{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T22:20:19Z","timestamp":1772230819966,"version":"3.50.1"},"reference-count":37,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T00:00:00Z","timestamp":1691971200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Laboratory of Lingnan Modern Agriculture Project","award":["NT2021009"],"award-info":[{"award-number":["NT2021009"]}]},{"name":"Laboratory of Lingnan Modern Agriculture Project","award":["2019B020214003"],"award-info":[{"award-number":["2019B020214003"]}]},{"name":"Laboratory of Lingnan Modern Agriculture Project","award":["D18019"],"award-info":[{"award-number":["D18019"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["NT2021009"],"award-info":[{"award-number":["NT2021009"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2019B020214003"],"award-info":[{"award-number":["2019B020214003"]}]},{"name":"Key-Area Research and Development Program of Guangdong Province","award":["D18019"],"award-info":[{"award-number":["D18019"]}]},{"name":"Top Talents Program for One Case One Discussion of Shandong Province, the 111 Project","award":["NT2021009"],"award-info":[{"award-number":["NT2021009"]}]},{"name":"Top Talents Program for One Case One Discussion of Shandong Province, the 111 Project","award":["2019B020214003"],"award-info":[{"award-number":["2019B020214003"]}]},{"name":"Top Talents Program for One Case One Discussion of Shandong Province, the 111 Project","award":["D18019"],"award-info":[{"award-number":["D18019"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The utilization of unmanned aerial vehicles (UAVs) for the precise and convenient detection of litchi fruits, in order to estimate yields and perform statistical analysis, holds significant value in the complex and variable litchi orchard environment. Currently, litchi yield estimation relies predominantly on manual rough counts, which often result in discrepancies between the estimated values and the actual production figures. This study proposes a large-scene and high-density litchi fruit recognition method based on the improved You Only Look Once version 5 (YOLOv5) model. The main objective is to enhance the accuracy and efficiency of yield estimation in natural orchards. First, the PANet in the original YOLOv5 model is replaced with the improved Bi-directional Feature Pyramid Network (BiFPN) to enhance the model\u2019s cross-scale feature fusion. Second, the P2 feature layer is fused into the BiFPN to enhance the learning capability of the model for high-resolution features. After that, the Normalized Gaussian Wasserstein Distance (NWD) metric is introduced into the regression loss function to enhance the learning ability of the model for litchi tiny targets. Finally, the Slicing Aided Hyper Inference (SAHI) is used to enhance the detection of tiny targets without increasing the model\u2019s parameters or computational memory. The experimental results show that the overall AP value of the improved YOLOv5 model has been effectively increased by 22%, compared to the original YOLOv5 model\u2019s AP value of 50.6%. Specifically, the APs value for detecting small targets has increased from 27.8% to 57.3%. The model size is only 3.6% larger than the original YOLOv5 model. Through ablation and comparative experiments, our method has successfully improved accuracy without compromising the model size and inference speed. Therefore, the proposed method in this paper holds practical applicability for detecting litchi fruits in orchards. It can serve as a valuable tool for providing guidance and suggestions for litchi yield estimation and subsequent harvesting processes. In future research, optimization can be continued for the small target detection problem, while it can be extended to study the small target tracking problem in dense scenarios, which is of great significance for litchi yield estimation.<\/jats:p>","DOI":"10.3390\/rs15164017","type":"journal-article","created":{"date-parts":[[2023,8,14]],"date-time":"2023-08-14T10:40:31Z","timestamp":1692009631000},"page":"4017","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Precision Detection of Dense Litchi Fruit in UAV Images Based on Improved YOLOv5 Model"],"prefix":"10.3390","volume":"15","author":[{"given":"Zhangjun","family":"Xiong","sequence":"first","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology, Guangzhou 510642, China"}]},{"given":"Lele","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology, Guangzhou 510642, China"}]},{"given":"Yingjie","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology, Guangzhou 510642, China"}]},{"given":"Yubin","family":"Lan","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering (College of Artificial Intelligence), South China Agricultural University, Guangzhou 510642, China"},{"name":"National Center for International Collaboration Research on Precision Agricultural Aviation Pesticides Spraying Technology, Guangzhou 510642, China"},{"name":"Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China"},{"name":"School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo 510642, China"},{"name":"Department of Biological and Agricultural Engineering, Texas A&M University, College Station, TX 77844, USA"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,14]]},"reference":[{"key":"ref_1","first-page":"132","article-title":"Development Status, Trend and Suggestion of Litchi Industry in Mainland China","volume":"46","author":"Houbin","year":"2019","journal-title":"Guangdong Agric. 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