{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T18:09:25Z","timestamp":1778782165326,"version":"3.51.4"},"reference-count":138,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001843","name":"Science and Engineering Research Board","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100001843","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100031001","name":"Tamil Nadu Agricultural University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100031001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001409","name":"Department of Science and Technology, Ministry of Science and Technology, India","doi-asserted-by":"publisher","award":["EEQ\/2023\/000582"],"award-info":[{"award-number":["EEQ\/2023\/000582"]}],"id":[{"id":"10.13039\/501100001409","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.engappai.2026.114455","type":"journal-article","created":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T16:49:03Z","timestamp":1774457343000},"page":"114455","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Revolutionising crop modelling and resource management by integrating deep learning - A review"],"prefix":"10.1016","volume":"175","author":[{"given":"C","family":"Guruanand","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8112-929X","authenticated-orcid":false,"given":"K","family":"Boomiraj","sequence":"additional","affiliation":[]},{"given":"V","family":"Geethalakshmi","sequence":"additional","affiliation":[]},{"given":"Ga","family":"Dheebakaran","sequence":"additional","affiliation":[]},{"given":"V","family":"Babu Rajendra Prasad","sequence":"additional","affiliation":[]},{"given":"S","family":"Naresh Kumar","sequence":"additional","affiliation":[]},{"given":"S","family":"Kokilavani","sequence":"additional","affiliation":[]},{"given":"J","family":"Gayathri","sequence":"additional","affiliation":[]},{"given":"V","family":"Nandhini","sequence":"additional","affiliation":[]},{"given":"K","family":"Senthilraja","sequence":"additional","affiliation":[]},{"given":"S","family":"Mohan Kumar","sequence":"additional","affiliation":[]},{"given":"S","family":"Selvakumar","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114455_bib1","series-title":"2017 Seventh International Conference on Innovative Computing Technology (INTECH)","article-title":"Convolution neural network in precision agriculture for plant image recognition and classification","author":"Abdullahi","year":"2017"},{"key":"10.1016\/j.engappai.2026.114455_bib2","doi-asserted-by":"crossref","first-page":"384","DOI":"10.1016\/j.compag.2016.03.015","article-title":"Calibration and validation of APSIM-Wheat and CERES-Wheat for spring wheat under rainfed conditions: Models evaluation and application","volume":"123","author":"Ahmed","year":"2016","journal-title":"Comp. and Elec. in Agri."},{"key":"10.1016\/j.engappai.2026.114455_bib3","series-title":"A Deep Learning-based Approach for Banana Leaf Diseases Classification","author":"Amara","year":"2017"},{"key":"10.1016\/j.engappai.2026.114455_bib4","doi-asserted-by":"crossref","first-page":"538","DOI":"10.9734\/ijecc\/2023\/v13i71906","article-title":"Assessing the impact of climate change on agricultural production using crop simulation model","volume":"13","author":"Annie","year":"2023","journal-title":"Int. J. Environ. Clim. Change"},{"issue":"11","key":"10.1016\/j.engappai.2026.114455_bib5","first-page":"117","article-title":"Evaluating and adapting climate change impacts on rice production in Indonesia: a case study of the Keduang subwatershed, Central Java","volume":"8","author":"Ansari","year":"2021","journal-title":"Environ"},{"key":"10.1016\/j.engappai.2026.114455_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.eja.2020.126030","article-title":"Deep learning techniques for estimation of the yield and size of citrus fruits using a UAV","volume":"115","author":"Apolo-Apolo","year":"2020","journal-title":"Eur. J. Agron."},{"issue":"9","key":"10.1016\/j.engappai.2026.114455_bib7","doi-asserted-by":"crossref","first-page":"1245","DOI":"10.3390\/plants9091245","article-title":"Increasing air temperatures and its effects on growth and productivity of tomato in South Florida","volume":"9","author":"Ayankojo","year":"2020","journal-title":"Plants"},{"issue":"2","key":"10.1016\/j.engappai.2026.114455_bib8","doi-asserted-by":"crossref","first-page":"242","DOI":"10.3390\/agriculture12020242","article-title":"Simulating cotton growth and productivity using AquaCrop model under deficit irrigation in a semi-arid climate","volume":"12","author":"Aziz","year":"2022","journal-title":"Agriculture"},{"issue":"1","key":"10.1016\/j.engappai.2026.114455_bib9","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1162\/qss_a_00019","article-title":"Scopus as a curated, high-quality bibliometric data source for academic research in quantitative","volume":"1","author":"Baas","year":"2020","journal-title":"Quant. Sci. Stud."},{"key":"10.1016\/j.engappai.2026.114455_bib10","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1016\/j.scitotenv.2019.07.307","article-title":"Assessing the long-term impact of conservation agriculture on wheat-based systems in Tunisia using APSIM simulations under a climate change context","volume":"692","author":"Bahri","year":"2019","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.engappai.2026.114455_bib11","author":"Balboa"},{"key":"10.1016\/j.engappai.2026.114455_bib12","author":"Bandyopadhyay"},{"issue":"2","key":"10.1016\/j.engappai.2026.114455_bib13","doi-asserted-by":"crossref","first-page":"143","DOI":"10.3390\/e23020143","article-title":"Benchmarking attention-based interpretability of deep learning in multivariate time series predictions","volume":"23","author":"Bari\u0107","year":"2021","journal-title":"Entropy"},{"issue":"10","key":"10.1016\/j.engappai.2026.114455_bib14","doi-asserted-by":"crossref","first-page":"1381","DOI":"10.1590\/S0100-204X2002001000005","article-title":"Simulation of growth and development of irrigated cowpea in Piau\u00ed State by CROPGRO model","volume":"37","author":"Bastos","year":"2002","journal-title":"Pesqui. Agropecu\u00e1ria Bras."},{"key":"10.1016\/j.engappai.2026.114455_bib15","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.aiia.2021.05.002","article-title":"Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network","volume":"5","author":"Bedi","year":"2021","journal-title":"Artificial Intelligence in Agriculture"},{"key":"10.1016\/j.engappai.2026.114455_bib16","author":"Bhatt"},{"issue":"spl","key":"10.1016\/j.engappai.2026.114455_bib17","first-page":"26","article-title":"InfoCrop\u2013a crop simulation model for assessing the climate change impacts on crops","volume":"15","author":"Boomiraj","year":"2013","journal-title":"J. Agrometeo"},{"key":"10.1016\/j.engappai.2026.114455_bib18","doi-asserted-by":"crossref","DOI":"10.1016\/j.rhisph.2022.100542","article-title":"Microplastics make their way into the soil and rhizosphere: a review of the ecological consequences","volume":"22","author":"Bouaicha","year":"2022","journal-title":"Rhizosphere"},{"issue":"2","key":"10.1016\/j.engappai.2026.114455_bib19","doi-asserted-by":"crossref","first-page":"387","DOI":"10.21603\/2308-4057-2021-2-387-396","article-title":"RNN-and CNN-based weed detection for crop improvement: an overview","volume":"9","author":"Brahim","year":"2021","journal-title":"Foods and Raw materials"},{"key":"10.1016\/j.engappai.2026.114455_bib20","doi-asserted-by":"crossref","first-page":"106336","DOI":"10.1016\/j.agwat.2020.106336","article-title":"Impact of climate change on water and nitrogen use efficiencies of processing tomato cultivated in Italy","volume":"241","author":"Cammarano","year":"2020","journal-title":"Agric. Water Manag."},{"key":"10.1016\/j.engappai.2026.114455_bib21","doi-asserted-by":"crossref","first-page":"105633","DOI":"10.1016\/j.envsoft.2023.105633","article-title":"The urgency for investment on local data for advancing food assessments in Africa: A review case study for APSIM crop modeling","volume":"161","author":"Carcedo","year":"2023","journal-title":"Environ. Model. Software"},{"issue":"27","key":"10.1016\/j.engappai.2026.114455_bib23","doi-asserted-by":"crossref","first-page":"42277","DOI":"10.1007\/s11042-023-15221-3","article-title":"Survey on crop pest detection using deep learning and machine learning approaches","volume":"82","author":"Chithambarathanu","year":"2023","journal-title":"Multimed. Tool. Appl."},{"issue":"3","key":"10.1016\/j.engappai.2026.114455_bib24","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1007\/s41348-022-00584-w","article-title":"Hybrid deep learning model for in-field pest detection on real-time field monitoring","volume":"129","author":"Chodey","year":"2022","journal-title":"J. Plant Dis. Prot."},{"issue":"2","key":"10.1016\/j.engappai.2026.114455_bib25","article-title":"Introduction to machine learning, neural networks, and deep learning","volume":"9","author":"Choi","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"10.1016\/j.engappai.2026.114455_bib26","author":"Choudhary"},{"key":"10.1016\/j.engappai.2026.114455_bib27","series-title":"2018 IEEE 14th International Conference on e-science (e-Science)","article-title":"A scalable machine learning system for pre-season agriculture yield forecast","author":"Cunha","year":"2018"},{"issue":"2","key":"10.1016\/j.engappai.2026.114455_bib28","doi-asserted-by":"crossref","first-page":"185","DOI":"10.54386\/jam.v25i2.2081","article-title":"Meta analysis on the evaluation and application of DSSAT in South Asia and China: Recent studies and the way forward","volume":"25","author":"Dar","year":"2023","journal-title":"J. Agrometeorol."},{"key":"10.1016\/j.engappai.2026.114455_bib29","doi-asserted-by":"crossref","DOI":"10.3389\/frai.2022.884192","article-title":"Recommendations for ethical and responsible use of artificial intelligence in digital agriculture","volume":"5","author":"Dara","year":"2022","journal-title":"Front. Artif. Intell."},{"issue":"4","key":"10.1016\/j.engappai.2026.114455_bib30","doi-asserted-by":"crossref","first-page":"646","DOI":"10.3390\/agronomy11040646","article-title":"Recognition of bloom\/yield in crop images using deep learning models for smart agriculture: a review","volume":"11","author":"Darwin","year":"2021","journal-title":"Agronomy"},{"key":"10.1016\/j.engappai.2026.114455_bib31","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2021.128254","article-title":"Blockchain for sustainable e-agriculture: literature review, architecture for data management, and implications","volume":"316","author":"Dey","year":"2021","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.engappai.2026.114455_bib32","series-title":"Journal of Physics: Conference Series","article-title":"Review on crop prediction using deep learning techniques","author":"Dharani","year":"2021"},{"key":"10.1016\/j.engappai.2026.114455_bib33","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/j.profoo.2016.02.039","article-title":"Application of DSSAT crop simulation model to identify the changes of rice growth and yield in Nilwala river basin for mid-centuries under changing climatic conditions","volume":"6","author":"Dias","year":"2016","journal-title":"Procedia Food Sci."},{"issue":"1","key":"10.1016\/j.engappai.2026.114455_bib34","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1007\/s12355-015-0367-0","article-title":"Performance of DSSAT CSM-CANEGRO under operational conditions and its use in determining the \u2018Saving irrigation\u2019impact on sugarcane crop","volume":"18","author":"dos Santos Vianna","year":"2016","journal-title":"Sugar Tech"},{"issue":"10","key":"10.1016\/j.engappai.2026.114455_bib35","doi-asserted-by":"crossref","first-page":"3999","DOI":"10.5194\/gmd-11-3999-2018","article-title":"Challenges and design choices for global weather and climate models based on machine learning","volume":"11","author":"Dueben","year":"2018","journal-title":"Geosci. Model Dev."},{"issue":"20","key":"10.1016\/j.engappai.2026.114455_bib36","doi-asserted-by":"crossref","first-page":"13205","DOI":"10.1007\/s00521-021-05950-7","article-title":"Fuzzy deep learning-based crop yield prediction model for sustainable agronomical frameworks","volume":"33","author":"Elavarasan","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.engappai.2026.114455_bib37","doi-asserted-by":"crossref","DOI":"10.1016\/j.agwat.2020.106334","article-title":"Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment","volume":"241","author":"Elbeltagi","year":"2020","journal-title":"Agric. Water Manag."},{"issue":"1","key":"10.1016\/j.engappai.2026.114455_bib38","article-title":"Simulation of maize (Zea mays L.) yield under alternative nitrogen fertilization using InfoCrop-maize model","volume":"17","author":"Fagodiya","year":"2017"},{"key":"10.1016\/j.engappai.2026.114455_bib39","doi-asserted-by":"crossref","DOI":"10.1016\/j.agwat.2020.106209","article-title":"Decision support systems and models for aiding irrigation and nutrient management of vegetable crops","volume":"240","author":"Gallardo","year":"2020","journal-title":"Agric. Water Manag."},{"key":"10.1016\/j.engappai.2026.114455_bib40","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/978-3-030-37468-6_14","article-title":"Deep learning and IoT for agricultural applications","author":"Garg","year":"2020","journal-title":"Internet of Things (IoT) Concepts and Applications"},{"issue":"1","key":"10.1016\/j.engappai.2026.114455_bib41","first-page":"4540454","article-title":"Potential impacts of future climate changes on crop productivity of cereals and legumes in Tamil Nadu, India: A mid\u2010century time slice approach","volume":"2023","author":"Geethalakshmi","year":"2023","journal-title":"Adv. Meteorol."},{"key":"10.1016\/j.engappai.2026.114455_bib42","series-title":"Transformer-Based Models for High-Resolution Soil Property Mapping: Leveraging Deep Learning and Multi-Modal Remote Sensing for Precision Agriculture","author":"Georgiou","year":"2025"},{"issue":"2","key":"10.1016\/j.engappai.2026.114455_bib43","doi-asserted-by":"crossref","first-page":"1963","DOI":"10.1007\/s40808-021-01194-5","article-title":"Application of CERES-sorghum crop simulation model DSSAT v4. 7 for determining crop water stress in crop phenological stages","volume":"8","author":"Gohain","year":"2022","journal-title":"Model. Earth Syst. Environ."},{"issue":"13","key":"10.1016\/j.engappai.2026.114455_bib44","doi-asserted-by":"crossref","first-page":"4537","DOI":"10.3390\/s21134537","article-title":"Deep learning based prediction on greenhouse crop yield combined TCN and RNN","volume":"21","author":"Gong","year":"2021","journal-title":"Sensors"},{"issue":"12","key":"10.1016\/j.engappai.2026.114455_bib45","first-page":"1801","article-title":"Evaluating InfoCrop model at mustard (Brassica juncea) crop field for multistage yield estimation.Indian","volume":"91","author":"Goyal","year":"2021","journal-title":"J. Agric. Sci."},{"issue":"2","key":"10.1016\/j.engappai.2026.114455_bib46","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1007\/s11069-021-05130-9","article-title":"Assessment of climate change impact and potential adaptation measures on wheat yield using the DSSAT model in the semi-arid environment","volume":"111","author":"Gunawat","year":"2022","journal-title":"Nat. Hazards"},{"key":"10.1016\/j.engappai.2026.114455_bib47","doi-asserted-by":"crossref","DOI":"10.1016\/j.agsy.2024.104144","article-title":"Drought risk assessment for maize\/peanut intercropping based on crop model and SPEI","volume":"221","author":"Guo","year":"2024","journal-title":"Agric. Syst."},{"issue":"10","key":"10.1016\/j.engappai.2026.114455_bib48","doi-asserted-by":"crossref","first-page":"806","DOI":"10.1017\/S0021859621000101","article-title":"Estimating the potential impact of climate change on sunflower yield in the Konya province of Turkey","volume":"158","author":"Gurkan","year":"2020","journal-title":"J. Agric. Sci."},{"issue":"3","key":"10.1016\/j.engappai.2026.114455_bib49","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1007\/s00704-020-03123-5","article-title":"Assessment of future climate variability and potential adaptation strategies on yield of peanut and Kharif rice in eastern India: D. Halder","volume":"140","author":"Halder","year":"2020","journal-title":"Theor. Appl. Climatol."},{"issue":"9","key":"10.1016\/j.engappai.2026.114455_bib50","doi-asserted-by":"crossref","first-page":"1021","DOI":"10.3390\/bioengineering10091021","article-title":"Intelligent grapevine disease detection using IoT sensor network","volume":"10","author":"Hnatiuc","year":"2023","journal-title":"Bioengineering"},{"key":"10.1016\/j.engappai.2026.114455_bib51","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.fcr.2018.01.019","article-title":"Exploring adaptations of groundnut cropping to prevailing climate variability and extremes in Limpopo Province, South Africa","volume":"219","author":"Hoffmann","year":"2018","journal-title":"Field Crops Res"},{"issue":"4","key":"10.1016\/j.engappai.2026.114455_bib52","doi-asserted-by":"crossref","first-page":"342","DOI":"10.3390\/insects12040342","article-title":"Automatic pest counting from pheromone trap images using deep learning object detectors for matsucoccus thunbergianae monitoring","volume":"12","author":"Hong","year":"2021","journal-title":"Insects"},{"issue":"5","key":"10.1016\/j.engappai.2026.114455_bib53","doi-asserted-by":"crossref","first-page":"e0285482","DOI":"10.1371\/journal.pone.0285482","article-title":"Evaluating the impact of Trichoderma biofertilizer and planting dates on mustard yield performance using the InfoCrop growth model","volume":"18","author":"Islam","year":"2023","journal-title":"Plos one"},{"key":"10.1016\/j.engappai.2026.114455_bib54","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","article-title":"Image-to-image translation with conditional adversarial networks","author":"Isola","year":"2017"},{"issue":"3","key":"10.1016\/j.engappai.2026.114455_bib55","first-page":"1071","article-title":"Evaluation of DSSAT CROPGRO model on growth and yield of pigeonpea cultivars under different fertigation levels","volume":"15","author":"Jeyajothi","year":"2023","journal-title":"J. Appl. Nat. Sci."},{"key":"10.1016\/j.engappai.2026.114455_bib56","doi-asserted-by":"crossref","first-page":"106591","DOI":"10.1016\/j.agwat.2020.106591","article-title":"Assessing water management effects on spring wheat yield in the Canadian Prairies using DSSAT wheat models","volume":"244","author":"Jing","year":"2021","journal-title":"Agric. Water Manag."},{"key":"10.1016\/j.engappai.2026.114455_bib57","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.copbio.2020.09.003","article-title":"The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems","volume":"70","author":"Jung","year":"2021","journal-title":"Curr. Opin. Biotechnol."},{"key":"10.1016\/j.engappai.2026.114455_bib58","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","article-title":"Deep learning in agriculture: a survey","volume":"147","author":"Kamilaris","year":"2018","journal-title":"Comput. Electron. Agric."},{"issue":"3","key":"10.1016\/j.engappai.2026.114455_bib59","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1017\/S0021859618000436","article-title":"A review of the use of convolutional neural networks in agriculture","volume":"156","author":"Kamilaris","year":"2018","journal-title":"J. Agric. Sci."},{"issue":"22","key":"10.1016\/j.engappai.2026.114455_bib60","doi-asserted-by":"crossref","first-page":"6526","DOI":"10.3390\/s20226526","article-title":"Deep-asymmetry: asymmetry matrix image for deep learning method in pre-screening depression","volume":"20","author":"Kang","year":"2020","journal-title":"Sensors"},{"issue":"5","key":"10.1016\/j.engappai.2026.114455_bib61","doi-asserted-by":"crossref","first-page":"4423","DOI":"10.1016\/j.aej.2021.03.009","article-title":"A new mobile application of agricultural pests recognition using deep learning in cloud computing system","volume":"60","author":"Karar","year":"2021","journal-title":"Alex. Eng. J."},{"key":"10.1016\/j.engappai.2026.114455_bib62","doi-asserted-by":"crossref","DOI":"10.1016\/j.jhydrol.2020.124905","article-title":"A review of remote sensing applications in agriculture for food security: crop growth and yield, irrigation, and crop losses","volume":"586","author":"Karthikeyan","year":"2020","journal-title":"J. Hydrol."},{"issue":"3","key":"10.1016\/j.engappai.2026.114455_bib63","first-page":"446","article-title":"Insect classification and detection in field crops using modern machine learning techniques","volume":"8","author":"Kasinathan","year":"2021","journal-title":"Inf. Process. Agric."},{"issue":"3","key":"10.1016\/j.engappai.2026.114455_bib64","first-page":"438","article-title":"Simulation of yield of rice cultivars under variable agronomic management options using ceres-rice and infocrop-rice models in irrigated plains of","volume":"59","author":"Kaur","year":"2022","journal-title":"Punjab. Agri. Res. J."},{"key":"10.1016\/j.engappai.2026.114455_bib65","series-title":"2020 Joint 9th International Conference on Informatics, Electronics & Vision (ICIEV) and 2020 4th International Conference on Imaging, Vision & Pattern Recognition (Icivpr). IEEE","article-title":"Deep learning with AnoGAN and efficient GAN to judge agricultural harvest image data","author":"Kawakura","year":"2020"},{"issue":"1","key":"10.1016\/j.engappai.2026.114455_bib66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40066-020-00283-5","article-title":"Challenges and opportunities in crop simulation modelling under seasonal and projected climate change scenarios for crop production in South Africa","volume":"10","author":"Kephe","year":"2021","journal-title":"Agric. Food Secur."},{"key":"10.1016\/j.engappai.2026.114455_bib67","doi-asserted-by":"crossref","first-page":"621","DOI":"10.3389\/fpls.2019.00621","article-title":"Crop yield prediction using deep neural networks","volume":"10","author":"Khaki","year":"2019","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.engappai.2026.114455_bib68","doi-asserted-by":"crossref","first-page":"1750","DOI":"10.3389\/fpls.2019.01750","article-title":"A CNN-RNN framework for crop yield prediction","volume":"10","author":"Khaki","year":"2020","journal-title":"Front. Plant Sci."},{"key":"10.1016\/j.engappai.2026.114455_bib69","doi-asserted-by":"crossref","DOI":"10.1016\/j.ecoinf.2022.101678","article-title":"A systematic review on hyperspectral imaging technology with a machine and deep learning methodology for agricultural applications","volume":"69","author":"Khan","year":"2022","journal-title":"Ecol. Inform."},{"key":"10.1016\/j.engappai.2026.114455_bib70","series-title":"Simulation and Validation Using Infocrop Model for Rabi Sorghum Crop Under Scarcity Zone of India","author":"Khobragade","year":"2023"},{"key":"10.1016\/j.engappai.2026.114455_bib71","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2024.1320969","article-title":"Combining machine learning and remote sensing-integrated crop modeling for rice and soybean crop simulation","volume":"15","author":"Ko","year":"2024","journal-title":"Front. Plant Sci."},{"issue":"1","key":"10.1016\/j.engappai.2026.114455_bib72","doi-asserted-by":"crossref","first-page":"81","DOI":"10.13031\/trans.13465","article-title":"Assessing the climate change impacts on grain sorghum yield and irrigation water use under full and deficit irrigation strategies","volume":"63","author":"Kothari","year":"2020","journal-title":"Trans. ASABE."},{"key":"10.1016\/j.engappai.2026.114455_bib73","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1016\/j.compag.2016.06.008","article-title":"Web-based crop model: Web InfoCrop\u2013Wheat to simulate the growth and yield of wheat","volume":"127","author":"Krishnan","year":"2016","journal-title":"Comp. Elect. Agri."},{"key":"10.1016\/j.engappai.2026.114455_bib74","series-title":"Artificial Neural Networks in Agriculture","author":"Kujawa","year":"2021"},{"issue":"8","key":"10.1016\/j.engappai.2026.114455_bib75","doi-asserted-by":"crossref","first-page":"2674","DOI":"10.3390\/s18082674","article-title":"Machine learning in agriculture: a review","volume":"18","author":"Liakos","year":"2018","journal-title":"Sensors"},{"key":"10.1016\/j.engappai.2026.114455_bib76","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.107208","article-title":"Generative adversarial networks (GANs) for image augmentation in agriculture: a systematic review","volume":"200","author":"Lu","year":"2022","journal-title":"Comput. Electron. Agric."},{"issue":"3","key":"10.1016\/j.engappai.2026.114455_bib77","doi-asserted-by":"crossref","first-page":"21","DOI":"10.33003\/fjs-2023-0703-1845","article-title":"Simulation of climate change effect on rice (Oryza sativa L.) production in Kano river irrigation scheme (Kris) using APSIM model","volume":"7","author":"Maina","year":"2023","journal-title":"FUDMA J. Sci."},{"issue":"1","key":"10.1016\/j.engappai.2026.114455_bib78","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-023-46218-5","article-title":"Image-based crop disease detection with federated learning","volume":"13","author":"Mamba Kabala","year":"2023","journal-title":"Sci. Rep."},{"issue":"4","key":"10.1016\/j.engappai.2026.114455_bib79","doi-asserted-by":"crossref","first-page":"43","DOI":"10.3390\/technologies12040043","article-title":"Applied deep learning-based crop yield prediction: a systematic analysis of current developments and potential challenges","volume":"12","author":"Meghraoui","year":"2024","journal-title":"Technologies"},{"key":"10.1016\/j.engappai.2026.114455_bib80","article-title":"Vision transformers in Precision Agriculture: a Comprehensive Survey","author":"Mehdipour","year":"2025","journal-title":"arXiv preprint arXiv:2504.21706"},{"key":"10.1016\/j.engappai.2026.114455_bib81","first-page":"1","article-title":"Explainable artificial intelligence: a comprehensive review","author":"Minh","year":"2022","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.engappai.2026.114455_bib82","doi-asserted-by":"crossref","first-page":"3922","DOI":"10.1016\/j.matpr.2021.01.973","article-title":"Automation and integration of growth monitoring in plants (with disease prediction) and crop prediction","volume":"43","author":"Mishra","year":"2021","journal-title":"Mater. Today Proc."},{"issue":"5","key":"10.1016\/j.engappai.2026.114455_bib83","doi-asserted-by":"crossref","first-page":"3975","DOI":"10.1002\/agj2.20788","article-title":"Evaluation of DSSAT\u2010CROPGRO\u2010cotton model to simulate phenology, growth, and seed cotton yield in northwestern India","volume":"113","author":"Mishra","year":"2021","journal-title":"Agron. J."},{"issue":"1","key":"10.1016\/j.engappai.2026.114455_bib84","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1007\/s40011-014-0443-3","article-title":"Efficient nitrogen and water management for the soybean\u2013wheat system of Madhya Pradesh, Central India, assessed using APSIM model","volume":"86","author":"Mohanty","year":"2016","journal-title":"Proceedings of the National Academy of Sciences, India Section B: Biological Sciences"},{"issue":"1","key":"10.1016\/j.engappai.2026.114455_bib85","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.32604\/cmc.2021.012431","article-title":"Paddy leaf disease detection using an optimized deep neural network","volume":"68","author":"Nalini","year":"2021","journal-title":"Comput. Mater. Continua (CMC)"},{"issue":"10","key":"10.1016\/j.engappai.2026.114455_bib86","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1007\/s42452-024-06228-y","article-title":"IoT and AI: a panacea for climate change-resilient smart agriculture","volume":"6","author":"Nawaz","year":"2024","journal-title":"Discov. Appl. Sci."},{"key":"10.1016\/j.engappai.2026.114455_bib87","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.104859","article-title":"Crop yield prediction with deep convolutional neural networks","volume":"163","author":"Nevavuori","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"10.1016\/j.engappai.2026.114455_bib88","series-title":"Proceedings of the 4th International Conference on Machine Learning and Soft Computing","article-title":"Diabetic retinopathy detection using deep learning","author":"Nguyen","year":"2020"},{"key":"10.1016\/j.engappai.2026.114455_bib89","article-title":"Automated abnormal potato plant detection system using deep learning models and portable video cameras","volume":"104","author":"Oishi","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"issue":"3","key":"10.1016\/j.engappai.2026.114455_bib90","first-page":"232","article-title":"Real time pest detection using YOLOv5","volume":"14","author":"\u00d6nler","year":"2021","journal-title":"International Journal of Agricultural and Natural Sciences"},{"issue":"13","key":"10.1016\/j.engappai.2026.114455_bib91","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1080\/08839514.2020.1792034","article-title":"Transfer learning-based framework for classification of pest in tomato plants","volume":"34","author":"Pattnaik","year":"2020","journal-title":"Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114455_bib92","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.107663","article-title":"Interpretability of deep learning models for crop yield forecasting","volume":"206","author":"Paudel","year":"2023","journal-title":"Comput. Electron. Agric."},{"issue":"2","key":"10.1016\/j.engappai.2026.114455_bib93","doi-asserted-by":"crossref","first-page":"37","DOI":"10.3390\/jimaging11020037","article-title":"Semantic-Guided transformer network for crop classification in hyperspectral images","volume":"11","author":"Pi","year":"2025","journal-title":"Journal of Imaging"},{"key":"10.1016\/j.engappai.2026.114455_bib94","article-title":"Automated climate prediction using pelican optimization based hybrid deep belief network for Smart Agriculture","volume":"27","author":"Punitha","year":"2023","journal-title":"Measurement: Sensors"},{"key":"10.1016\/j.engappai.2026.114455_bib95","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.eja.2018.01.015","article-title":"Modeling salinity effect on rice growth and grain yield with ORYZA v3 and APSIM-Oryza","volume":"100","author":"Radanielson","year":"2018","journal-title":"European J. Agron."},{"key":"10.1016\/j.engappai.2026.114455_bib96","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.biosystemseng.2020.03.020","article-title":"Identification and recognition of rice diseases and pests using convolutional neural networks","volume":"194","author":"Rahman","year":"2020","journal-title":"Biosyst. Eng."},{"issue":"11","key":"10.1016\/j.engappai.2026.114455_bib97","doi-asserted-by":"crossref","first-page":"3885","DOI":"10.1175\/MWR-D-18-0187.1","article-title":"Neural networks for postprocessing ensemble weather forecasts","volume":"146","author":"Rasp","year":"2018","journal-title":"Mon. Weather Rev."},{"key":"10.1016\/j.engappai.2026.114455_bib98","series-title":"Generative Adversarial Text to Image Synthesis, International Conference on Machine Learning","author":"Reed","year":"2016"},{"issue":"3","key":"10.1016\/j.engappai.2026.114455_bib99","doi-asserted-by":"crossref","first-page":"592","DOI":"10.3390\/rs14030592","article-title":"Transformer neural network for weed and crop classification of high resolution UAV images","volume":"14","author":"Reedha","year":"2022","journal-title":"Remote Sens."},{"issue":"1","key":"10.1016\/j.engappai.2026.114455_bib100","article-title":"[Retracted] application of iot-based drones in precision agriculture for Pest control","volume":"2022","author":"Refaai","year":"2022","journal-title":"Adv. Mater. Sci. Eng."},{"key":"10.1016\/j.engappai.2026.114455_bib101","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.future.2018.05.046","article-title":"On blockchain and its integration with IoT. Challenges and opportunities","volume":"88","author":"Reyna","year":"2018","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.engappai.2026.114455_bib102","first-page":"108986","article-title":"Fine-tuning the CROPGRO-Sunflower model and its application to the quantification of crop responses to environmental and management","volume":"300","author":"Rodriguez","year":"2023","journal-title":"variables"},{"issue":"12","key":"10.1016\/j.engappai.2026.114455_bib103","doi-asserted-by":"crossref","DOI":"10.1016\/j.patter.2021.100390","article-title":"Exploring complex and heterogeneous correlations on hypergraph for the prediction of drug-target interactions","volume":"2","author":"Ruan","year":"2021","journal-title":"Patterns"},{"key":"10.1016\/j.engappai.2026.114455_bib104","doi-asserted-by":"crossref","DOI":"10.1109\/ACCESS.2025.3610649","article-title":"A Data-Driven review of remote Sensing-Based data fusion in precision agriculture from foundational to Transformer-Based techniques","author":"Saki","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.114455_bib105","article-title":"Deep learning applications in agriculture: a short review, Robot 2019: fourth Iberian robotics Conference","volume":"vol. 1","author":"Santos","year":"2020"},{"key":"10.1016\/j.engappai.2026.114455_bib106","series-title":"1991 Second International Conference on Artificial Neural Networks","article-title":"Artificial neural networks in forecasting minimum temperature (weather)","author":"Schizas","year":"1991"},{"key":"10.1016\/j.engappai.2026.114455_bib107","article-title":"Modeling cloud reflectance fields using conditional generative adversarial networks","author":"Schmidt","year":"2020","journal-title":"arXiv preprint arXiv:2002.07579"},{"key":"10.1016\/j.engappai.2026.114455_bib108","series-title":"Modelling the Effects of Mulch Application Rates on Cowpea Growth, Yield and Soil Water Content Using APSIM","author":"Selamolela","year":"2020"},{"issue":"1","key":"10.1016\/j.engappai.2026.114455_bib109","doi-asserted-by":"crossref","first-page":"1606","DOI":"10.1038\/s41598-020-80820-1","article-title":"Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt","volume":"11","author":"Shahhosseini","year":"2021","journal-title":"Sci. Rep."},{"issue":"11","key":"10.1016\/j.engappai.2026.114455_bib110","doi-asserted-by":"crossref","first-page":"5695","DOI":"10.1007\/s00521-023-09391-2","article-title":"Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making","volume":"36","author":"Shams","year":"2024","journal-title":"Neural Comput. Appl."},{"issue":"21","key":"10.1016\/j.engappai.2026.114455_bib111","doi-asserted-by":"crossref","first-page":"4043","DOI":"10.3390\/rs16214043","article-title":"Multimodal deep learning integration of image, weather, and phenotypic data under temporal effects for early prediction of maize yield","volume":"16","author":"Shamsuddin","year":"2024","journal-title":"Remote Sens."},{"key":"10.1016\/j.engappai.2026.114455_bib112","series-title":"Convolutional LSTM Network: a Machine Learning Approach for Precipitation Nowcasting","year":"2015"},{"issue":"10","key":"10.1016\/j.engappai.2026.114455_bib113","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1007\/s12517-022-10266-4","article-title":"Tropospheric ozone effect on yield, quality and antioxidant defence of six cultivars of jute with ethylene diurea in the lower Gangetic Plains of India","volume":"15","author":"Singh","year":"2022","journal-title":"Arabian J. Geosci."},{"key":"10.1016\/j.engappai.2026.114455_bib114","series-title":"2021 2nd International Conference on Intelligent Engineering and Management (ICIEM)","article-title":"A hybrid model for the classification of sunflower diseases using deep learning","author":"Sirohi","year":"2021"},{"key":"10.1016\/j.engappai.2026.114455_bib115","series-title":"Agricultural System Models in Field Research and Technology Transfercrc","first-page":"177","article-title":"Applications of crop growth models in the semiarid regions","author":"Sivakumar","year":"2016"},{"issue":"14","key":"10.1016\/j.engappai.2026.114455_bib116","doi-asserted-by":"crossref","first-page":"2242","DOI":"10.1080\/01904167.2024.2338759","article-title":"Exploring the best nutrient management options for improving the maize yield for future climate change scenario with DSSAT crop simulation model in century old Permanent Manurial Experiment","volume":"47","author":"Sridevi","year":"2024","journal-title":"J. Plant Nutr."},{"issue":"16","key":"10.1016\/j.engappai.2026.114455_bib117","doi-asserted-by":"crossref","first-page":"6299","DOI":"10.3390\/s22166299","article-title":"A cloud enabled crop recommendation platform for machine learning-driven precision farming","volume":"22","author":"Thilakarathne","year":"2022","journal-title":"Sensors"},{"issue":"1","key":"10.1016\/j.engappai.2026.114455_bib118","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1007\/s13593-015-0336-8","article-title":"Nutrient management in African sorghum cropping systems: applying meta-analysis to assess yield and profitability","volume":"36","author":"Tonitto","year":"2016","journal-title":"Agron. Sust. Develop."},{"issue":"7","key":"10.1016\/j.engappai.2026.114455_bib119","doi-asserted-by":"crossref","first-page":"3335","DOI":"10.1007\/s12652-019-01591-w","article-title":"Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests","volume":"13","author":"Turkoglu","year":"2022","journal-title":"J. Ambient Intell. Hum. Comput."},{"key":"10.1016\/j.engappai.2026.114455_bib120","doi-asserted-by":"crossref","first-page":"108034","DOI":"10.1016\/j.agwat.2022.108034","article-title":"Variety-specific sugarcane yield simulations and climate change impacts on sugarcane yield using DSSAT-CSM-CANEGRO model","volume":"275","author":"Verma","year":"2023","journal-title":"Agric. Water Manag."},{"key":"10.1016\/j.engappai.2026.114455_bib121","article-title":"Learning incompressible fluid dynamics from Scratch--Towards fast, differentiable fluid models that generalize","author":"Wandel","year":"2020","journal-title":"arXiv preprint arXiv:2006.08762"},{"issue":"7","key":"10.1016\/j.engappai.2026.114455_bib123","doi-asserted-by":"crossref","first-page":"5205","DOI":"10.1007\/s10462-021-10018-y","article-title":"A review of deep learning used in the hyperspectral image analysis for agriculture","volume":"54","author":"Wang","year":"2021","journal-title":"Artif. Intell. Rev."},{"issue":"8","key":"10.1016\/j.engappai.2026.114455_bib124","doi-asserted-by":"crossref","first-page":"3060","DOI":"10.1007\/s11368-023-03541-8","article-title":"Sustainable soil remediation using mineral and hydrogel: field evidence for metalloid immobilization and soil health improvement","volume":"23","author":"Wang","year":"2023","journal-title":"J. Soils Sediments"},{"key":"10.1016\/j.engappai.2026.114455_bib125","article-title":"Predrnn: Recurrent neural networks for predictive learning using spatiotemporal lstms","volume":"30","author":"Wang","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"1","key":"10.1016\/j.engappai.2026.114455_bib126","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.eja.2009.05.004","article-title":"How farming systems simulation can aid the development of more sustainable smallholder farming systems in southern Africa","volume":"32","author":"Whitbread","year":"2010","journal-title":"Eur. J. Agron."},{"key":"10.1016\/j.engappai.2026.114455_bib127","doi-asserted-by":"crossref","DOI":"10.3389\/fpls.2024.1206998","article-title":"Ultra-high-resolution UAV-imaging and supervised deep learning for accurate detection of Alternaria solani in potato fields","volume":"15","author":"Wieme","year":"2024","journal-title":"Front. Plant Sci."},{"issue":"21","key":"10.1016\/j.engappai.2026.114455_bib128","first-page":"34","article-title":"A review: performance evaluation of crop simulation model (APSIM) in prediction crop growth, development and yield in semi-arid tropics","volume":"5","author":"Wolday","year":"2015","journal-title":"J. Nat. Sci. Res."},{"issue":"9","key":"10.1016\/j.engappai.2026.114455_bib22","doi-asserted-by":"crossref","first-page":"351","DOI":"10.9734\/ijecc\/2024\/v14i94419","article-title":"Investigating the Impact of Climate Change Impacts on Direct-seeded Rice Production in Middle Gujarat Using the InfoCrop-rice Model","volume":"14","author":"Chaudhari","year":"2024","journal-title":"Int. J. Environ. Clim. Change"},{"key":"10.1016\/j.engappai.2026.114455_bib129","series-title":"Exploring the potential of cowpea\u2013wheat double cropping in the semi-arid region of the southern United States using the DSSAT crop model","first-page":"35","author":"Woli","year":"2023"},{"key":"10.1016\/j.engappai.2026.114455_bib130","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109412","article-title":"Recent advances in Transformer technology for agriculture: a comprehensive survey","volume":"138","author":"Xie","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114455_bib131","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.102928","article-title":"Improving the local diagnostic explanations of diabetes mellitus with the ensemble of label noise filters","volume":"117","author":"Xu","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.engappai.2026.114455_bib132","series-title":"ICC 2019-2019 IEEE International Conference on Communications (ICC)","article-title":"Satellite image prediction relying on GAN and LSTM neural networks","author":"Xu","year":"2019"},{"key":"10.1016\/j.engappai.2026.114455_bib133","doi-asserted-by":"crossref","first-page":"132","DOI":"10.1016\/j.future.2020.04.037","article-title":"Detection and diagnosis of pancreatic tumor using deep learning-based hierarchical convolutional neural network on the internet of medical things platform","volume":"111","author":"Xuan","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"10.1016\/j.engappai.2026.114455_bib134","doi-asserted-by":"crossref","first-page":"109105","DOI":"10.1016\/j.compag.2024.109105","article-title":"Drought-tolerant peanut (Arachis hypogaea L.) varieties can mitigate negative impacts of climate change on yield in the Southeastern US","volume":"224","author":"Zhen","year":"2024","journal-title":"Comput. Elect. Agri."},{"issue":"5","key":"10.1016\/j.engappai.2026.114455_bib135","doi-asserted-by":"crossref","first-page":"1058","DOI":"10.3390\/s19051058","article-title":"CropDeep: the crop vision dataset for deep-learning-based classification and detection in precision agriculture","volume":"19","author":"Zheng","year":"2019","journal-title":"Sensors"},{"key":"10.1016\/j.engappai.2026.114455_bib136","doi-asserted-by":"crossref","DOI":"10.1016\/j.scitotenv.2021.147444","article-title":"Microplastics as an emerging threat to plant and soil health in agroecosystems","volume":"787","author":"Zhou","year":"2021","journal-title":"Sci. Total Environ."},{"issue":"12","key":"10.1016\/j.engappai.2026.114455_bib137","doi-asserted-by":"crossref","first-page":"1935","DOI":"10.1109\/LGRS.2016.2618840","article-title":"Polarimetric SAR image classification using deep convolutional neural networks","volume":"13","author":"Zhou","year":"2016","journal-title":"IEEE Geosci. Rem. Sens. Lett."},{"key":"10.1016\/j.engappai.2026.114455_bib138","doi-asserted-by":"crossref","DOI":"10.1016\/j.eneco.2025.108895","article-title":"AI-driven hypergraph neural network for predicting gasoline price trends","author":"Zhu","year":"2025","journal-title":"Energy Econ."},{"issue":"1","key":"10.1016\/j.engappai.2026.114455_bib139","doi-asserted-by":"crossref","first-page":"62","DOI":"10.3390\/s23010062","article-title":"Deep learning in diverse intelligent sensor based systems","volume":"23","author":"Zhu","year":"2022","journal-title":"Sensors"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626007360?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626007360?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T17:16:46Z","timestamp":1778779006000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626007360"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":138,"alternative-id":["S0952197626007360"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114455","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Revolutionising crop modelling and resource management by integrating deep learning - A review","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114455","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114455"}}