-asserted-by":"publisher","unstructured":"Hasan MZ, Ahamed MS, Rakshit A, Hasan KZ (2019) Recognition of Jute diseases by leaf image classification using convolutional neural network. In 2019 10th international conference on computing, communication and networking technologies (ICCCNT) pp. 1\u20135. IEEE. https:\/\/doi.org\/10.1109\/ICCCNT45670.2019.8944907","DOI":"10.1109\/ICCCNT45670.2019.8944907"},{"key":"7744_CR109","doi-asserted-by":"publisher","first-page":"105162","DOI":"10.1016\/j.compag.2019.105162","volume":"169","author":"JG Esgario","year":"2020","unstructured":"Esgario JG, Krohling RA, Ventura JA (2020) Deep learning for classification and severity estimation of coffee leaf biotic stress. Comput Electron Agric 169:105162","journal-title":"Comput Electron Agric"},{"key":"7744_CR110","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.biosystemseng.2019.02.002","volume":"180","author":"JGA Barbedo","year":"2019","unstructured":"Barbedo JGA (2019) Plant disease identification from individual lesions and spots using deep learning. Biosyst Eng 180:96\u2013107. https:\/\/doi.org\/10.1016\/j.biosystemseng.2019.02.002","journal-title":"Biosyst Eng"},{"key":"7744_CR111","doi-asserted-by":"publisher","first-page":"100353","DOI":"10.1016\/j.suscom.2019.100353","volume":"24","author":"G Hu","year":"2019","unstructured":"Hu G, Yang X, Zhang Y, Wan M (2019) Identification of tea leaf diseases by using an improved deep convolutional neural network. Sustain Comput Infor Sys 24:100353. https:\/\/doi.org\/10.1016\/j.suscom.2019.100353","journal-title":"Sustain Comput Infor Sys"},{"key":"7744_CR112","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.1016\/j.procs.2018.07.070","volume":"133","author":"AK Rangarajan","year":"2018","unstructured":"Rangarajan AK, Purushothaman R, Ramesh A (2018) Tomato crop disease classification using pre-trained deep learning algorithm. Proc Comput Sci 133:1040\u20131047. https:\/\/doi.org\/10.1016\/j.procs.2018.07.070","journal-title":"Proc Comput Sci"},{"key":"7744_CR113","doi-asserted-by":"publisher","unstructured":"Rangarajan AK, Raja P (2020) Automated disease classification in (Selected) agricultural crops using transfer learning. Automatika: \u010dasopiszaautomatiku, mjerenje, elektroniku, ra\u010dunarstvoikomunikacije,\u00a061(2): 260\u2013272. https:\/\/doi.org\/10.1080\/00051144.2020.1728911","DOI":"10.1080\/00051144.2020.1728911"},{"issue":"1","key":"7744_CR114","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3390\/sym10010011","volume":"10","author":"B Liu","year":"2018","unstructured":"Liu B, Zhang Y, He D, Li Y (2018) Identification of apple leaf diseases based on deep convolutional neural networks. Symmetry 10(1):11. https:\/\/doi.org\/10.3390\/sym10010011","journal-title":"Symmetry"},{"issue":"3","key":"7744_CR115","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.inpa.2019.10.003","volume":"7","author":"M Ji","year":"2020","unstructured":"Ji M, Zhang L, Wu Q (2020) Automatic grape leaf diseases identification via United Model based on multiple convolutional neural networks. Inf Process Agric 7(3):418\u2013426. https:\/\/doi.org\/10.1016\/j.inpa.2019.10.003","journal-title":"Inf Process Agric"},{"key":"7744_CR116","doi-asserted-by":"publisher","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","volume":"145","author":"KP Ferentinos","year":"2018","unstructured":"Ferentinos KP (2018) Deep learning models for plant disease detection and diagnosis. Comput Electron Agric 145:311\u2013318","journal-title":"Comput Electron Agric"},{"key":"7744_CR117","doi-asserted-by":"publisher","first-page":"272","DOI":"10.1016\/j.compag.2018.03.032","volume":"161","author":"EC Too","year":"2019","unstructured":"Too EC, Yujian L, Njuki S, Yingchun L (2019) A comparative study of fine-tuning deep learning models for plant disease identification. Comput Electron Agric 161:272\u2013279. https:\/\/doi.org\/10.1016\/j.compag.2018.03.032","journal-title":"Comput Electron Agric"},{"issue":"6","key":"7744_CR118","doi-asserted-by":"publisher","first-page":"8103","DOI":"10.3233\/JIFS-189132","volume":"39","author":"G Latif","year":"2020","unstructured":"Latif G, Alghazo J, Maheswar R, Vijayakumar V, Butt M (2020) Deep learning based intelligence cognitive vision drone for automatic plant diseases identification and spraying. J Intell Fuzzy Syst 39(6):8103\u20138114. https:\/\/doi.org\/10.3233\/JIFS-189132","journal-title":"J Intell Fuzzy Syst"},{"key":"7744_CR119","unstructured":"Amara J, Bouaziz B, Algergawy A (2017) A deep learning-based approach for banana leaf diseases classification. Datenbanksystemef\u00fcr Business, Technologie und Web (BTW 2017)-Workshopband"},{"key":"7744_CR120","doi-asserted-by":"crossref","unstructured":"Agrios GN (2005) Chapter fourteen-plant diseases caused by viruses. Agrios GNBT-PP, 5th (ed.) Academic Press, San Diego, CA, USA, pp. 723\u2013824","DOI":"10.1016\/B978-0-08-047378-9.50020-8"},{"issue":"3","key":"7744_CR121","doi-asserted-by":"publisher","first-page":"1636","DOI":"10.3906\/elk-1809-181","volume":"27","author":"M T\u00fcrko\u011flu","year":"2019","unstructured":"T\u00fcrko\u011flu M, Hanbay D (2019) Plant disease and pest detection using deep learning-based features. Turk J Electr Eng Comput Sci 27(3):1636\u20131651. https:\/\/doi.org\/10.3906\/elk-1809-181","journal-title":"Turk J Electr Eng Comput Sci"},{"key":"7744_CR122","doi-asserted-by":"publisher","first-page":"105002","DOI":"10.1016\/j.compag.2019.105002","volume":"166","author":"L Wu","year":"2019","unstructured":"Wu L, Liu Z, Bera T, Ding H, Langley DA, Jenkins-Barnes A, Furlanello C, Maggio V, Tong W, Xu J (2019) A deep learning model to recognize food contaminating beetle species based on elytra fragments. Comput Electron Agric 166:105002","journal-title":"Comput Electron Agric"},{"key":"7744_CR123","doi-asserted-by":"publisher","first-page":"105108","DOI":"10.1016\/j.compag.2019.105108","volume":"168","author":"H Kang","year":"2020","unstructured":"Kang H, Chen C (2020) Fast implementation of real-time fruit detection in apple orchards using deep learning. Comput Electron Agric 168:105108. https:\/\/doi.org\/10.1016\/j.compag.2019.105108","journal-title":"Comput Electron Agric"},{"issue":"1","key":"7744_CR124","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40648-019-0141-2","volume":"6","author":"Y Onishi","year":"2019","unstructured":"Onishi Y, Yoshida T, Kurita H, Fukao T, Arihara H, Iwai A (2019) An automated fruit harvesting robot by using deep learning. Robomech J 6(1):1\u20138. https:\/\/doi.org\/10.1186\/s40648-019-0141-2","journal-title":"Robomech J"},{"issue":"8","key":"7744_CR125","doi-asserted-by":"publisher","first-page":"1222","DOI":"10.3390\/s16081222","volume":"16","author":"I Sa","year":"2016","unstructured":"Sa I, Ge Z, Dayoub F, Upcroft B, Perez T, McCool C (2016) Deepfruits: a fruit detection system using deep neural networks. Sens 16(8):1222. https:\/\/doi.org\/10.3390\/s16081222","journal-title":"Sens"},{"key":"7744_CR126","doi-asserted-by":"publisher","first-page":"104846","DOI":"10.1016\/j.compag.2019.06.001","volume":"163","author":"Y Yu","year":"2019","unstructured":"Yu Y, Zhang K, Yang L, Zhang D (2019) Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN. Comput Electron Agric 163:104846. https:\/\/doi.org\/10.1016\/j.compag.2019.06.001","journal-title":"Comput Electron Agric"},{"key":"7744_CR127","doi-asserted-by":"publisher","first-page":"1759","DOI":"10.3389\/fpls.2020.571299","volume":"11","author":"M Afonso","year":"2020","unstructured":"Afonso M, Fonteijn H, Fiorentin FS, Lensink D, Mooij M, Faber N, Polder G, Wehrens R (2020) Tomato fruit detection and counting in greenhouses using deep learning. Front Plant Sci 11:1759. https:\/\/doi.org\/10.3389\/fpls.2020.571299","journal-title":"Front Plant Sci"},{"issue":"7","key":"7744_CR128","doi-asserted-by":"publisher","first-page":"12191","DOI":"10.3390\/s140712191","volume":"14","author":"K Yamamoto","year":"2014","unstructured":"Yamamoto K, Guo W, Yoshioka Y, Ninomiya S (2014) On plant detection of intact tomato fruits using image analysis and machine learning methods. Sens 14(7):12191\u201312206. https:\/\/doi.org\/10.3390\/s140712191","journal-title":"Sens"},{"issue":"4","key":"7744_CR129","doi-asserted-by":"publisher","first-page":"905","DOI":"10.3390\/s17040905","volume":"17","author":"M Rahnemoonfar","year":"2017","unstructured":"Rahnemoonfar M, Sheppard C (2017) Deep count: fruit counting based on deep simulated learning. Sens 17(4):905","journal-title":"Sens"},{"key":"7744_CR130","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.biosystemseng.2021.06.001","volume":"208","author":"PM Blok","year":"2021","unstructured":"Blok PM, van Henten EJ, van Evert FK, Kootstra G (2021) Image-based size estimation of broccoli heads under varying degrees of occlusion. Biosyst Eng 208:213\u2013233. https:\/\/doi.org\/10.1016\/j.biosystemseng.2021.06.001","journal-title":"Biosyst Eng"},{"key":"7744_CR131","doi-asserted-by":"publisher","first-page":"26","DOI":"10.2478\/ausi-2018-0002","volume":"10","author":"H Muresan","year":"2018","unstructured":"Muresan H, Oltean M (2018) Fruit recognition from images using deep learning. Acta Univ Sapientiae Informatica 10:26\u201342","journal-title":"Acta Univ Sapientiae Informatica"},{"key":"7744_CR132","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.compag.2017.12.032","volume":"145","author":"A Bakhshipour","year":"2018","unstructured":"Bakhshipour A, Jafari A (2018) Evaluation of support vector machine and artificial neural networks in weed detection using shape features. Comput Electron Agric 145:153\u2013160. https:\/\/doi.org\/10.1016\/j.compag.2017.12.032","journal-title":"Comput Electron Agric"},{"key":"7744_CR133","doi-asserted-by":"publisher","first-page":"226","DOI":"10.1016\/j.compag.2019.02.005","volume":"158","author":"A Wang","year":"2019","unstructured":"Wang A, Zhang W, Wei X (2019) A review on weed detection using ground-based machine vision and image processing techniques. Comput Electron Agric 158:226\u2013240. https:\/\/doi.org\/10.1016\/j.compag.2019.02.005","journal-title":"Comput Electron Agric"},{"key":"7744_CR134","doi-asserted-by":"publisher","first-page":"696","DOI":"10.1016\/j.procs.2019.11.025","volume":"160","author":"F Dankhara","year":"2019","unstructured":"Dankhara F, Patel K, Doshi N (2019) Analysis of robust weed detection techniques based on the Internet of Things (IoT). Proc Comput Sci 160:696\u2013701. https:\/\/doi.org\/10.1016\/j.procs.2019.11.025","journal-title":"Proc Comput Sci"},{"issue":"1","key":"7744_CR135","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1023\/A:1013326304427","volume":"3","author":"E Vrindts","year":"2002","unstructured":"Vrindts E, De Baerdemaeker J, Ramon H (2002) Weed detection using canopy reflection. Preci Agric 3(1):63\u201380. https:\/\/doi.org\/10.1023\/A:1013326304427","journal-title":"Preci Agric"},{"key":"7744_CR136","doi-asserted-by":"publisher","first-page":"106067","DOI":"10.1016\/j.compag.2021.106067","volume":"184","author":"AM Hasan","year":"2021","unstructured":"Hasan AM, Sohel F, Diepeveen D, Laga H, Jones MG (2021) A survey of deep learning techniques for weed detection from images. Comput Electron Agric 184:106067","journal-title":"Comput Electron Agric"},{"issue":"11","key":"7744_CR137","doi-asserted-by":"publisher","first-page":"1690","DOI":"10.3390\/rs10111690","volume":"10","author":"MD Bah","year":"2018","unstructured":"Bah MD, Hafiane A, Canals R (2018) Deep learning with unsupervised data labeling for weed detection in line crops in UAV images. Remote sens 10(11):1690. https:\/\/doi.org\/10.3390\/rs10111690","journal-title":"Remote sens"},{"key":"7744_CR138","doi-asserted-by":"publisher","first-page":"314","DOI":"10.1016\/j.compag.2017.10.027","volume":"143","author":"AS Ferreira","year":"2017","unstructured":"Ferreira AS, Freitas DM, da Silva GG, Pistori H, Folhes MT (2017) Weed detection in soybean crops using ConvNets. Comput Electron Agric 143:314\u2013324. https:\/\/doi.org\/10.1016\/j.compag.2017.10.027","journal-title":"Comput Electron Agric"},{"key":"7744_CR139","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.eja.2019.01.004","volume":"104","author":"J Yu","year":"2019","unstructured":"Yu J, Sharpe SM, Schumann AW, Boyd NS (2019) Deep learning for image-based weed detection in turfgrass. Eur J Agron 104:78\u201384. https:\/\/doi.org\/10.1016\/j.eja.2019.01.004","journal-title":"Eur J Agron"},{"issue":"1","key":"7744_CR140","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-018-38343-3","volume":"9","author":"A Olsen","year":"2019","unstructured":"Olsen A, Konovalov DA, Philippa B, Ridd P, Wood JC, Johns J, Banks W, Girgenti B, Kenny O, Whinney J, Azghadiand MR, Calvert B (2019) DeepWeeds: a multiclass weed species image dataset for deep learning. Sci Rep 9(1):1\u201312. https:\/\/doi.org\/10.1038\/s41598-018-38343-3","journal-title":"Sci Rep"},{"issue":"3","key":"7744_CR141","doi-asserted-by":"publisher","first-page":"471","DOI":"10.3390\/agriengineering2030032","volume":"2","author":"K Osorio","year":"2020","unstructured":"Osorio K, Puerto A, Pedraza C, Jamaica D, Rodr\u00edguez L (2020) A deep learning approach for weed detection in lettuce crops using multispectral images. Agri Eng 2(3):471\u2013488. https:\/\/doi.org\/10.3390\/agriengineering2030032","journal-title":"Agri Eng"},{"issue":"13","key":"7744_CR142","doi-asserted-by":"publisher","first-page":"2136","DOI":"10.3390\/rs12132136","volume":"12","author":"ANV Sivakumar","year":"2020","unstructured":"Sivakumar ANV, Li J, Scott S, Psota E, Jhala AJ, Luck JD, Shi Y (2020) Comparison of object detection and patch-based classification deep learning models on mid- to late-season weed detection in UAV imagery. Remote Sens 12(13):2136. https:\/\/doi.org\/10.3390\/rs12132136","journal-title":"Remote Sens"},{"key":"7744_CR143","doi-asserted-by":"publisher","first-page":"1422","DOI":"10.3389\/fpls.2019.01422","volume":"10","author":"J Yu","year":"2019","unstructured":"Yu J, Schumann AW, Cao Z, Sharpe SM, Boyd NS (2019) Weed detection in perennial ryegrass with deep learning convolutional neural network. Front Plant Sci 10:1422. https:\/\/doi.org\/10.3389\/fpls.2019.01422","journal-title":"Front Plant Sci"},{"key":"7744_CR144","doi-asserted-by":"publisher","unstructured":"Schulz S, Becker R, Cerda JC, Usman M, aus der Beek T, Merz R, Sch\u00fcth C (2021) Estimating water balance components in irrigated agriculture using a combined approach of soil moisture and energy balance monitoring, and numerical modelling. Hydrol Process, 35(3), e14077https:\/\/doi.org\/10.1002\/hyp.14077","DOI":"10.1002\/hyp.14077"},{"key":"7744_CR145","doi-asserted-by":"publisher","first-page":"125286","DOI":"10.1016\/j.jhydrol.2020.125286","volume":"591","author":"Z Chen","year":"2020","unstructured":"Chen Z, Zhu Z, Jiang H, Sun S (2020) Estimating daily reference evapotranspiration based on limited meteorological data using deep learning and classical machine learning methods. J Hydrol 591:125286. https:\/\/doi.org\/10.1016\/j.jhydrol.2020.125286","journal-title":"J Hydrol"},{"key":"7744_CR146","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.ejrh.2015.02.002","volume":"3","author":"K Djaman","year":"2015","unstructured":"Djaman K, Balde AB, Sow A, Muller B, Irmak S, N\u2019Diaye MK, Manneh B, Moukoumbi YD, Futakuchi K, Saito K (2015) Evaluation of sixteen reference evapotranspiration methods under sahelian conditions in the Senegal River Valley. J Hydrol Reg Stud 3:139\u2013159. https:\/\/doi.org\/10.1016\/j.ejrh.2015.02.002","journal-title":"J Hydrol Reg Stud"},{"key":"7744_CR147","doi-asserted-by":"publisher","first-page":"892","DOI":"10.1016\/j.jhydrol.2016.11.007","volume":"543","author":"F Karandish","year":"2016","unstructured":"Karandish F, \u0160im\u016fnek J (2016) A comparison of numerical and machine-learning modeling of soil water content with limited input data. J Hydrol 543:892\u2013909. https:\/\/doi.org\/10.1016\/j.jhydrol.2016.11.007","journal-title":"J Hydrol"},{"issue":"6","key":"7744_CR148","doi-asserted-by":"publisher","first-page":"1633","DOI":"10.1029\/WR015i006p01633","volume":"15","author":"SC Gupta","year":"1979","unstructured":"Gupta SC, Larson WE (1979) Estimating soil water retention characteristics from particle size distribution, organic matter percent, and bulk density. Water Resour Res 15(6):1633\u20131635. https:\/\/doi.org\/10.1029\/WR015i006p01633","journal-title":"Water Resour Res"},{"issue":"5","key":"7744_CR149","doi-asserted-by":"publisher","first-page":"1316","DOI":"10.13031\/2013.33720","volume":"25","author":"WJ Rawls","year":"1982","unstructured":"Rawls WJ, Brakensiek DL, Saxtonn KE (1982) Estimation of soil water properties. Trans ASAE 25(5):1316\u20131320. https:\/\/doi.org\/10.13031\/2013.33720","journal-title":"Trans ASAE"},{"key":"7744_CR150","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.2136\/sssaj2005.0117","volume":"70","author":"KE Saxton","year":"2006","unstructured":"Saxton KE, Rawls WJ (2006) Soil water characteristic estimates by texture and organic matter for hydrologic solutions. Soil Sci Soc Am J 70:1569\u20131578. https:\/\/doi.org\/10.2136\/sssaj2005.0117","journal-title":"Soil Sci Soc Am J"},{"key":"7744_CR151","doi-asserted-by":"publisher","first-page":"126698","DOI":"10.1016\/j.jhydrol.2021.126698","volume":"600","author":"Q Li","year":"2021","unstructured":"Li Q, Wang Z, Shangguan W, Li L, Yao Y, Yu F (2021) Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning. J Hydrol 600:126698. https:\/\/doi.org\/10.1016\/j.jhydrol.2021.126698","journal-title":"J Hydrol"},{"key":"7744_CR152","unstructured":"Quinlan JR (1992) Learning with continuous classes. In: Australian joint conference on artificial intelligence. pp. 343\u2013348"},{"key":"7744_CR153","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1016\/j.jhydrol.2012.04.007","volume":"444\u2013445","author":"H Tabari","year":"2012","unstructured":"Tabari H, Kisi O, Ezani A, Hosseinzadeh Talaee P (2012) SVM, ANFIS, regression and climate based models for reference evapotranspiration modeling using limited climatic data in a semi-arid highland environment. J Hydrol 444\u2013445:78\u201389. https:\/\/doi.org\/10.1016\/j.jhydrol.2012.04.007","journal-title":"J Hydrol"},{"key":"7744_CR154","doi-asserted-by":"publisher","first-page":"3195","DOI":"10.1007\/s11269-015-0990-2","volume":"29","author":"X Wen","year":"2015","unstructured":"Wen X, Si J, He Z, Wu J, Shao H, Yu H (2015) Support-Vector-Machine-based models for modeling daily reference evapotranspiration with limited climatic data in extreme arid regions. Water Resour Manag 29:3195\u20133209. https:\/\/doi.org\/10.1007\/s11269-015-0990-2","journal-title":"Water Resour Manag"},{"key":"7744_CR155","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1016\/j.jhydrol.2016.09.035","volume":"542","author":"ZM Yaseen","year":"2016","unstructured":"Yaseen ZM, Jaafar O, Deo RC, Kisi O, Adamowski J, Quilty J, El-Shafie A (2016) Stream-flow forecasting using extreme learning machines: A case study in a semi-arid region in Iraq. J Hydrol 542:603\u2013614. https:\/\/doi.org\/10.1016\/j.jhydrol.2016.09.035","journal-title":"J Hydrol"},{"key":"7744_CR156","doi-asserted-by":"publisher","first-page":"4","DOI":"10.1016\/j.agwat.2014.07.031","volume":"147","author":"LS Pereira","year":"2015","unstructured":"Pereira LS, Allen RG, Smith M, Raes D (2015) Crop evapotranspiration estimation with FAO56: past and future. Agric Water Manag 147:4\u201320. https:\/\/doi.org\/10.1016\/j.agwat.2014.07.031","journal-title":"Agric Water Manag"},{"key":"7744_CR157","doi-asserted-by":"publisher","first-page":"106113","DOI":"10.1016\/j.agwat.2020.106113","volume":"234","author":"LB Ferreira","year":"2020","unstructured":"Ferreira LB, Da Cunha FF (2020) New approach to estimate daily reference evapotranspiration based on hourly temperature and relative humidity using machine learning and deep learning. Agric Water Manag 234:106113. https:\/\/doi.org\/10.1016\/j.agwat.2020.106113","journal-title":"Agric Water Manag"},{"key":"7744_CR158","doi-asserted-by":"publisher","first-page":"147293","DOI":"10.1016\/j.scitotenv.2021.147293","volume":"786","author":"S Vulova","year":"2021","unstructured":"Vulova S, Meier F, Rocha AD, Quanz J, Nouri H, Kleinschmit B (2021) Modeling urban evapotranspiration using remote sensing, flux footprints, and artificial intelligence. Sci Total Environ 786:147293. https:\/\/doi.org\/10.1016\/j.scitotenv.2021.147293","journal-title":"Sci Total Environ"},{"key":"7744_CR159","doi-asserted-by":"publisher","first-page":"387","DOI":"10.1016\/j.compag.2018.11.031","volume":"156","author":"MK Saggi","year":"2019","unstructured":"Saggi MK, Jain S (2019) Reference evapotranspiration estimation and modeling of the Punjab Northern India using deep learning. Comput Electron Agric 156:387\u2013398. https:\/\/doi.org\/10.1016\/j.compag.2018.11.031","journal-title":"Comput Electron Agric"},{"key":"7744_CR160","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1007\/s40710-021-00512-4","volume":"8","author":"DK Roy","year":"2021","unstructured":"Roy DK (2021) Long short-term memory networks to predict one-step ahead reference evapotranspiration in a subtropical climatic zone. Environ Process 8:911\u2013941. https:\/\/doi.org\/10.1007\/s40710-021-00512-4","journal-title":"Environ Process"},{"issue":"11","key":"7744_CR161","doi-asserted-by":"publisher","first-page":"5029","DOI":"10.3390\/app11115029","volume":"11","author":"K Alibabaei","year":"2021","unstructured":"Alibabaei K, Gaspar PD, Lima TM (2021) Modeling soil water content and reference evapotranspiration from climate data using deep learning method. Appl Sci 11(11):5029. https:\/\/doi.org\/10.3390\/app11115029","journal-title":"Appl Sci"},{"key":"7744_CR162","doi-asserted-by":"publisher","first-page":"106386","DOI":"10.1016\/j.agwat.2020.106386","volume":"242","author":"J Yin","year":"2020","unstructured":"Yin J, Deng Z, Ines AVM, Wu J, Rasu E (2020) Forecast of short-term daily reference evapotranspiration under limited meteorological variables using a hybrid bi-directional long short-term memory model (Bi-LSTM). Agric Water Manag 242:106386. https:\/\/doi.org\/10.1016\/j.agwat.2020.106386","journal-title":"Agric Water Manag"},{"issue":"5","key":"7744_CR163","doi-asserted-by":"publisher","first-page":"1621","DOI":"10.3390\/app10051621","volume":"10","author":"H Afzaal","year":"2020","unstructured":"Afzaal H, Farooque AA, Abbas F, Acharya B, Esau T (2020) Computation of evapotranspiration with artificial intelligence for precision water resource management. Appl Sci 10(5):1621. https:\/\/doi.org\/10.3390\/app10051621","journal-title":"Appl Sci"},{"key":"7744_CR164","doi-asserted-by":"publisher","first-page":"699","DOI":"10.37394\/232015.2020.16.72","volume":"16","author":"GE Proias","year":"2020","unstructured":"Proias GE, Gravalos IO, Papageorgiou EL, Pocz\u0119ta KA, Sakellariou-Makrantonaki MA (2020) Forecasting reference evapotranspiration using time lagged recurrent neural network. Wseas Trans Environ Dev 16:699\u2013707. https:\/\/doi.org\/10.37394\/232015.2020.16.72","journal-title":"Wseas Trans Environ Dev"},{"key":"7744_CR165","doi-asserted-by":"publisher","first-page":"105206","DOI":"10.1016\/j.compag.2019.105206","volume":"169","author":"Z Chen","year":"2020","unstructured":"Chen Z, Sun S, Wang Y, Wang Q, Zhang X (2020) Temporal convolution-network-based models for modeling maize evapotranspiration under mulched drip irrigation. Comput Electron Agric 169:105206. https:\/\/doi.org\/10.1016\/j.compag.2019.105206","journal-title":"Comput Electron Agric"},{"key":"7744_CR166","doi-asserted-by":"publisher","first-page":"107040","DOI":"10.1016\/j.agwat.2021.107040","volume":"255","author":"F Granata","year":"2021","unstructured":"Granata F, Di Nunno F (2021) Forecasting evapotranspiration in different climates using ensembles of recurrent neural networks. Agric Water Manag 255:107040. https:\/\/doi.org\/10.1016\/j.agwat.2021.107040","journal-title":"Agric Water Manag"},{"key":"7744_CR167","doi-asserted-by":"publisher","first-page":"603","DOI":"10.5194\/hess-25-603-2021","volume":"25","author":"MT Sattari","year":"2021","unstructured":"Sattari MT, Apaydin H, Band SS, Mosavi A, Prasad R (2021) Comparative analysis of kernel-based versus ANN and deep learning methods in monthly reference evapotranspiration estimation. Hydrol Earth Syst Sci 25:603\u2013618. https:\/\/doi.org\/10.5194\/hess-25-603-2021","journal-title":"Hydrol Earth Syst Sci"},{"key":"7744_CR168","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1016\/0022-1694(95)02965-6","volume":"184","author":"D Entekhabi","year":"1996","unstructured":"Entekhabi D, Rodriguez-Iturbe I, Castelli F (1996) Mutual interaction of soil moisture state and atmospheric processes. J Hydrol 184:3\u201317. https:\/\/doi.org\/10.1016\/0022-1694(95)02965-6","journal-title":"J Hydrol"},{"key":"7744_CR169","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.agwat.2012.04.006","volume":"111","author":"YD Botula","year":"2012","unstructured":"Botula YD, Cornelis WM, Baert G, Van Ranst E (2012) Evaluation of pedotransfer functions for predicting water retention of soils in lower congo (D.R. Congo). Agric Water Manag 111:1\u201310. https:\/\/doi.org\/10.1016\/j.agwat.2012.04.006","journal-title":"Agric Water Manag"},{"key":"7744_CR170","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s40009-015-0358-4","volume":"38","author":"M Mohanty","year":"2015","unstructured":"Mohanty M, Sinha NK, Painuli DK, Bandyopadhyay KK, Hati KM, Sammi Reddy K, Chaudhary RS (2015) Modelling soil water contents at field capacity and permanent wilting point using artificial neural network for Indian soils. Natl Acad Sci Lett 38:373\u2013377. https:\/\/doi.org\/10.1007\/s40009-015-0358-4","journal-title":"Natl Acad Sci Lett"},{"issue":"D3","key":"7744_CR171","doi-asserted-by":"publisher","first-page":"D03102","DOI":"10.1029\/2006JD007478","volume":"112","author":"M Drusch","year":"2007","unstructured":"Drusch M (2007) Initializing numerical weather prediction models with satellite-derived surface soil moisture: data assimilation experiments with ECMWF\u2019s integrated forecast system and the TMI soil moisture data set. J Geophys Res Atmos 112(D3):D03102. https:\/\/doi.org\/10.1029\/2006JD007478","journal-title":"J Geophys Res Atmos"},{"issue":"5687","key":"7744_CR172","doi-asserted-by":"publisher","first-page":"1138","DOI":"10.1126\/science.1100217","volume":"305","author":"RD Koster","year":"2004","unstructured":"Koster RD, Dirmeyer PA, Guo Z, Bonan G, Chan E, Cox P, Gordon CT, Kanae S, Kowalczyk E, Lawrence D, Liu P, Lu CH, Malyshev S, McAvaney B, Mitchell K, Mocko D, Oki T, Oleson K, Pitman A, Sud YC, Taylor CM, Verseghy D, Vasic R, Xue Y, Yamada T (2004) Regions of strong coupling between soil moisture and precipitation. Science 305(5687):1138\u20131140. https:\/\/doi.org\/10.1126\/science.1100217","journal-title":"Science"},{"issue":"3\u20134","key":"7744_CR173","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.earscirev.2010.02.004","volume":"99","author":"SI Seneviratne","year":"2010","unstructured":"Seneviratne SI, Corti T, Davin EL, Hirschi M, Jaeger EB, Lehner I, Orlowsky B, Teuling AJ (2010) Investigating soil moisture\u2013climate interactions in a changing climate: a review. Earth-Science Rev 99(3\u20134):125\u2013161. https:\/\/doi.org\/10.1016\/j.earscirev.2010.02.004","journal-title":"Earth-Science Rev"},{"key":"7744_CR174","doi-asserted-by":"publisher","first-page":"L21709","DOI":"10.1029\/2009GL040334","volume":"36","author":"W Cai","year":"2009","unstructured":"Cai W, Cowan T, Briggs P, Raupach M (2009) Rising temperature depletes soil moisture and exacerbates severe drought conditions across southeast Australia. Geophys Res Lett 36:L21709. https:\/\/doi.org\/10.1029\/2009GL040334","journal-title":"Geophys Res Lett"},{"issue":"3\u20134","key":"7744_CR175","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1016\/j.jhydrol.2008.08.023","volume":"362","author":"D Norbiato","year":"2008","unstructured":"Norbiato D, Borga M, Degli Esposti S, Gaume E, Anquetin S (2008) Flash flood warning based on rainfall thresholds and soil moisture conditions: an assessment for gauged and ungauged basins. J Hydrol 362(3\u20134):274\u2013290. https:\/\/doi.org\/10.1016\/j.jhydrol.2008.08.023","journal-title":"J Hydrol"},{"key":"7744_CR176","doi-asserted-by":"publisher","first-page":"2343","DOI":"10.5194\/hess-18-2343-2014","volume":"18","author":"N Wanders","year":"2014","unstructured":"Wanders N, Karssenberg D, De Roo A, De Jong SM, Bierkens MFP (2014) The suitability of remotely sensed soil moisture for improving operational flood forecasting. Hydrol Earth Syst Sci 18:2343\u20132357. https:\/\/doi.org\/10.5194\/hess-18-2343-2014","journal-title":"Hydrol Earth Syst Sci"},{"key":"7744_CR177","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/BF02186962","volume":"2","author":"J Pastor","year":"1986","unstructured":"Pastor J, Post WM (1986) Influence of climate, soil moisture, and succession on forest carbon and nitrogen cycles. Biogeochem 2:3\u201327. https:\/\/doi.org\/10.1007\/BF02186962","journal-title":"Biogeochem"},{"issue":"1","key":"7744_CR178","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1109\/JSTARS.2009.2037163","volume":"3","author":"JD Bolten","year":"2010","unstructured":"Bolten JD, Crow WT, Jackson TJ, Zhan X, Reynolds CA (2010) Evaluating the utility of remotely sensed soil moisture retrievals for operational agricultural drought monitoring. IEEE J Sel Top Appl Earth Obs Remote Sens 3(1):57\u201366. https:\/\/doi.org\/10.1109\/JSTARS.2009.2037163","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"issue":"1","key":"7744_CR179","doi-asserted-by":"publisher","first-page":"275","DOI":"10.1175\/JHM-D-12-079.1","volume":"14","author":"J Wei","year":"2013","unstructured":"Wei J, Dirmeyer PA, Wisser D, Bosilovich MG, Mocko DM (2013) Where does the irrigation water go? an estimate of the contribution of irrigation to precipitation using MERRA. J Hydrometeorol 14(1):275\u2013289. https:\/\/doi.org\/10.1175\/JHM-D-12-079.1","journal-title":"J Hydrometeorol"},{"issue":"7743","key":"7744_CR180","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","volume":"566","author":"M Reichstein","year":"2019","unstructured":"Reichstein M, Camps-Valls G, Stevens B, Jung M, Denzler J, Carvalhais N (2019) Deep learning and process understanding for data-driven earth system science. Nature 566(7743):195\u2013204. https:\/\/doi.org\/10.1038\/s41586-019-0912-1","journal-title":"Nature"},{"key":"7744_CR181","doi-asserted-by":"publisher","first-page":"734","DOI":"10.1007\/s40333-016-0049-0","volume":"8","author":"X Song","year":"2016","unstructured":"Song X, Zhang G, Liu F, Li D, Zhao Y, Yang J (2016) Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model. J Arid Land 8:734\u2013748. https:\/\/doi.org\/10.1007\/s40333-016-0049-0","journal-title":"J Arid Land"},{"key":"7744_CR182","doi-asserted-by":"publisher","unstructured":"Tseng D, Wang D, Chen C, Miller L, Song W, Viers J, Vougioukas S, Carpin S, Ojea JA, Goldberg K (2018) Towards automating precision irrigation: deep learning to infer local soil moisture conditions from synthetic aerial agricultural images. In: IEEE international conference on automation science and engineering. IEEE computer society, pp. 284\u2013291 https:\/\/doi.org\/10.1109\/COASE.2018.8560431","DOI":"10.1109\/COASE.2018.8560431"},{"key":"7744_CR183","doi-asserted-by":"publisher","first-page":"106121","DOI":"10.1016\/j.agwat.2020.106121","volume":"234","author":"SS Yama\u00e7","year":"2020","unstructured":"Yama\u00e7 SS, \u015eeker C, Negi\u015f H (2020) Evaluation of machine learning methods to predict soil moisture constants with different combinations of soil input data for calcareous soils in a semi arid area. Agric Water Manag 234:106121. https:\/\/doi.org\/10.1016\/j.agwat.2020.106121","journal-title":"Agric Water Manag"},{"key":"7744_CR184","doi-asserted-by":"publisher","first-page":"199097","DOI":"10.1109\/ACCESS.2020.3034984","volume":"8","author":"J Yu","year":"2020","unstructured":"Yu J, Tang S, Zhangzhong L, Zheng W, Wang L, Wong A, Xu L (2020) A deep learning approach for multi-depth soil water content prediction in summer maize growth period. IEEE Access 8:199097\u2013199110. https:\/\/doi.org\/10.1109\/ACCESS.2020.3034984","journal-title":"IEEE Access"},{"key":"7744_CR185","doi-asserted-by":"publisher","first-page":"106649","DOI":"10.1016\/j.agwat.2020.106649","volume":"245","author":"J Yu","year":"2021","unstructured":"Yu J, Zhang X, Xu L, Dong J, Zhangzhong L (2021) A hybrid CNN-GRU model for predicting soil moisture in maize root zone. Agric Water Manag 245:106649. https:\/\/doi.org\/10.1016\/j.agwat.2020.106649","journal-title":"Agric Water Manag"},{"issue":"2","key":"7744_CR186","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1109\/TGRS.2002.808243","volume":"41","author":"EG Njoku","year":"2003","unstructured":"Njoku EG, Jackson TJ, Lakshmi V, Member S, Chan TK, Nghiem SV (2003) Soil moisture retrieval from AMSR-E. IEEE Trans Geosci Remote Sens 41(2):215\u2013229. https:\/\/doi.org\/10.1109\/TGRS.2002.808243","journal-title":"IEEE Trans Geosci Remote Sens"},{"issue":"2","key":"7744_CR187","doi-asserted-by":"publisher","first-page":"191","DOI":"10.1016\/S0034-4257(99)00036-X","volume":"70","author":"W Wagner","year":"1999","unstructured":"Wagner W, Lemoine G, Rott H (1999) A method for estimating soil moisture from ers scatterometer and soil data. RSEnv 70(2):191\u2013207. https:\/\/doi.org\/10.1016\/S0034-4257(99)00036-X","journal-title":"RSEnv"},{"issue":"5","key":"7744_CR188","doi-asserted-by":"publisher","first-page":"666","DOI":"10.1109\/JPROC.2010.2043032","volume":"98","author":"YH Kerr","year":"2010","unstructured":"Kerr YH, Waldteufel P, Wigneron JP, Delwart S, Cabot F, Boutin J, Escorihuela MJ, Font J, Reul N, Gruhier C, Juglea SE, Drinkwater MR, Hahne A, Martin-Neira M, Mecklenburg S (2010) The SMOS L: New tool for monitoring key elements ofthe global water cycle. Proc IEEE 98(5):666\u2013687. https:\/\/doi.org\/10.1109\/JPROC.2010.2043032","journal-title":"Proc IEEE"},{"issue":"5","key":"7744_CR189","doi-asserted-by":"publisher","first-page":"704","DOI":"10.1109\/JPROC.2010.2043918","volume":"98","author":"D Entekhabi","year":"2010","unstructured":"Entekhabi D, Njoku EG, O\u2019Neill PE, Kellogg KH, Crow WT, Edelstein WN, Entin JK, Goodman SD, Jackson TJ, Johnson J, Kimball J, Piepmeier JR, Koster RD, Martin N, McDonald KC, Moghaddam M, Moran S, Reichle R, Shi JC, Spencer MW, Thurman SW, Tsang L, Van Zyl J (2010) The soil moisture active passive (SMAP) mission. Proc IEEE 98(5):704\u2013716. https:\/\/doi.org\/10.1109\/JPROC.2010.2043918","journal-title":"Proc IEEE"},{"key":"7744_CR190","unstructured":"Liang S, Wang J (2019) Advanced remote sensing: terrestrial information extraction and applications. Adv Remote Sens Terr Inf Extr Appl 1\u2013986"},{"issue":"8","key":"7744_CR191","doi-asserted-by":"publisher","first-page":"1662","DOI":"10.1109\/36.942544","volume":"39","author":"YA Liou","year":"2001","unstructured":"Liou YA, Liu SF, Wang WJ (2001) Retrieving soil moisture from simulated brightness temperatures by a neural network. IEEE Trans Geosci Remote Sens 39(8):1662\u20131672. https:\/\/doi.org\/10.1109\/36.942544","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"7744_CR192","doi-asserted-by":"publisher","first-page":"11030","DOI":"10.1002\/2017GL075619","volume":"44","author":"K Fang","year":"2017","unstructured":"Fang K, Shen C, Kifer D, Yang X (2017) Prolongation of SMAP to spatiotemporally seamless coverage of continental U.S. using a deep learning neural network. Geophys Res Lett 44:11030\u201311039. https:\/\/doi.org\/10.1002\/2017GL075619","journal-title":"Geophys Res Lett"},{"issue":"5","key":"7744_CR193","doi-asserted-by":"publisher","first-page":"130","DOI":"10.3390\/ijgi6050130","volume":"6","author":"D Zhang","year":"2017","unstructured":"Zhang D, Zhang W, Huang W, Hong Z, Meng L (2017) Upscaling of surface soil moisture using a deep learning model with VIIRS RDR. ISPRS Int J Geo-Inf 6(5):130. https:\/\/doi.org\/10.3390\/ijgi6050130","journal-title":"ISPRS Int J Geo-Inf"},{"issue":"1","key":"7744_CR194","doi-asserted-by":"publisher","first-page":"43","DOI":"10.1080\/15481603.2018.1489943","volume":"56","author":"CS Lee","year":"2019","unstructured":"Lee CS, Sohn E, Park JD, Jang JD (2019) Estimation of soil moisture using deep learning based on satellite data: a case study of South Korea. GISci Remote Sens 56(1):43\u201367. https:\/\/doi.org\/10.1080\/15481603.2018.1489943","journal-title":"GISci Remote Sens"},{"issue":"34","key":"7744_CR195","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1007\/s13351-020-9191-x","volume":"344","author":"W Wang","year":"2020","unstructured":"Wang W, Zhang C, Li F, Song J, Li P, Zhang Y (2020) Extracting soil moisture from fengyun-3D medium resolution spectral imager-II imagery by using a deep belief network. J Meteorol Res 344(34):748\u2013759. https:\/\/doi.org\/10.1007\/s13351-020-9191-x","journal-title":"J Meteorol Res"},{"issue":"4","key":"7744_CR196","doi-asserted-by":"publisher","first-page":"554","DOI":"10.3390\/rs13040554","volume":"13","author":"AA Masrur Ahmed","year":"2021","unstructured":"Masrur Ahmed AA, Deo RC, Raj N, Ghahramani A, Feng Q, Yin Z, Yang L (2021) Deep learning forecasts of soil moisture: convolutional neural network and gated recurrent unit models coupled with satellite-derived MODIS, observations and synoptic-scale climate index data. Remote Sens 13(4):554. https:\/\/doi.org\/10.3390\/rs13040554","journal-title":"Remote Sens"},{"key":"7744_CR197","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.postharvbio.2017.11.019","volume":"139","author":"R Porat","year":"2018","unstructured":"Porat R, Lichter A, Terry LA, Harker R, Buzby J (2018) Postharvest losses of fruit and vegetables during retail and in consumers\u2019 homes: quantifications, causes, and means of prevention. Postharvest Biol Technol 139:135\u2013149","journal-title":"Postharvest Biol Technol"},{"key":"7744_CR198","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/s13197-020-04552-w","volume":"58","author":"SK Chakraborty","year":"2021","unstructured":"Chakraborty SK, Mahanti NK, Mansoori SM, Tripathi MK, Kotwaliwale N, Jayas DS (2021) Non-destructive classification and prediction of aflatoxin-B1 concentration in maize kernels using Vis-NIR (400\u20131000 nm) hyperspectral imaging. J Food Sci Technol 58:437\u2013450","journal-title":"J Food Sci Technol"},{"key":"7744_CR199","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.postharvbio.2018.10.003","volume":"148","author":"S Bureau","year":"2019","unstructured":"Bureau S, Cozzolino D, Clark CJ (2019) Contributions of Fourier-transform mid infrared (FT-MIR) spectroscopy to the study of fruit and vegetables: a review. Postharvest Biol Technol 148:1\u201314","journal-title":"Postharvest Biol Technol"},{"key":"7744_CR200","doi-asserted-by":"crossref","unstructured":"Li L, Peng Y, Li Y, Chao K, Dhakal S (2020, April) Online detection of tomato internal and external quality attributes by an optical sensing system. In sensing for agriculture and food quality and safety XII (Vol. 11421, p. 114210T). International society for optics and photonics","DOI":"10.1117\/12.2557058"},{"key":"7744_CR201","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1016\/j.saa.2018.10.028","volume":"209","author":"C Ni","year":"2019","unstructured":"Ni C, Wang D, Tao Y (2019) Variable weighted convolutional neural network for the nitrogen content quantization of Masson pine seedling leaves with near-infrared spectroscopy. Spectrochim Acta A Mol Biomol Spectrosc Spectrochim Acta A 209:32\u201339","journal-title":"Spectrochim Acta A Mol Biomol Spectrosc Spectrochim Acta A"},{"key":"7744_CR202","doi-asserted-by":"publisher","first-page":"105553","DOI":"10.1016\/j.compag.2020.105553","volume":"175","author":"D Rong","year":"2020","unstructured":"Rong D, Wang H, Ying Y, Zhang Z, Zhang Y (2020) Peach variety detection using VIS-NIR spectroscopy and deep learning. Comput Electron Agric 175:105553","journal-title":"Comput Electron Agric"},{"key":"7744_CR203","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.aca.2016.12.010","volume":"954","author":"J Acquarelli","year":"2017","unstructured":"Acquarelli J, Van Laarhoven T, Gerretzen J, Tran TN, Buydens LMC, Marchiori E (2017) Convolutional neural networks for vibrational spectroscopic data analysis. Anal Chim Acta 954:22\u201331","journal-title":"Anal Chim Acta"},{"key":"7744_CR204","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.aca.2019.08.026","volume":"1086","author":"Y Liu","year":"2019","unstructured":"Liu Y, Zhou S, Han W, Liu W, Qiu Z, Li C (2019) Convolutional neural network for hyperspectral data analysis and effective wavelengths selection. Anal Chim Acta 1086:46\u201354","journal-title":"Anal Chim Acta"},{"key":"7744_CR205","doi-asserted-by":"publisher","first-page":"110943","DOI":"10.1016\/j.postharvbio.2019.110943","volume":"156","author":"Y Bai","year":"2019","unstructured":"Bai Y, Xiong Y, Huang J, Zhou J, Zhang B (2019) Accurate prediction of soluble solid content of apples from multiple geographical regions by combining deep learning with spectral fingerprint features. Postharvest Biol Technol 156:110943","journal-title":"Postharvest Biol Technol"},{"issue":"2","key":"7744_CR206","doi-asserted-by":"publisher","first-page":"212","DOI":"10.3390\/app8020212","volume":"8","author":"Z Qiu","year":"2018","unstructured":"Qiu Z, Chen J, Zhao Y, Zhu S, He Y, Zhang C (2018) Variety identification of single rice seed using hyperspectral imaging combined with convolutional neural network. Appl Sci 8(2):212","journal-title":"Appl Sci"},{"key":"7744_CR207","doi-asserted-by":"publisher","first-page":"126630","DOI":"10.1016\/j.snb.2019.126630","volume":"296","author":"P Nie","year":"2019","unstructured":"Nie P, Zhang J, Feng X, Yu C, He Y (2019) Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning. Sens Actuators B Chem 296:126630","journal-title":"Sens Actuators B Chem"},{"issue":"22","key":"7744_CR208","doi-asserted-by":"publisher","first-page":"12635","DOI":"10.1039\/C8RA10335F","volume":"9","author":"N Wu","year":"2019","unstructured":"Wu N, Zhang Y, Na R, Mi C, Zhu S, He Y (2019) Variety identification of oat seeds using hyperspectral imaging: Investigating the representation ability of deep convolutional neural network. RSC Adv 9(22):12635\u201312644","journal-title":"RSC Adv"},{"key":"7744_CR209","doi-asserted-by":"publisher","first-page":"2831","DOI":"10.3390\/molecules23112831","volume":"23","author":"N Wu","year":"2018","unstructured":"Wu N, Zhang C, Bai X, Du X, He Y (2018) Discrimination of Chrysanthemum varieties using hyperspectral imaging combined with a deep convolutional neural network. Molecules 23:2831","journal-title":"Molecules"},{"key":"7744_CR210","unstructured":"Zhang X, Xu J, Lin T, Ying Y (2018) Convolutional neural network based classification analysis for near infrared spectroscopic sensing. In: 2018 ASABE international meeting (pp. 1\u20136). ASABE"},{"key":"7744_CR211","doi-asserted-by":"publisher","first-page":"1211","DOI":"10.1016\/j.procs.2020.09.117","volume":"176","author":"B Franczyk","year":"2020","unstructured":"Franczyk B, Hernes M, Kozierkiewicz A, Kozina A, Pietranik M, Roemer I, Schieck M (2020) Deep learning for grape variety recognition. Proc Comput Sci 176:1211\u20131220","journal-title":"Proc Comput Sci"},{"key":"7744_CR212","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1016\/j.postharvbio.2018.02.013","volume":"141","author":"X Yu","year":"2018","unstructured":"Yu X, Lu H, Wu D (2018) Development of deep learning method for predicting firmness and soluble solid content of postharvest Korla fragrant pear using Vis\/NIR hyperspectral reflectance imaging. Postharvest Biol Technol 141:39\u201349","journal-title":"Postharvest Biol Technol"},{"issue":"5","key":"7744_CR213","doi-asserted-by":"publisher","first-page":"e2977","DOI":"10.1002\/cem.2977","volume":"32","author":"S Malek","year":"2018","unstructured":"Malek S, Melgani F, Bazi Y (2018) One-dimensional convolutional neural networks for spectroscopic signal regression. J Chemom 32(5):e2977","journal-title":"J Chemom"},{"key":"7744_CR214","doi-asserted-by":"publisher","first-page":"126536","DOI":"10.1016\/j.foodchem.2020.126536","volume":"319","author":"C Zhang","year":"2020","unstructured":"Zhang C, Wu W, Zhou L, Cheng H, Ye X, He Y (2020) Developing deep learning based regression approaches for determination of chemical compositions in dry black goji berries (Lycium ruthenicum Murr.) using near-infrared hyperspectral imaging. Food Chem 319:126536","journal-title":"Food Chem"},{"key":"7744_CR215","doi-asserted-by":"publisher","first-page":"64494","DOI":"10.1109\/ACCESS.2019.2917267","volume":"7","author":"L Feng","year":"2019","unstructured":"Feng L, Zhu S, Zhou L, Zhao Y, Bao Y, Zhang C (2019) Detection of subtle bruises on winter jujube using hyperspectral imaging with pixel-wise deep learning method. IEEE Access 7:64494\u201364505","journal-title":"IEEE Access"},{"key":"7744_CR216","first-page":"31","volume":"4","author":"Z Gao","year":"2020","unstructured":"Gao Z, Yuanyuan Shao Y, Xuan G, Wang Y, Liu Y, Han X (2020) Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning. Artif Intell Agric 4:31\u201338","journal-title":"Artif Intell Agric"},{"key":"7744_CR217","first-page":"90","volume":"1","author":"S Arivazhagan","year":"2010","unstructured":"Arivazhagan S, Shebiah N, Nidhyanandhan S, Ganesan L (2010) Fruit recognition using color and texture features. J Emerg Trends Comput Inf Sci 1:90\u201394","journal-title":"J Emerg Trends Comput Inf Sci"},{"key":"7744_CR218","doi-asserted-by":"publisher","unstructured":"Zawbaa HM, Abbass M, Hazman M, Hassenian AE (2014) Automatic fruit image recognition system based on shape and color features. In: Advanced Machine Learning Technologies and Applications. (eds.: Hassanien, AE, Tolba, MF, and Taher Azar A) AMLTA. Series: Commun Comput 488: 278\u2013290. https:\/\/doi.org\/10.1007\/978-3-319-13461-1_27","DOI":"10.1007\/978-3-319-13461-1_27"},{"issue":"1754016","key":"7744_CR219","first-page":"17","volume":"31","author":"D Li","year":"2017","unstructured":"Li D, Zhao H, Zhao X, Gao Q, Xu L (2017) Cucumber detection based on texture and color in greenhouse. Intern J Pattern Recognit Artif Intell 31(1754016):17","journal-title":"Intern J Pattern Recognit Artif Intell"},{"key":"7744_CR220","first-page":"2352","volume":"3","author":"P Ninawe","year":"2014","unstructured":"Ninawe P, Pandey S (2014) A completion on fruit recognition system using k-nearest neighbours algorithm. Int J Adv Res 3:2352\u20132356","journal-title":"Int J Adv Res"},{"key":"7744_CR221","doi-asserted-by":"publisher","first-page":"425","DOI":"10.13031\/trans.12214","volume":"61","author":"Z Liu","year":"2018","unstructured":"Liu Z, He Y, Cen H, Lu R (2018) Deep feature representation with stacked sparse auto-encoder and convolutional neural network for hyperspectral imaging-based detection of cucumber defects. Trans ASABE 61:425\u2013436","journal-title":"Trans ASABE"},{"key":"7744_CR222","doi-asserted-by":"publisher","first-page":"2380","DOI":"10.1016\/j.procs.2020.04.258","volume":"171","author":"MM Raikar","year":"2020","unstructured":"Raikar MM, Meena SM, Kuchanur C, Girraddi S, Benagi P (2020) Classification and grading of okra-ladies finger using deep learning. Proc Comput Sci 171:2380\u20132389","journal-title":"Proc Comput Sci"},{"key":"7744_CR223","doi-asserted-by":"publisher","first-page":"209","DOI":"10.3389\/fpls.2019.00209","volume":"10","author":"G Polder","year":"2019","unstructured":"Polder G, Blok PM, de Villiers HA, van der Wolf JM, Kamp J (2019) Potato virus y detection in seed potatoes using deep learning on hyperspectral images. Front Plant Sci 10:209","journal-title":"Front Plant Sci"},{"key":"7744_CR224","first-page":"21","volume":"3","author":"MA Alajrami","year":"2020","unstructured":"Alajrami MA, Abu-Naser SS (2020) Type of tomato classification using deep learning. Int J Acad Pedagog Res 3:21\u201325","journal-title":"Int J Acad Pedagog Res"},{"key":"7744_CR225","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.biosystemseng.2019.12.003","volume":"190","author":"AZ Da Costa","year":"2020","unstructured":"Da Costa AZ, Figueroa HE, Fracarolli JA (2020) Computer vision based detection of external defects on tomatoes using deep learning. Biosyst Eng 190:131\u2013144","journal-title":"Biosyst Eng"},{"key":"7744_CR226","first-page":"4","volume":"13","author":"L Zhang","year":"2019","unstructured":"Zhang L, Jia J, Li Y, Gao W, Wang M (2019) Deep learning based rapid diagnosis system for identifying tomato nutrition disorders. KSII T Internet Info 13:4","journal-title":"KSII T Internet Info"},{"key":"7744_CR227","doi-asserted-by":"publisher","first-page":"7500","DOI":"10.1080\/01431161.2019.1569282","volume":"40","author":"NA Mubin","year":"2019","unstructured":"Mubin NA, Nadarajoo E, Shafri HZM, Hamedianfar A (2019) Young and mature oil palm tree detection and counting using convolutional neural network deep learning method. Int J Remote Sens 40:7500\u20137515. https:\/\/doi.org\/10.1080\/01431161.2019.1569282","journal-title":"Int J Remote Sens"},{"key":"7744_CR228","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.postharvbio.2019.04.003","volume":"153","author":"A Nasiri","year":"2019","unstructured":"Nasiri A, Taheri-Garavand A, Zhang YD (2019) Image-based deep learning automated sorting of date fruit. Postharvest Biol Technol 153:133\u2013141","journal-title":"Postharvest Biol Technol"},{"key":"7744_CR229","doi-asserted-by":"publisher","first-page":"117115","DOI":"10.1109\/ACCESS.2019.2936536","volume":"7","author":"H Altaheri","year":"2019","unstructured":"Altaheri H, Alsulaiman M, Muhammad G (2019) Date fruit classification for robotic harvesting in a natural environment using deep learning. IEEE Access 7:117115\u2013117133","journal-title":"IEEE Access"},{"key":"7744_CR230","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1038\/s41598-018-24926-7","volume":"8","author":"H Bisgin","year":"2018","unstructured":"Bisgin H, Bera T, Ding HJ, Semey HG, Wu LH, Xu J (2018) Comparing SVM and ANN based machine learning methods for species identification of food contaminating beetles. Sci Rep 8:12. https:\/\/doi.org\/10.1038\/s41598-018-24926-7","journal-title":"Sci Rep"},{"issue":"1","key":"7744_CR231","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11947-016-1817-8","volume":"10","author":"L Ravikanth","year":"2017","unstructured":"Ravikanth L, Jayas DS, White NDG, Fields PG, Sun DW (2017) Extraction of spectral information from hyperspectral data and application of hyperspectral imaging for food and agricultural products. Food Bioproc Tech 10(1):1\u201333","journal-title":"Food Bioproc Tech"},{"key":"7744_CR232","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.tifs.2016.01.011","volume":"50","author":"AI Ropodi","year":"2016","unstructured":"Ropodi AI, Panagou EZ, Nychas GJE (2016) Data mining derived from food analyses using non-invasive\/non-destructive analytical techniques; determination of food authenticity, quality & safety in tandem with computer science disciplines. Trends Food Sci Technol 50:11\u201325. https:\/\/doi.org\/10.1016\/j.tifs.2016.01.011","journal-title":"Trends Food Sci Technol"},{"key":"7744_CR233","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.neucom.2016.11.018","volume":"226","author":"Q Song","year":"2017","unstructured":"Song Q, Zheng YJ, Xue Y, Sheng WG, Zhao MR (2017) An evolutionary deep neural network for predicting morbidity of gastrointestinal infections by food contamination. Neurocomputing 226:16\u201322","journal-title":"Neurocomputing"},{"key":"7744_CR234","doi-asserted-by":"publisher","first-page":"105764","DOI":"10.1016\/j.compag.2020.105764","volume":"178","author":"D Rong","year":"2020","unstructured":"Rong D, Wang H, Xie L, Ying Y, Zhang Y (2020) Impurity detection of juglans using deep learning and machine vision. Comput Electron Agric 178:105764","journal-title":"Comput Electron Agric"},{"key":"7744_CR235","first-page":"1","volume":"1","author":"B Jiang","year":"2019","unstructured":"Jiang B, He J, Yang S, Fu H, Li T, Song H, He D (2019) Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues. Artif Intell Agric 1:1\u20138","journal-title":"Artif Intell Agric"},{"issue":"1","key":"7744_CR236","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1186\/s13040-019-0200-5","volume":"12","author":"HA Neto","year":"2019","unstructured":"Neto HA, Tavares WLF, Ribeiro DCSZ, Alves RCO, Fonseca LM, Campos SVA (2019) On the utilization of deep and ensemble learning to detect milk adulteration. BioData Min 12(1):13","journal-title":"BioData Min"},{"key":"7744_CR237","doi-asserted-by":"publisher","first-page":"108044","DOI":"10.1016\/j.foodcont.2021.108044","volume":"126","author":"M Zheng","year":"2021","unstructured":"Zheng M, Zhang Y, Gu J, Bai Z, Zhu R (2021) Classification and quantification of minced mutton adulteration with pork using thermal imaging and convolutional neural network. Food Control 126:108044. https:\/\/doi.org\/10.1016\/j.foodcont.2021.108044","journal-title":"Food Control"},{"key":"7744_CR238","doi-asserted-by":"publisher","first-page":"75","DOI":"10.1016\/j.jfoodeng.2017.08.005","volume":"217","author":"Z Wei","year":"2018","unstructured":"Wei Z, Yang Y, Wang J, Zhang W, Ren Q (2018) The measurement principles, working parameters and configurations of voltammetric electronic tongues and its applications for foodstuff analysis. J Food Eng 217:75\u201392","journal-title":"J Food Eng"},{"key":"7744_CR239","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.sna.2012.08.030","volume":"187","author":"AH Kiranmayee","year":"2012","unstructured":"Kiranmayee AH, Panchariya PC, Sharma AL (2012) New data reduction algorithm for voltammetric signals of electronic tongue for discrimination of liquids. Sens Actuators A 187:154\u2013161","journal-title":"Sens Actuators A"},{"key":"7744_CR240","doi-asserted-by":"publisher","first-page":"183","DOI":"10.1016\/j.meatsci.2019.06.002","volume":"156","author":"A Taheri-Garavand","year":"2019","unstructured":"Taheri-Garavand A, Fatahi S, Omid M, Makino Y (2019) Meat quality evaluation based on computer vision technique: a review. Meat Sci 156:183\u2013195","journal-title":"Meat Sci"},{"key":"7744_CR241","doi-asserted-by":"publisher","first-page":"106297","DOI":"10.1016\/j.compag.2021.106297","volume":"187","author":"Z Yang","year":"2021","unstructured":"Yang Z, Gao J, Wang S, Wang Z, Li C, Lan Y, Sun X, Shengxi Li S (2021) Synergetic application of E-tongue and E-eye based on deep learning to discrimination of Pu-erh tea storage time. Comput Electron Agric 187:106297","journal-title":"Comput Electron Agric"},{"key":"7744_CR242","doi-asserted-by":"publisher","first-page":"11359","DOI":"10.20964\/2018.12.45","volume":"13","author":"Q Shi","year":"2018","unstructured":"Shi Q, Guo T, Yin T, Wang Z, Li C, Sun X, Guo Y, Yuan W (2018) Classification of Pericarpium Citri Reticulatae of different ages by using a voltammetric electronic tongue system. Int J Electrochem Sci 13:11359\u201311374. https:\/\/doi.org\/10.20964\/2018.12.45","journal-title":"Int J Electrochem Sci"},{"key":"7744_CR243","unstructured":"Hughes D, Salath\u00e9 M (2015) An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060"},{"key":"7744_CR244","doi-asserted-by":"publisher","first-page":"45301","DOI":"10.1109\/access.2019.2909522","volume":"7","author":"L Liu","year":"2019","unstructured":"Liu L, Wang R, Xie C, Yang P, Wang F, Sudirman S, Liu W (2019) PestNet: an end-to-end deep learning approach for large-scale multi-class pest detection and classification. IEEE Access 7:45301\u201345312. https:\/\/doi.org\/10.1109\/access.2019.2909522","journal-title":"IEEE Access"},{"issue":"1","key":"7744_CR245","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1080\/19479830903561035","volume":"1","author":"J Zhang","year":"2010","unstructured":"Zhang J (2010) Multi-source remote sensing data fusion: status and trends. Int J Image Data Fusion 1(1):5\u201324","journal-title":"Int J Image Data Fusion"},{"key":"7744_CR246","doi-asserted-by":"crossref","unstructured":"Li H, Lin Z, Shen X, Brandt J, Hua G (2015) A convolutional neural network cascade for face detection. In 2015 IEEE conference on computer vision and pattern recognition (CVPR). IEEE 5325\u20135334","DOI":"10.1109\/CVPR.2015.7299170"},{"issue":"10","key":"7744_CR247","doi-asserted-by":"publisher","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","volume":"22","author":"SJ Pan","year":"2010","unstructured":"Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345\u20131359","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"7744_CR248","doi-asserted-by":"publisher","first-page":"467","DOI":"10.1016\/B978-0-12-816514-0.00019-9","volume-title":"Handbook of probabilistic models","author":"Z Wengang","year":"2020","unstructured":"Wengang Z, Teck Chee Goh A, Runhong Z, Yongqin L, Ning W (2020) Chapter 19-Back-propagation neural network modeling on the load\u2013settlement response of single piles. In: Samui P, Tien Bui D, Chakraborty S, Deo RC (eds) Handbook of probabilistic models. Butterworth-Heinemann, pp 467\u2013487"},{"key":"7744_CR249","doi-asserted-by":"publisher","first-page":"31","DOI":"10.1016\/B978-0-12-824145-5.00005-8","volume-title":"Deep learning for medical applications with unique data","author":"DK Sharma","year":"2022","unstructured":"Sharma DK, Chatterjee M, Kaur G, Vavilala S (2022) 3-Deep learning applications for disease diagnosis. In: Gupta D, Kose U, Khanna A, Balas VE (eds) Deep learning for medical applications with unique data. Academic Press, pp 31\u201351"},{"key":"7744_CR250","doi-asserted-by":"publisher","first-page":"1247","DOI":"10.3389\/fpsyg.2020.513474","volume":"12","author":"P Maheswari","year":"2021","unstructured":"Maheswari P, Raja P, Apolo-Apolo OE, P\u00e9rez-Ruiz M (2021) Intelligent fruit yield estimation for orchards using deep learning based semantic segmentation techniques-a review. Front Plant Sci 12:1247. https:\/\/doi.org\/10.3389\/fpsyg.2020.513474","journal-title":"Front Plant Sci"},{"key":"7744_CR251","unstructured":"Cho J, Lee K, Shin E, Choy G, Do S (2015) Medical image deep learning with hospital PACS dataset. ArXiv Prepr ArXiv151106348"},{"key":"7744_CR252","doi-asserted-by":"crossref","unstructured":"Femin A, Biju KS (2020) Accurate Detection of Buildings from Satellite Images using CNN. In: 2020 international conference on electrical, communication, and computer engineering (ICECCE). pp 1\u20135","DOI":"10.1109\/ICECCE49384.2020.9179232"},{"key":"7744_CR253","doi-asserted-by":"publisher","first-page":"2603","DOI":"10.3389\/fpls.2021.684328","volume":"11","author":"JM Bishop","year":"2021","unstructured":"Bishop JM (2021) Artificial intelligence is stupid and causal reasoning will not fix it. Front Psychol 11:2603. https:\/\/doi.org\/10.3389\/fpls.2021.684328","journal-title":"Front Psychol"},{"key":"7744_CR254","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1080\/10696679.2020.1860683","volume":"29","author":"JF Hair","year":"2021","unstructured":"Hair JF, Sarstedt M (2021) Data, measurement, and causal inferences in machine learning: opportunities and challenges for marketing. J Mark Theory Pract 29:65\u201377. https:\/\/doi.org\/10.1080\/10696679.2020.1860683","journal-title":"J Mark Theory Pract"},{"key":"7744_CR255","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.eng.2019.08.016","volume":"6","author":"K Kuang","year":"2020","unstructured":"Kuang K, Li L, Geng Z, Xu L, Zhang K (2020) Causal Inference. Engineering 6:253\u2013263. https:\/\/doi.org\/10.1016\/j.eng.2019.08.016","journal-title":"Engineering"},{"key":"7744_CR256","doi-asserted-by":"publisher","first-page":"126030","DOI":"10.1016\/j.eja.2020.126030","volume":"115","author":"OE Apolo-Apolo","year":"2020","unstructured":"Apolo-Apolo OE, Mart\u00ednez-Guanter J, Egea G, Raja P, P\u00e9rez-Ruiz M (2020) Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV. Eur J Agron 115:126030. https:\/\/doi.org\/10.1016\/j.eja.2020.126030","journal-title":"Eur J Agron"},{"key":"7744_CR257","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/978-3-030-37218-7_10","volume-title":"Computational vision and bio-inspired computing","author":"N Saranya","year":"2020","unstructured":"Saranya N, Srinivasan K, Pravin Kumar SK, Rukkumani V, Ramya R (2020) Fruit classification using traditional machine learning and deep learning approach. In: Smys S, Tavares JMRS, Balas VE, Iliyasu AM (eds) Computational vision and bio-inspired computing. Springer International Publishing, Cham, pp 79\u201389"},{"key":"7744_CR258","doi-asserted-by":"publisher","first-page":"10","DOI":"10.4236\/jcc.2020.86002","volume":"8","author":"F Mohameth","year":"2020","unstructured":"Mohameth F, Bingcai C, Sada KA (2020) Plant disease detection with deep learning and feature extraction using plant village. J Comput Commun 8:10\u201322","journal-title":"J Comput Commun"},{"key":"7744_CR259","doi-asserted-by":"publisher","first-page":"452","DOI":"10.1038\/nature14541","volume":"521","author":"Z Ghahramani","year":"2015","unstructured":"Ghahramani Z (2015) Probilistic machine learning and artificial intelligence. Nature 521:452\u2013459","journal-title":"Nature"},{"key":"7744_CR260","doi-asserted-by":"publisher","first-page":"2517","DOI":"10.1109\/JIOT.2020.3024234","volume":"8","author":"R Zhang","year":"2021","unstructured":"Zhang R, Jing X, Wu S, Jiang C, Mu J, Richard YuF (2021) Device-free wireless sensing for human detection: the deep learning perspective. IEEE Internet Things J 8:2517\u20132539. https:\/\/doi.org\/10.1109\/JIOT.2020.3024234","journal-title":"IEEE Internet Things J"},{"key":"7744_CR261","doi-asserted-by":"publisher","unstructured":"Curilem M, Canrio J, Franco L, Rios R (2018) Using CNN to classify spectrograms of seismic events from llaima volcano (Chile). In: proceedings of the international joint conference on neural networks pp. 1\u20138. https:\/\/doi.org\/10.1109\/IJCNN.2018.18598489285","DOI":"10.1109\/IJCNN.2018.18598489285"},{"key":"7744_CR262","doi-asserted-by":"publisher","first-page":"1079","DOI":"10.1785\/0220180319","volume":"90","author":"Y Zhou","year":"2019","unstructured":"Zhou Y, Yue H, Zhou S, Kong Q (2019) Hybrid event detection and phase-picking algorithm using convolutional and recurrent neural networks. Seismol Res Lett 90:1079\u20131087. https:\/\/doi.org\/10.1785\/0220180319","journal-title":"Seismol Res Lett"},{"key":"7744_CR263","doi-asserted-by":"publisher","first-page":"103858","DOI":"10.1016\/j.earscirev.2021.103858","volume":"223","author":"Z Ma","year":"2021","unstructured":"Ma Z, Mei G (2021) Deep learning for geological hazards analysis: Data, models, applications, and opportunities. Earth Sci Rev 223:103858. https:\/\/doi.org\/10.1016\/j.earscirev.2021.103858","journal-title":"Earth Sci Rev"},{"key":"7744_CR264","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1109\/TPAMI.2018.2798607","volume":"41","author":"T Baltrusaitis","year":"2019","unstructured":"Baltrusaitis T, Ahuja C, Morency LP (2019) Multimodal machine learning: a survey and taxonomy. IEEE Trans Pattern Anal Mach Intell 41:423\u2013443. https:\/\/doi.org\/10.1109\/TPAMI.2018.2798607","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"7744_CR265","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-019-45748-1","volume":"9","author":"S Mousavi","year":"2019","unstructured":"Mousavi S, Zhu W, Sheng Y, Beroza G (2019) Cred: a deep residual network of convolutional and recurrent units for earthquake signal detection. Sci Rep 9:1\u201314. https:\/\/doi.org\/10.1038\/s41598-019-45748-1","journal-title":"Sci Rep"},{"key":"7744_CR266","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","volume":"73","author":"G Montavon","year":"2017","unstructured":"Montavon G, Sanick W, Muller KR (2017) Methods for interpreting and understanding deep learning networks. Digit Signal Process 73:1\u20131","journal-title":"Digit Signal Process"},{"key":"7744_CR267","doi-asserted-by":"publisher","first-page":"360","DOI":"10.3390\/ijerph17010360","volume":"17","author":"YT Bai","year":"2020","unstructured":"Bai YT, Jin XB, Wang XY, Wang XK, Xu JP (2020) Dynamic correlation analysis method of air pollutants in spatio-temporal analysis. Int J Environ Res Public Health 17:360","journal-title":"Int J Environ Res Public Health"},{"key":"7744_CR268","doi-asserted-by":"publisher","first-page":"422","DOI":"10.1016\/j.compag.2019.03.012","volume":"162","author":"S Zhang","year":"2019","unstructured":"Zhang S, Zhang S, Zhang C, Wang X, Shi Y (2019) Cucumber leaf disease identification with global pooling dilated convolutional neural network. Comput Electron Agric 162:422\u2013430. https:\/\/doi.org\/10.1016\/j.compag.2019.03.012","journal-title":"Comput Electron Agric"},{"issue":"2","key":"7744_CR269","doi-asserted-by":"publisher","first-page":"469","DOI":"10.3390\/app10020469","volume":"10","author":"A Anagnostis","year":"2020","unstructured":"Anagnostis A, Asiminari G, Papageorgiou E, Bochtis D (2020) A convolutional neural networks based method for anthracnose infected walnut tree leaves identification. Appl Sci 10(2):469. https:\/\/doi.org\/10.3390\/app10020469","journal-title":"Appl Sci"},{"key":"7744_CR270","doi-asserted-by":"publisher","unstructured":"Butte S, Vakanski A, Duellman K, Wang H, Mirkouei A (2021) Potato crop stress identification in aerial images using deep learning-based object detection.\u00a0arXiv preprint arXiv:2106.07770. https:\/\/doi.org\/10.1002\/agj2.20841","DOI":"10.1002\/agj2.20841"},{"key":"7744_CR271","doi-asserted-by":"publisher","DOI":"10.1002\/saj2.20193","author":"F Zhang","year":"2021","unstructured":"Zhang F, Wu S, Liu J, Wang C, Guo Z, Xu A, Pan K, Pan X (2021) Predicting soil moisture content over partially vegetation covered surfaces from hyperspectral data with deep learning. Soil Sci Soc Am J. https:\/\/doi.org\/10.1002\/saj2.20193","journal-title":"Soil Sci Soc Am J"},{"key":"7744_CR272","doi-asserted-by":"publisher","first-page":"106334","DOI":"10.1016\/j.agwat.2020.106334","volume":"241","author":"A Elbeltagi","year":"2020","unstructured":"Elbeltagi A, Deng J, Wang K, Malik A, Maroufpoor S (2020) Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment. Agric Water Manag 241:106334","journal-title":"Agric Water Manag"},{"key":"7744_CR273","doi-asserted-by":"publisher","first-page":"636234","DOI":"10.3389\/frai.2021.636234","volume":"4","author":"M ElSaadani","year":"2021","unstructured":"ElSaadani M, Habib E, Abdelhameed AM, Bayoumi M (2021) Assessment of a spatiotemporal deep learning approach for soil moisture prediction and filling the gaps in between soil moisture observations. Front Artif Intell 4:636234. https:\/\/doi.org\/10.3389\/frai.2021.636234","journal-title":"Front Artif Intell"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07744-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07744-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07744-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,11,10]],"date-time":"2022-11-10T10:38:25Z","timestamp":1668076705000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07744-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,14]]},"references-count":273,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2022,12]]}},"alternative-id":["7744"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07744-x","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,14]]},"assertion":[{"value":"10 January 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 August 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 September 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}