{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T15:12:30Z","timestamp":1783696350142,"version":"3.55.0"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"16","license":[{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T00:00:00Z","timestamp":1677110400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2023,6]]},"DOI":"10.1007\/s00521-023-08339-w","type":"journal-article","created":{"date-parts":[[2023,2,23]],"date-time":"2023-02-23T18:02:21Z","timestamp":1677175341000},"page":"12055-12071","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Coconut trees classification based on height, inclination, and orientation using MIN-SVM algorithm"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1097-5576","authenticated-orcid":false,"given":"Rajesh Kannan","family":"Megalingam","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sakthiprasad","family":"Kuttankulangara Manoharan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dasari Hema Teja Anirudh","family":"Babu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ghali","family":"Sriram","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Karanam","family":"Lokesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sankardas","family":"Kariparambil Sudheesh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,2,23]]},"reference":[{"key":"8339_CR1","doi-asserted-by":"publisher","first-page":"43721","DOI":"10.1109\/ACCESS.2019.2907383","volume":"7","author":"UP Singh","year":"2019","unstructured":"Singh UP, Chouhan SS, Jain S, Jain S (2019) Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access 7:43721\u201343729. https:\/\/doi.org\/10.1109\/ACCESS.2019.2907383","journal-title":"IEEE Access"},{"key":"8339_CR2","doi-asserted-by":"publisher","first-page":"2554","DOI":"10.1109\/JSTARS.2020.2995391","volume":"13","author":"L Windrim","year":"2020","unstructured":"Windrim L, Carnegie AJ, Webster M, Bryson M (2020) Tree detection and health monitoring in multispectral aerial imagery and photogrammetric pointclouds using machine learning. IEEE J Sel Top Appl Earth Obs Remote Sens 13:2554\u20132572. https:\/\/doi.org\/10.1109\/JSTARS.2020.2995391","journal-title":"IEEE J Sel Top Appl Earth Obs Remote Sens"},{"key":"8339_CR3","doi-asserted-by":"publisher","DOI":"10.1109\/IGARSS39084.2020.9323897","author":"S Zhu","year":"2020","unstructured":"Zhu S, Zhang J, Shuai G, Hongli L, Zhang F, Dong Z (2020) Autumn crop mapping based on deep learning method driven by historical labelled dataset. IEEE Int Geosci Remote Sens Symp. https:\/\/doi.org\/10.1109\/IGARSS39084.2020.9323897","journal-title":"IEEE Int Geosci Remote Sens Symp"},{"key":"8339_CR4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/ICONAT53423.2022.9725999","volume":"2022","author":"RK Megalingam","year":"2022","unstructured":"Megalingam RK, Darla VP, Nimmala CSK, Sankardas KS (2022) Computer vision-based measuring method to estimate the diameter of the coconut tree trunk. Int Conf Adv Technol 2022:1\u20136. https:\/\/doi.org\/10.1109\/ICONAT53423.2022.9725999","journal-title":"Int Conf Adv Technol"},{"issue":"1","key":"8339_CR5","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1109\/TMECH.2020.3014293","volume":"26","author":"RK Megalingam","year":"2021","unstructured":"Megalingam RK et al (2021) Amaran: an unmanned robotic coconut tree climber and harvester. IEEE\/ASME Trans Mechatron 26(1):288\u2013299. https:\/\/doi.org\/10.1109\/TMECH.2020.3014293","journal-title":"IEEE\/ASME Trans Mechatron"},{"key":"8339_CR6","doi-asserted-by":"publisher","first-page":"35512","DOI":"10.1109\/ACCESS.2018.2851376","volume":"6","author":"R Fern\u00e1ndez","year":"2018","unstructured":"Fern\u00e1ndez R, Montes H, Surdilovic J, Surdilovic D, Gonzalez-De-Santos P, Armada M (2018) Automatic detection of field-grown cucumbers for robotic harvesting. IEEE Access 6:35512\u201335527. https:\/\/doi.org\/10.1109\/ACCESS.2018.2851376","journal-title":"IEEE Access"},{"key":"8339_CR7","doi-asserted-by":"publisher","first-page":"23814","DOI":"10.1109\/ACCESS.2021.3056577","volume":"9","author":"M Tufail","year":"2021","unstructured":"Tufail M, Iqbal J, Tiwana MI, Alam MS, Khan ZA, Khan MT (2021) Identification of tobacco crop based on machine learning for a precision agricultural sprayer. IEEE Access 9:23814\u201323825. https:\/\/doi.org\/10.1109\/ACCESS.2021.3056577","journal-title":"IEEE Access"},{"key":"8339_CR8","doi-asserted-by":"publisher","unstructured":"Korkut UB, G\u00f6kt\u00fcrk \u00d6B, Yildiz O (2018) Detection of plant diseases by machine learning. In: 2018 26th Signal processing and communications applications conference (SIU). pp 1\u20134. https:\/\/doi.org\/10.1109\/SIU.2018.8404692","DOI":"10.1109\/SIU.2018.8404692"},{"key":"8339_CR9","doi-asserted-by":"publisher","unstructured":"Venkataraman D, Mangayarkarasi N (2017) Support vector machine-based classification of medicinal plants using leaf features. In: 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI). pp 793\u2013798. https:\/\/doi.org\/10.1109\/ICACCI.2017.8125939.","DOI":"10.1109\/ICACCI.2017.8125939"},{"key":"8339_CR10","doi-asserted-by":"publisher","unstructured":"Medar R, Rajpurohit VS, Shweta S (2019) Crop yield prediction using machine learning techniques. In: 2019 IEEE 5th International conference for convergence in technology (I2CT). pp 1\u20135. https:\/\/doi.org\/10.1109\/I2CT45611.2019.9033611","DOI":"10.1109\/I2CT45611.2019.9033611"},{"key":"8339_CR11","doi-asserted-by":"publisher","unstructured":"Shruthi U, Nagaveni V, Raghavendra BK (2019) A review on machine learning classification techniques for plant disease detection. In: 2019 5th International conference on advanced computing & communication systems (ICACCS). pp 281\u2013284. https:\/\/doi.org\/10.1109\/ICACCS.2019.8728415","DOI":"10.1109\/ICACCS.2019.8728415"},{"key":"8339_CR12","doi-asserted-by":"publisher","first-page":"3295","DOI":"10.1109\/JSTARS.2019.2922469","volume":"12","author":"S feng","year":"2016","unstructured":"feng S, zhao J, liu T, zhang H, zhang Z, guo X (2016) Crop type identification and mapping using machine learning algorithms and sentinel-2 time series data. IEEE Access 12:3295\u20133306. https:\/\/doi.org\/10.1109\/JSTARS.2019.2922469","journal-title":"IEEE Access"},{"key":"8339_CR13","doi-asserted-by":"publisher","first-page":"50358","DOI":"10.1109\/ACCESS.2021.3069449","volume":"9","author":"R Alfred","year":"2021","unstructured":"Alfred R, Obit JH, Chin CP-Y, Haviluddin H, Lim Y (2021) Towards paddy rice smart farming: a review on big data, machine learning, and rice production tasks. IEEE Access 9:50358\u201350380. https:\/\/doi.org\/10.1109\/ACCESS.2021.3069449","journal-title":"IEEE Access"},{"key":"8339_CR14","doi-asserted-by":"publisher","unstructured":"Chaitanya Reddy P, Chandra RMS, Vadiraj P, Ayyappa Reddy M, Mahesh TR, Sindhu Madhuri G (2021) Detection of plant leaf-based diseases using machine learning approach. In: 2021 IEEE International conference on computation system and information technology for sustainable solutions (CSITSS). pp 1\u20134. https:\/\/doi.org\/10.1109\/CSITSS54238.2021.9683020","DOI":"10.1109\/CSITSS54238.2021.9683020"},{"key":"8339_CR15","doi-asserted-by":"publisher","unstructured":"Jaisakthi SM, Mirunalini P, Thenmozhi D, Vatsala (2019) Grape leaf disease identification using machine learning techniques. In: 2019 International conference on computational intelligence in data science (ICCIDS). pp 1\u20136. https:\/\/doi.org\/10.1109\/ICCIDS.2019.8862084","DOI":"10.1109\/ICCIDS.2019.8862084"},{"key":"8339_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/GIOTS.2018.8534558","volume":"2018","author":"J Treboux","year":"2018","unstructured":"Treboux J, Genoud D (2018) Improved machine learning methodology for high precision agriculture. Glob Internet Things Summit (GIoTS) 2018:1\u20136. https:\/\/doi.org\/10.1109\/GIOTS.2018.8534558","journal-title":"Glob Internet Things Summit (GIoTS)"},{"key":"8339_CR17","doi-asserted-by":"publisher","unstructured":"Memon R, Memon M, Malioto N, Raza MO (2021) Identification of growth stages of crops using mobile phone images and machine learning. In: 2021 International conference on computing, Electronic and Electrical Engineering (ICE Cube). pp 1\u20136. https:\/\/doi.org\/10.1109\/ICECube53880.2021.9628197","DOI":"10.1109\/ICECube53880.2021.9628197"},{"key":"8339_CR18","doi-asserted-by":"publisher","unstructured":"Jena L, Behera SK, Sethy PK (2021) Identification of wheat grain using geometrical feature and machine learning. In: 2021 2nd International conference for emerging technology (INCET). pp 1\u20136. https:\/\/doi.org\/10.1109\/INCET51464.2021.9456281","DOI":"10.1109\/INCET51464.2021.9456281"},{"issue":"3","key":"8339_CR19","first-page":"2509","volume":"12","author":"AN Daneshwari","year":"2022","unstructured":"Daneshwari AN, Basavaraju DR (2022) Corn leaf image classification based on machine learning techniques for accurate leaf disease detection. Int J Electric Comput Eng 12(3):2509","journal-title":"Int J Electric Comput Eng"},{"key":"8339_CR20","doi-asserted-by":"publisher","first-page":"5390","DOI":"10.1109\/ACCESS.2022.3141371","volume":"10","author":"SM Hassan","year":"2022","unstructured":"Hassan SM, Maji AK (2022) Plant disease identification using a novel convolutional neural network. IEEE Access 10:5390\u20135401. https:\/\/doi.org\/10.1109\/ACCESS.2022.3141371","journal-title":"IEEE Access"},{"key":"8339_CR21","doi-asserted-by":"publisher","first-page":"789","DOI":"10.1109\/ACCESS.2021.3138920","volume":"10","author":"SCK","year":"2022","unstructured":"SCK, Jcd, Patil N (2022) Cardamom plant disease detection approach using efficientnetV2. IEEE Access 10:789\u2013804. https:\/\/doi.org\/10.1109\/ACCESS.2021.3138920","journal-title":"IEEE Access"},{"key":"8339_CR22","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1109\/SCSE53661.2021.9568308","volume":"2021","author":"S Sangeevan","year":"2021","unstructured":"Sangeevan S (2021) Deep learning-based pesticides prescription system for leaf diseases of home garden crops in Sri Lanka. Int Res Conf Smart Comput Syst Eng 2021:94\u201398. https:\/\/doi.org\/10.1109\/SCSE53661.2021.9568308","journal-title":"Int Res Conf Smart Comput Syst Eng"},{"key":"8339_CR23","doi-asserted-by":"publisher","unstructured":"Tunio MH, Jianping L, Butt MHF, Memon I (2021) Identification and classification of rice plant disease using hybrid transfer learning. In: 2021 18th International computer conference on wavelet active media technology and information processing (ICCWAMTIP). pp 525\u2013529. https:\/\/doi.org\/10.1109\/ICCWAMTIP53232.2021.9674124","DOI":"10.1109\/ICCWAMTIP53232.2021.9674124"},{"issue":"1","key":"8339_CR24","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1109\/TLA.2022.9662170","volume":"20","author":"S Ortega Cisneros","year":"2022","unstructured":"Ortega Cisneros S, Ruiz Varela JM, Rivera Acosta MA, Rivera Dominguez J, Moreno Villalobos P (2022) Pollen grains classification with a deep learning system GPU-trained. IEEE Latin America Trans 20(1):22\u201331. https:\/\/doi.org\/10.1109\/TLA.2022.9662170","journal-title":"IEEE Latin America Trans"},{"key":"8339_CR25","doi-asserted-by":"publisher","first-page":"31103","DOI":"10.1109\/ACCESS.2022.3159678","volume":"10","author":"H Amin","year":"2022","unstructured":"Amin H, Darwish A, Hassanien AE, Soliman M (2022) End-to-end deep learning model for corn leaf disease classification. IEEE Access 10:31103\u201331115. https:\/\/doi.org\/10.1109\/ACCESS.2022.3159678","journal-title":"IEEE Access"},{"issue":"2","key":"8339_CR26","doi-asserted-by":"publisher","first-page":"3599","DOI":"10.1109\/LRA.2021.3060712","volume":"6","author":"J Weyler","year":"2021","unstructured":"Weyler J, Milioto A, Falck T, Behley J, Stachniss C (2021) Joint plant instance detection and leaf count estimation for in-field plant phenotyping. IEEE Robot Autom Lett 6(2):3599\u20133606. https:\/\/doi.org\/10.1109\/LRA.2021.3060712","journal-title":"IEEE Robot Autom Lett"},{"key":"8339_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/AIMV53313.2021.9670918","volume":"2021","author":"A Lakshmanarao","year":"2021","unstructured":"Lakshmanarao A, Babu MR, Kiran TSR (2021) Plant disease prediction and classification using deep learning convnets. Int Conf Artif Intell Mach Vis (AIMV) 2021:1\u20136. https:\/\/doi.org\/10.1109\/AIMV53313.2021.9670918","journal-title":"Int Conf Artif Intell Mach Vis (AIMV)"},{"key":"8339_CR28","doi-asserted-by":"crossref","unstructured":"Trivedi NK, Maheshwari S, Anand A, Kumar A, Rathor VS (2023) Identify and classify corn leaf diseases using a deep neural network architecture. In: Proceedings of seventh international congress on information and communication technology (pp 873\u2013880). Springer, Singapore","DOI":"10.1007\/978-981-19-1610-6_78"},{"key":"8339_CR29","doi-asserted-by":"crossref","unstructured":"Ganeshkumar M, Sowmya V, Gopalakrishnan EA, Soman, KP (2022) Unsupervised deep learning approach for the identification of intracranial haemorrhage in CT images using PCA-Net and K-Means algorithm. In: Intelligent vision in healthcare (pp. 23\u201331). Springer, Singapore","DOI":"10.1007\/978-981-16-7771-7_3"},{"issue":"11","key":"8339_CR30","doi-asserted-by":"publisher","first-page":"2330","DOI":"10.1109\/TLA.2022.9904757","volume":"20","author":"JA Campos-Leal","year":"2022","unstructured":"Campos-Leal JA, Yee-Rend\u00f3n A, Vega-L\u00f3pez IF (2022) Simplifying VGG-16 for plant species identification. IEEE Lat Am Trans 20(11):2330\u20132338. https:\/\/doi.org\/10.1109\/TLA.2022.9904757","journal-title":"IEEE Lat Am Trans"},{"key":"8339_CR31","doi-asserted-by":"publisher","first-page":"115287","DOI":"10.1109\/ACCESS.2020.3001237","volume":"8","author":"W-J Hu","year":"2020","unstructured":"Hu W-J, Fan J, Du Y-X, Li B-S, Xiong N, Bekkering E (2020) MDFC\u2013ResNet: an agricultural IoT system to accurately recognize crop diseases. IEEE Access 8:115287\u2013115298. https:\/\/doi.org\/10.1109\/ACCESS.2020.3001237","journal-title":"IEEE Access"},{"issue":"14","key":"8339_CR32","doi-asserted-by":"publisher","first-page":"14628","DOI":"10.1109\/JSEN.2022.3182304","volume":"22","author":"J Chen","year":"2022","unstructured":"Chen J, Chen W, Zeb A, Yang S, Zhang D (2022) Lightweight inception networks for the recognition and detection of rice plant diseases. IEEE Sens J 22(14):14628\u201314638. https:\/\/doi.org\/10.1109\/JSEN.2022.3182304","journal-title":"IEEE Sens J"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08339-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-023-08339-w\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-023-08339-w.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,5,11]],"date-time":"2023-05-11T17:22:36Z","timestamp":1683825756000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-023-08339-w"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,23]]},"references-count":32,"journal-issue":{"issue":"16","published-print":{"date-parts":[[2023,6]]}},"alternative-id":["8339"],"URL":"https:\/\/doi.org\/10.1007\/s00521-023-08339-w","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,2,23]]},"assertion":[{"value":"15 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 January 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2023","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 would like to declare that there is no conflict of interest with this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}