{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T21:06:00Z","timestamp":1774040760505,"version":"3.50.1"},"reference-count":41,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100014717","name":"Youth Science Fund Project","doi-asserted-by":"publisher","award":["62002215"],"award-info":[{"award-number":["62002215"]}],"id":[{"id":"10.13039\/100014717","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014717","name":"Youth Science Fund Project","doi-asserted-by":"publisher","award":["62401352"],"award-info":[{"award-number":["62401352"]}],"id":[{"id":"10.13039\/100014717","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People's Republic of China","doi-asserted-by":"publisher","award":["SCRC2024ZZ05TS"],"award-info":[{"award-number":["SCRC2024ZZ05TS"]}],"id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52273228"],"award-info":[{"award-number":["52273228"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009002","name":"Shanghai University","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100009002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Sciences"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.ins.2026.123283","type":"journal-article","created":{"date-parts":[[2026,2,23]],"date-time":"2026-02-23T07:43:30Z","timestamp":1771832610000},"page":"123283","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A multi-task learning framework for integrated assessment in agricultural applications"],"prefix":"10.1016","volume":"741","author":[{"given":"Yuexing","family":"Han","sequence":"first","affiliation":[]},{"given":"Jiahao","family":"Ge","sequence":"additional","affiliation":[]},{"given":"Yan","family":"Sun","sequence":"additional","affiliation":[]},{"given":"Tiejun","family":"Zhao","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6630-5339","authenticated-orcid":false,"given":"Qiaochuan","family":"Chen","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.ins.2026.123283_bib0005","doi-asserted-by":"crossref","DOI":"10.3390\/app13137405","article-title":"Artificial intelligence in agriculture: benefits, challenges, and trends","author":"Oliveira","year":"2023","journal-title":"Appl. Sci."},{"key":"10.1016\/j.ins.2026.123283_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2023.108799","article-title":"Smart and sustainable agriculture: fundamentals, enabling technologies, and future directions","volume":"110","author":"Jararweh","year":"2023","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.ins.2026.123283_bib0015","series-title":"International Symposium on Distributed Computing and Artificial Intelligence","first-page":"261","article-title":"Ai-powered crop monitoring for precision agriculture","author":"Hassan","year":"2024"},{"issue":"1","key":"10.1016\/j.ins.2026.123283_bib0020","doi-asserted-by":"crossref","first-page":"54","DOI":"10.3390\/insects14010054","article-title":"A new pest detection method based on improved yolov5m","volume":"14","author":"Dai","year":"2023","journal-title":"Insects"},{"key":"10.1016\/j.ins.2026.123283_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.fcr.2021.108377","article-title":"Machine learning for regional crop yield forecasting in Europe","volume":"276","author":"Paudel","year":"2022","journal-title":"Field Crops Res."},{"key":"10.1016\/j.ins.2026.123283_bib0030","doi-asserted-by":"crossref","DOI":"10.15832\/ankutbd.1434767","article-title":"Non-destructive weight prediction model of spherical fruits and vegetables using u-net image segmentation and machine learning methods","author":"Ko\u00e7","year":"2024","journal-title":"Tarim Bilim. Derg."},{"key":"10.1016\/j.ins.2026.123283_bib0035","series-title":"2023 Global Conference on Information Technologies and Communications (GCITC)","first-page":"1","article-title":"Ai-weigh live tracking of fruits and vegetables","author":"Kumar","year":"2023"},{"key":"10.1016\/j.ins.2026.123283_bib0040","series-title":"2024 5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV)","first-page":"342","article-title":"Classification of fruits and its quality prediction using deep learning","author":"Sangeetha","year":"2024"},{"key":"10.1016\/j.ins.2026.123283_bib0045","series-title":"2024 International Conference on Emerging Techniques in Computational Intelligence (ICETCI)","first-page":"373","article-title":"Fruit classification based on freshness","author":"Mallegowda","year":"2024"},{"issue":"2","key":"10.1016\/j.ins.2026.123283_bib0050","first-page":"160","article-title":"Applying linear regression to estimate weight of non axi-symmetric fruit","volume":"5","author":"Fitriyah","year":"2020","journal-title":"J. Inf. Technol. Comput. Sci."},{"key":"10.1016\/j.ins.2026.123283_bib0055","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2024.109262","article-title":"Advanced model predictive control strategies for nondestructive monitoring quality of fruit and vegetables during supply chain processes","volume":"225","author":"Zhang","year":"2024","journal-title":"Comput. Electron. Agric."},{"issue":"13","key":"10.1016\/j.ins.2026.123283_bib0060","doi-asserted-by":"crossref","DOI":"10.3390\/su151310482","article-title":"Using artificial intelligence to tackle food waste and enhance the circular economy: maximising resource efficiency and minimising environmental impact: a review","volume":"15","author":"Onyeaka","year":"2023","journal-title":"Sustainability"},{"key":"10.1016\/j.ins.2026.123283_bib0065","doi-asserted-by":"crossref","DOI":"10.1016\/j.foodpol.2025.102983","article-title":"A systematic review on the impact of artificial intelligence in the agri-food supply chain","volume":"137","author":"Reitano","year":"2025","journal-title":"Food Policy"},{"issue":"1","key":"10.1016\/j.ins.2026.123283_bib0070","doi-asserted-by":"crossref","first-page":"26","DOI":"10.2478\/ausi-2018-0002","article-title":"Fruit recognition from images using deep learning","volume":"10","author":"Muresan","year":"2018","journal-title":"Acta Univ. Sapientiae, Inform."},{"key":"10.1016\/j.ins.2026.123283_bib0075","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"2117","article-title":"Feature pyramid networks for object detection","author":"Lin","year":"2017"},{"key":"10.1016\/j.ins.2026.123283_bib0080","series-title":"2024 IEEE 21st International Conference on Smart Communities: Improving Quality of Life Using AI, Robotics and IoT (HONET)","first-page":"124","article-title":"Smartdate: ai-driven precision sorting and quality control in date fruits","author":"Eskaf","year":"2024"},{"key":"10.1016\/j.ins.2026.123283_bib0085","article-title":"AI in business AI driven water content measurement for detecting internal rot in fruits: enhancing quality control in grocery stores","author":"Vakthi","year":"2024","journal-title":"REST J. Data Anal. Artif. Intell."},{"key":"10.1016\/j.ins.2026.123283_bib0090","series-title":"Handbook of Research on Emerging Perspectives in Intelligent Pattern Recognition, Analysis, and Image Processing","first-page":"367","article-title":"A novel fuzzy logic classifier for classification and quality measurement of Apple fruit","author":"Kamila","year":"2016"},{"key":"10.1016\/j.ins.2026.123283_bib0095","first-page":"1","article-title":"Residual channel-attention (Rca) network for remote sensing image scene classification","author":"Gomaa","year":"2025","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.ins.2026.123283_bib0100","series-title":"2024 6th Novel Intelligent and Leading Emerging Sciences Conference (NILES)","first-page":"211","article-title":"Advanced domain adaptation technique for object detection leveraging semi-automated dataset construction and enhanced yolov8","author":"Gomaa","year":"2024"},{"issue":"6","key":"10.1016\/j.ins.2026.123283_bib0105","doi-asserted-by":"crossref","first-page":"255","DOI":"10.3390\/wevj15060255","article-title":"Novel deep learning domain adaptation approach for object detection using semi-self building dataset and modified yolov4","volume":"15","author":"Gomaa","year":"2024","journal-title":"World Electr. Veh. J."},{"key":"10.1016\/j.ins.2026.123283_bib0110","series-title":"Machine Learning: Proceedings of the Tenth International Conference","first-page":"41","article-title":"Multitask learning: a knowledge-based source of inductive bias","author":"Caruna","year":"1993"},{"key":"10.1016\/j.ins.2026.123283_bib0115","series-title":"Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining","first-page":"1930","article-title":"Modeling task relationships in multi-task learning with multi-gate mixture-of-experts","author":"Ma","year":"2018"},{"key":"10.1016\/j.ins.2026.123283_bib0120","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"1871","article-title":"End-to-end multi-task learning with attention","author":"Liu","year":"2019"},{"key":"10.1016\/j.ins.2026.123283_bib0125","article-title":"Agricultural object detection in complex environments via co-attention and self-knowledge distillation","author":"Han","year":"2025","journal-title":"Inf. Sci."},{"key":"10.1016\/j.ins.2026.123283_bib0130","series-title":"IECON 2021\u201347th Annual Conference of the IEEE Industrial Electronics Society","first-page":"1","article-title":"Fruit maturity recognition from agricultural, market and Automation perspectives","author":"Jerripothula","year":"2021"},{"key":"10.1016\/j.ins.2026.123283_bib0135","series-title":"Fruitnet and Fruitbox Dataset","author":"Sharafudeen","year":"2023"},{"key":"10.1016\/j.ins.2026.123283_bib0140","series-title":"Proceedings of the Fifteenth Indian Conference on Computer Vision Graphics and Image Processing","first-page":"1","article-title":"Applev: a dataset for Apple fruit volume estimation","author":"Barda","year":"2024"},{"issue":"4","key":"10.1016\/j.ins.2026.123283_bib0145","doi-asserted-by":"crossref","first-page":"11433","DOI":"10.1007\/s11042-023-16058-6","article-title":"Fruitq: a new dataset of multiple fruit images for freshness evaluation","volume":"83","author":"Abayomi-Alli","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"10.1016\/j.ins.2026.123283_bib0150","doi-asserted-by":"crossref","DOI":"10.1093\/gigascience\/giac052","article-title":"A novel ground truth multispectral image dataset with weight, anthocyanins, and brix index measures of grape berries tested for its utility in machine learning pipelines","volume":"11","author":"Navarro","year":"2022","journal-title":"GigaScience"},{"key":"10.1016\/j.ins.2026.123283_bib0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.dib.2021.107686","article-title":"Fruitnet: Indian fruits image dataset with quality for machine learning applications","volume":"40","author":"Meshram","year":"2022","journal-title":"Data in Brief"},{"key":"10.1016\/j.ins.2026.123283_bib0160","author":"Zahurul Haquea"},{"key":"10.1016\/j.ins.2026.123283_bib0165","author":"Jafary"},{"key":"10.1016\/j.ins.2026.123283_bib0170","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016"},{"issue":"4","key":"10.1016\/j.ins.2026.123283_bib0175","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1007\/s41095-023-0364-2","article-title":"Visual attention network","volume":"9","author":"Guo","year":"2023","journal-title":"Comput. Vis. Media"},{"key":"10.1016\/j.ins.2026.123283_bib0180","author":"Loshchilov"},{"key":"10.1016\/j.ins.2026.123283_bib0185","author":"Chuquimarca"},{"issue":"1","key":"10.1016\/j.ins.2026.123283_bib0190","article-title":"Fruitvision: a deep learning based automatic fruit grading system","volume":"9","author":"Hayat","year":"2024","journal-title":"Open Agric."},{"key":"10.1016\/j.ins.2026.123283_bib0195","first-page":"29335","article-title":"Dselect-k: differentiable selection in the mixture of experts with applications to multi-task learning","volume":"34","author":"Hazimeh","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.ins.2026.123283_bib0200","series-title":"International Conference on Machine Learning","first-page":"3854","article-title":"Learning to branch for multi-task learning","author":"Guo","year":"2020"},{"key":"10.1016\/j.ins.2026.123283_bib0205","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","first-page":"7482","article-title":"Multi-task learning using uncertainty to weigh losses for scene geometry and semantics","author":"Kendall","year":"2018"}],"container-title":["Information Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526002148?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0020025526002148?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T19:16:20Z","timestamp":1774034180000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0020025526002148"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":41,"alternative-id":["S0020025526002148"],"URL":"https:\/\/doi.org\/10.1016\/j.ins.2026.123283","relation":{},"ISSN":["0020-0255"],"issn-type":[{"value":"0020-0255","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A multi-task learning framework for integrated assessment in agricultural applications","name":"articletitle","label":"Article Title"},{"value":"Information Sciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ins.2026.123283","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"123283"}}