{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T19:51:56Z","timestamp":1776109916290,"version":"3.50.1"},"reference-count":52,"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\/501100008984","name":"Indian Institute of Technology Kharagpur","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008984","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.eswa.2026.132067","type":"journal-article","created":{"date-parts":[[2026,3,16]],"date-time":"2026-03-16T17:08:55Z","timestamp":1773680935000},"page":"132067","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Optimized deep learning models with heatmap visualization for optimal maturity and automated yield assessment in precision cotton farming"],"prefix":"10.1016","volume":"318","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5917-0747","authenticated-orcid":false,"given":"Pooja","family":"Verma","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4584-2119","authenticated-orcid":false,"given":"Ayan","family":"Paul","sequence":"additional","affiliation":[]},{"given":"Rajendra","family":"Machavaram","sequence":"additional","affiliation":[]},{"given":"Mahua","family":"Bhattacharya","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132067_b0005","doi-asserted-by":"crossref","first-page":"117115","DOI":"10.1109\/ACCESS.2019.2936536","article-title":"Date fruit classification for robotic harvesting in a natural environment using deep learning","volume":"7","author":"Altaheri","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2026.132067_b0010","doi-asserted-by":"crossref","first-page":"124363","DOI":"10.1109\/ACCESS.2022.3220234","article-title":"Real-time monitoring method of strawberry fruit growth state based on YOLO improved model","volume":"10","author":"An","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2026.132067_b0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.108757","article-title":"Object detection and tracking in Precision Farming: A systematic review","volume":"219","author":"Ariza-Sent\u00eds","year":"2024","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.1016\/j.eswa.2026.132067_b0020","doi-asserted-by":"crossref","first-page":"180","DOI":"10.1016\/j.isprsjprs.2020.09.015","article-title":"Developing a machine learning based cotton yield estimation framework using multi-temporal UAS data","volume":"169","author":"Ashapure","year":"2020","journal-title":"ISPRS Journal of Photogrammetry and Remote Sensing"},{"key":"10.1016\/j.eswa.2026.132067_b0025","series-title":"August). End-to-end object detection with transformers","first-page":"213","author":"Carion","year":"2020"},{"key":"10.1016\/j.eswa.2026.132067_b0030","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.fcr.2016.02.003","article-title":"Mechanisms and regulation of senescence and maturity performance in cotton","volume":"189","author":"Chen","year":"2016","journal-title":"Field Crops Research"},{"key":"10.1016\/j.eswa.2026.132067_b0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.108622","article-title":"Multi-growth stage plant recognition: A case study of Palmer amaranth (Amaranthus palmeri) in cotton (Gossypium hirsutum)","volume":"217","author":"Coleman","year":"2024","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.1016\/j.eswa.2026.132067_b0040","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1016\/j.aiia.2022.09.007","article-title":"Deep learning based computer vision approaches for smart agricultural applications","volume":"6","author":"Dhanya","year":"2022","journal-title":"Artificial Intelligence in Agriculture"},{"key":"10.1016\/j.eswa.2026.132067_b0045","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.compind.2018.03.010","article-title":"Apple flower detection using deep convolutional networks","volume":"99","author":"Dias","year":"2018","journal-title":"Computers in Industry"},{"issue":"2","key":"10.1016\/j.eswa.2026.132067_b0050","doi-asserted-by":"crossref","first-page":"4989","DOI":"10.32604\/cmc.2022.023884","article-title":"Optimized ensemble algorithm for predicting metamaterial antenna parameters","volume":"71","author":"El-Kenawy","year":"2022","journal-title":"Computers, Materials and Continua"},{"issue":"23","key":"10.1016\/j.eswa.2026.132067_b0055","doi-asserted-by":"crossref","first-page":"4421","DOI":"10.3390\/math10234421","article-title":"Metaheuristic optimization for improving weed detection in wheat images captured by drones","volume":"10","author":"El-Kenawy","year":"2022","journal-title":"Mathematics"},{"key":"10.1016\/j.eswa.2026.132067_b0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105634","article-title":"Multi-class fruit-on-plant detection for apple in SNAP system using Faster R-CNN","volume":"176","author":"Gao","year":"2020","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.1016\/j.eswa.2026.132067_b0070","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compag.2015.05.021","article-title":"Sensors and systems for fruit detection and localization: A review","volume":"116","author":"Gongal","year":"2015","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.1016\/j.eswa.2026.132067_b0075","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.fcr.2016.01.002","article-title":"Cotton crop maturity: A compendium of measures and predictors","volume":"191","author":"Gwathmey","year":"2016","journal-title":"Field Crops Research"},{"key":"10.1016\/j.eswa.2026.132067_b0080","unstructured":"ICAR-AICRP (Cotton) Annual Report (2022-23)."},{"key":"10.1016\/j.eswa.2026.132067_b0085","doi-asserted-by":"crossref","first-page":"1066","DOI":"10.1016\/j.procs.2022.01.135","article-title":"A Review of Yolo algorithm developments","volume":"199","author":"Jiang","year":"2022","journal-title":"Procedia computer science"},{"key":"10.1016\/j.eswa.2026.132067_b0090","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-020-00698-y","article-title":"DeepFlower: A deep learning-based approach to characterize flowering patterns of cotton plants in the field","volume":"16","author":"Jiang","year":"2020","journal-title":"Plant Methods"},{"key":"10.1016\/j.eswa.2026.132067_b0095","article-title":"1-tensorrt, tensorflow edge tpu and openvino export and inference","volume":"ultralytics\/yolov5","author":"Jocher","year":"2022","journal-title":"Zenodo"},{"key":"10.1016\/j.eswa.2026.132067_b0100","unstructured":"Kaggle data, https:\/\/www.kaggle.com\/datasets\/panashemusemwa\/cotton\/code."},{"key":"10.1016\/j.eswa.2026.132067_b0105","doi-asserted-by":"crossref","first-page":"106673","DOI":"10.1109\/ACCESS.2022.3212081","article-title":"An archive-based multi-objective arithmetic optimization algorithm for solving industrial engineering problems","volume":"10","author":"Khodadadi","year":"2022","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2026.132067_b0110","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.compag.2019.04.017","article-title":"Deep learning\u2013Method overview and review of use for fruit detection and yield estimation","volume":"162","author":"Koirala","year":"2019","journal-title":"Computers and electronics in agriculture"},{"key":"10.1016\/j.eswa.2026.132067_b0115","doi-asserted-by":"crossref","unstructured":"K., S. Elkanzi, M. (2024). A Review on the Role of Machine Learning in Predicting the Spread of Infectious Diseases. Metaheuristic Optimization Review, 14 27.","DOI":"10.54216\/MOR.020102"},{"key":"10.1016\/j.eswa.2026.132067_b0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105745","article-title":"Unsupervised domain adaptation for in-field cotton boll status identification","volume":"178","author":"Li","year":"2020","journal-title":"Computers and Electronics in Agriculture"},{"issue":"8","key":"10.1016\/j.eswa.2026.132067_b0125","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.3390\/agronomy13081988","article-title":"YOLO-C: An Efficient and Robust Detection Algorithm for Mature Long Staple Cotton Targets with High-Resolution RGB Images","volume":"13","author":"Liang","year":"2023","journal-title":"Agronomy"},{"key":"10.1016\/j.eswa.2026.132067_b0130","doi-asserted-by":"crossref","unstructured":"Liu, Qianhui, Yan Zhang, and Gongping Yang. \u201cSmall unopened cotton boll counting by detection with MRF-YOLO in the wild (2023).\u201d Computers and Electronics in Agriculture, 204, 107576.","DOI":"10.1016\/j.compag.2022.107576"},{"key":"10.1016\/j.eswa.2026.132067_b0135","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., & Berg, A. C. (2016). Ssd: Single shot multibox detector. In Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14 (pp. 21-37). Springer International Publishing.","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"7","key":"10.1016\/j.eswa.2026.132067_b0140","article-title":"COTTON PHYSIOLOGY TODAY","volume":"5","author":"Louncll","year":"1994","journal-title":"Newsletter of the Cotton Physiology Education Program\u2013NATIONAL COTTON COUNCIL"},{"key":"10.1016\/j.eswa.2026.132067_b0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2022.106954","article-title":"A visual identification method for the apple growth forms in the orchard","volume":"197","author":"Lv","year":"2022","journal-title":"Computers and Electronics in Agriculture"},{"issue":"2","key":"10.1016\/j.eswa.2026.132067_b0150","doi-asserted-by":"crossref","first-page":"956","DOI":"10.1002\/agj2.20516","article-title":"Cotton boll distribution: A review","volume":"113","author":"Pabuayon","year":"2021","journal-title":"Agronomy Journal"},{"key":"10.1016\/j.eswa.2026.132067_b0160","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.compag.2017.03.010","article-title":"Automatic fruit count on coffee branches using computer vision","volume":"137","author":"Ramos","year":"2017","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.1016\/j.eswa.2026.132067_b0165","series-title":"In Proceedings of the IEEE conference on computer vision and pattern recognition","first-page":"779","article-title":"You only look once: Unified, real-time object detection","author":"Redmon","year":"2016"},{"key":"10.1016\/j.eswa.2026.132067_b0170","series-title":"Advances in neural information processing systems","first-page":"28","article-title":"Faster r-cnn: Towards real-time object detection with region proposal networks","author":"Ren","year":"2015"},{"key":"10.1016\/j.eswa.2026.132067_b0175","unstructured":"Ritchie, G. L., Bednarz, C. W., Jost, P. H., & Brown, S. M. (2007). Cotton growth and development."},{"issue":"9","key":"10.1016\/j.eswa.2026.132067_b0180","doi-asserted-by":"crossref","first-page":"254","DOI":"10.3390\/drones6090254","article-title":"Cotton yield estimation using the remotely sensed cotton boll index from UAV images","volume":"6","author":"Shi","year":"2022","journal-title":"Drones"},{"key":"10.1016\/j.eswa.2026.132067_b0185","article-title":"Cotton boll localization method based on point annotation and multi-scale fusion","volume":"13","author":"Sun","year":"2022","journal-title":"Frontiers in Plant Science"},{"key":"10.1016\/j.eswa.2026.132067_b0190","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.104976","article-title":"Image processing algorithms for infield single cotton boll counting and yield prediction","volume":"166","author":"Sun","year":"2019","journal-title":"Computers and electronics in agriculture"},{"key":"10.1016\/j.eswa.2026.132067_b0195","doi-asserted-by":"crossref","first-page":"1316","DOI":"10.1007\/s13197-013-1188-3","article-title":"Assessment of banana fruit maturity by image processing technique","volume":"52","author":"Surya Prabha","year":"2015","journal-title":"Journal of food science and technology"},{"key":"10.1016\/j.eswa.2026.132067_b0205","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105307","article-title":"Convolutional neural networks in predicting cotton yield from images of commercial fields","volume":"171","author":"Tedesco-Oliveira","year":"2020","journal-title":"Computers and Electronics in Agriculture"},{"issue":"1","key":"10.1016\/j.eswa.2026.132067_b0210","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.inpa.2019.09.006","article-title":"Computer vision technology in agricultural automation\u2014A review","volume":"7","author":"Tian","year":"2020","journal-title":"Information Processing in Agriculture"},{"key":"10.1016\/j.eswa.2026.132067_b0215","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.compag.2019.01.012","article-title":"Apple detection during different growth stages in orchards using the improved YOLO-V3 model","volume":"157","author":"Tian","year":"2019","journal-title":"Computers and electronics in agriculture"},{"key":"10.1016\/j.eswa.2026.132067_b0220","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105348","article-title":"Comparison of convolutional neural networks in fruit detection and counting: A comprehensive evaluation","volume":"173","author":"Vasconez","year":"2020","journal-title":"Computers and Electronics in Agriculture"},{"issue":"2","key":"10.1016\/j.eswa.2026.132067_b0225","doi-asserted-by":"crossref","first-page":"494","DOI":"10.2134\/agronj2005.0494","article-title":"Predicting cotton boll maturation period using degree days and other climatic factors","volume":"97","author":"Viator","year":"2005","journal-title":"Agronomy journal"},{"key":"10.1016\/j.eswa.2026.132067_b0230","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.compag.2018.01.011","article-title":"A methodology for fresh tomato maturity detection using computer vision","volume":"146","author":"Wan","year":"2018","journal-title":"Computers and electronics in agriculture"},{"key":"10.1016\/j.eswa.2026.132067_b0235","series-title":"In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","first-page":"7464","article-title":"YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors","author":"Wang","year":"2023"},{"key":"10.1016\/j.eswa.2026.132067_b0240","doi-asserted-by":"crossref","first-page":"2235","DOI":"10.3389\/fpls.2017.02235","article-title":"Aerial images and convolutional neural network for cotton bloom detection","volume":"8","author":"Xu","year":"2018","journal-title":"Frontiers in plant science"},{"key":"10.1016\/j.eswa.2026.132067_b0245","doi-asserted-by":"crossref","DOI":"10.1016\/j.jag.2021.102511","article-title":"Cotton yield estimation model based on machine learning using time series UAV remote sensing data","volume":"104","author":"Xu","year":"2021","journal-title":"International Journal of Applied Earth Observation and Geoinformation"},{"key":"10.1016\/j.eswa.2026.132067_b0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105762","article-title":"Establishing a model to predict the single boll weight of cotton in northern Xinjiang by using high resolution UAV remote sensing data","volume":"179","author":"Xu","year":"2020","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.1016\/j.eswa.2026.132067_b0255","article-title":"Brief summary of YOLOv8 model structure","author":"RangeKing","year":"2023","journal-title":"[online] Available"},{"key":"10.1016\/j.eswa.2026.132067_b0260","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.aiia.2022.11.005","article-title":"Assessing the performance of YOLOv5 algorithm for detecting volunteer cotton plants in corn fields at three different growth stages","volume":"6","author":"Yadav","year":"2022","journal-title":"Artificial intelligence in agriculture"},{"key":"10.1016\/j.eswa.2026.132067_b0265","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2019.06.001","article-title":"Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN","volume":"163","author":"Yu","year":"2019","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.1016\/j.eswa.2026.132067_b0270","series-title":"October). Bytetrack: Multi-object tracking by associating every detection box","first-page":"1","author":"Zhang","year":"2022"},{"key":"10.1016\/j.eswa.2026.132067_b0275","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2023.107857","article-title":"YOLO-BYTE: An efficient multi-object tracking algorithm for automatic monitoring of dairy cows","volume":"209","author":"Zheng","year":"2023","journal-title":"Computers and Electronics in Agriculture"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426009802?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426009802?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,13]],"date-time":"2026-04-13T18:21:43Z","timestamp":1776104503000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426009802"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":52,"alternative-id":["S0957417426009802"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132067","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Optimized deep learning models with heatmap visualization for optimal maturity and automated yield assessment in precision cotton farming","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132067","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":"132067"}}