{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T15:57:24Z","timestamp":1776182244927,"version":"3.50.1"},"reference-count":122,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T00:00:00Z","timestamp":1583798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Research and development of intelligent model and precise monitoring of shrimp processing","award":["2018YFD0700904-2"],"award-info":[{"award-number":["2018YFD0700904-2"]}]},{"name":"Next Generation Precision Aquaculture: R&amp;D on intelligent measurement, control technology","award":["2017YFE0122100"],"award-info":[{"award-number":["2017YFE0122100"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic segmentation, and scene analysis. However, with the increase of dense scenes in reality, due to severe occlusions, and small size of objects, the analysis of dense scenes becomes particularly challenging. To overcome these problems, DL recently has been increasingly applied to dense scenes and has begun to be used in dense agricultural scenes. The purpose of this review is to explore the applications of DL for dense scenes analysis in agriculture. In order to better elaborate the topic, we first describe the types of dense scenes in agriculture, as well as the challenges. Next, we introduce various popular deep neural networks used in these dense scenes. Then, the applications of these structures in various agricultural tasks are comprehensively introduced in this review, including recognition and classification, detection, counting and yield estimation. Finally, the surveyed DL applications, limitations and the future work for analysis of dense images in agriculture are summarized.<\/jats:p>","DOI":"10.3390\/s20051520","type":"journal-article","created":{"date-parts":[[2020,3,10]],"date-time":"2020-03-10T11:59:36Z","timestamp":1583841576000},"page":"1520","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":170,"title":["Applications of Deep Learning for Dense Scenes Analysis in Agriculture: A Review"],"prefix":"10.3390","volume":"20","author":[{"given":"Qian","family":"Zhang","sequence":"first","affiliation":[{"name":"National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China"},{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"},{"name":"Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, China"}]},{"given":"Yeqi","family":"Liu","sequence":"additional","affiliation":[{"name":"National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China"},{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"},{"name":"Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, China"}]},{"given":"Chuanyang","family":"Gong","sequence":"additional","affiliation":[{"name":"National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China"},{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"},{"name":"Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9635-8044","authenticated-orcid":false,"given":"Yingyi","family":"Chen","sequence":"additional","affiliation":[{"name":"National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China"},{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"},{"name":"Beijing Engineering and Technology Research Center for Internet of Things in Agriculture, Beijing 100083, China"},{"name":"Key Laboratory of Agricultural Information Acquisition Technology, Ministry of Agriculture, Beijing 100083, China"}]},{"given":"Huihui","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Information Science &amp; Technology, Beijing Forestry University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"388","DOI":"10.1002\/ird.2076","article-title":"Towards a Second Green Revolution","volume":"65","author":"Tyagi","year":"2016","journal-title":"Irrig. 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