{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T12:19:18Z","timestamp":1774527558970,"version":"3.50.1"},"reference-count":142,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2024,3,12]],"date-time":"2024-03-12T00:00:00Z","timestamp":1710201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program Project","award":["2023YFD2201805"],"award-info":[{"award-number":["2023YFD2201805"]}]},{"name":"National Key Research and Development Program Project","award":["BAIC10-2024"],"award-info":[{"award-number":["BAIC10-2024"]}]},{"name":"National Key Research and Development Program Project","award":["Z231100003923005"],"award-info":[{"award-number":["Z231100003923005"]}]},{"name":"Beijing Smart Agriculture Innovation Consortium Project","award":["2023YFD2201805"],"award-info":[{"award-number":["2023YFD2201805"]}]},{"name":"Beijing Smart Agriculture Innovation Consortium Project","award":["BAIC10-2024"],"award-info":[{"award-number":["BAIC10-2024"]}]},{"name":"Beijing Smart Agriculture Innovation Consortium Project","award":["Z231100003923005"],"award-info":[{"award-number":["Z231100003923005"]}]},{"name":"Beijing Science and Technology Plan","award":["2023YFD2201805"],"award-info":[{"award-number":["2023YFD2201805"]}]},{"name":"Beijing Science and Technology Plan","award":["BAIC10-2024"],"award-info":[{"award-number":["BAIC10-2024"]}]},{"name":"Beijing Science and Technology Plan","award":["Z231100003923005"],"award-info":[{"award-number":["Z231100003923005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Yield calculation is an important link in modern precision agriculture that is an effective means to improve breeding efficiency and to adjust planting and marketing plans. With the continuous progress of artificial intelligence and sensing technology, yield-calculation schemes based on image-processing technology have many advantages such as high accuracy, low cost, and non-destructive calculation, and they have been favored by a large number of researchers. This article reviews the research progress of crop-yield calculation based on remote sensing images and visible light images, describes the technical characteristics and applicable objects of different schemes, and focuses on detailed explanations of data acquisition, independent variable screening, algorithm selection, and optimization. Common issues are also discussed and summarized. Finally, solutions are proposed for the main problems that have arisen so far, and future research directions are predicted, with the aim of achieving more progress and wider popularization of yield-calculation solutions based on image technology.<\/jats:p>","DOI":"10.3390\/rs16061003","type":"journal-article","created":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T03:46:31Z","timestamp":1710301591000},"page":"1003","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Advancements in Utilizing Image-Analysis Technology for Crop-Yield Estimation"],"prefix":"10.3390","volume":"16","author":[{"given":"Feng","family":"Yu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"given":"Ming","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1799-3948","authenticated-orcid":false,"given":"Jun","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Qian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"given":"Jinmeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"given":"Xin","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"given":"Yang","family":"Ping","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]},{"given":"Rupeng","family":"Luan","sequence":"additional","affiliation":[{"name":"Institute of Data Science and Agricultural Economics, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"103016","DOI":"10.1016\/j.agsy.2020.103016","article-title":"Machine learning for large-scale crop yield forecasting","volume":"187","author":"Paudel","year":"2021","journal-title":"Agric. 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