{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:06:11Z","timestamp":1775145971583,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T00:00:00Z","timestamp":1641945600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Grant No. 31971797 and No. 61601189"],"award-info":[{"award-number":["Grant No. 31971797 and No. 61601189"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"General program of Guangdong Natural Science Foundation","award":["2021A1515010923"],"award-info":[{"award-number":["2021A1515010923"]}]},{"name":"China Agriculture Research System of MOF and MARA","award":["CARS-26"],"award-info":[{"award-number":["CARS-26"]}]},{"name":"Special projects for key fields of colleges and universities in Guangdong Province","award":["2020ZDZX3061"],"award-info":[{"award-number":["2020ZDZX3061"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Green citrus detection in citrus orchards provides reliable support for production management chains, such as fruit thinning, sunburn prevention and yield estimation. In this paper, we proposed a lightweight object detection YOLOv5-CS (Citrus Sort) model to realize object detection and the accurate counting of green citrus in the natural environment. First, we employ image rotation codes to improve the generalization ability of the model. Second, in the backbone, a convolutional layer is replaced by a convolutional block attention module, and a detection layer is embedded to improve the detection accuracy of the little citrus. Third, both the loss function CIoU (Complete Intersection over Union) and cosine annealing algorithm are used to get the better training effect of the model. Finally, our model is migrated and deployed to the AI (Artificial Intelligence) edge system. Furthermore, we apply the scene segmentation method using the \u201cvirtual region\u201d to achieve accurate counting of the green citrus, thereby forming an embedded system of green citrus counting by edge computing. The results show that the mAP@.5 of the YOLOv5-CS model for green citrus was 98.23%, and the recall is 97.66%. The inference speed of YOLOv5-CS detecting a picture on the server is 0.017 s, and the inference speed on Nvidia Jetson Xavier NX is 0.037 s. The detection and counting frame rate of the AI edge system-side counting system is 28 FPS, which meets the counting requirements of green citrus.<\/jats:p>","DOI":"10.3390\/s22020576","type":"journal-article","created":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T23:17:07Z","timestamp":1642029427000},"page":"576","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":68,"title":["Green Citrus Detection and Counting in Orchards Based on YOLOv5-CS and AI Edge System"],"prefix":"10.3390","volume":"22","author":[{"given":"Shilei","family":"Lyu","sequence":"first","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"Pazhou Lab, Guangzhou 510330, China"},{"name":"Division of Citrus Machinery, China Agriculture Research System of MOF and MARA, Guangzhou 510642, China"}]},{"given":"Ruiyao","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"Pazhou Lab, Guangzhou 510330, China"}]},{"given":"Yawen","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"Pazhou Lab, Guangzhou 510330, China"}]},{"given":"Zhen","family":"Li","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"Pazhou Lab, Guangzhou 510330, China"},{"name":"Division of Citrus Machinery, China Agriculture Research System of MOF and MARA, Guangzhou 510642, China"}]},{"given":"Renjie","family":"Fan","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"Pazhou Lab, Guangzhou 510330, China"}]},{"given":"Siying","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.compag.2019.04.017","article-title":"Deep learning\u2014Method overview and review of use for fruit detection and yield estimation","volume":"162","author":"Koirala","year":"2019","journal-title":"Comput. 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