{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T20:32:07Z","timestamp":1768595527655,"version":"3.49.0"},"reference-count":39,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T00:00:00Z","timestamp":1722384000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"2024 Basic and Applied Research Project of Guangzhou Science and Technology Plan","award":["2024A04J4140"],"award-info":[{"award-number":["2024A04J4140"]}]},{"name":"2024 Basic and Applied Research Project of Guangzhou Science and Technology Plan","award":["SKLRS-2024-KF-08"],"award-info":[{"award-number":["SKLRS-2024-KF-08"]}]},{"name":"State Key Laboratory of Robotics and Systems (HIT)","award":["2024A04J4140"],"award-info":[{"award-number":["2024A04J4140"]}]},{"name":"State Key Laboratory of Robotics and Systems (HIT)","award":["SKLRS-2024-KF-08"],"award-info":[{"award-number":["SKLRS-2024-KF-08"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-complexity network models are challenging to execute on agricultural robots with limited computing capabilities in a large-scale pineapple planting environment in real time. Traditional module replacement often struggles to reduce model complexity while maintaining stable network accuracy effectively. This paper investigates a pineapple detection framework with a YOLOv7-tiny model improved via pruning and a lightweight backbone sub-network (the RGDP-YOLOv7-tiny model). The ReXNet network is designed to significantly reduce the number of parameters in the YOLOv7-tiny backbone network layer during the group-level pruning process. Meanwhile, to enhance the efficacy of the lightweight network, a GSConv network has been developed and integrated into the neck network, to further diminish the number of parameters. In addition, the detection network incorporates a decoupled head network aimed at separating the tasks of classification and localization, which can enhance the model\u2019s convergence speed. The experimental results indicate that the network before pruning optimization achieved an improvement of 3.0% and 2.2%, in terms of mean average precision and F1 score, respectively. After pruning optimization, the RGDP-YOLOv7-tiny network was compressed to just 2.27 M in parameter count, 4.5 \u00d7 109 in computational complexity, and 5.0MB in model size, which were 37.8%, 34.1%, and 40.7% of the original YOLOv7-tiny network, respectively. Concurrently, the mean average precision and F1 score reached 87.9% and 87.4%, respectively, with increases of 0.8% and 1.3%. Ultimately, the model\u2019s generalization performance was validated through heatmap visualization experiments. Overall, the proposed pineapple object detection framework can effectively enhance detection accuracy. In a large-scale fruit cultivation environment, especially under the constraints of hardware limitations and limited computational power in the real-time detection processes of agricultural robots, it facilitates the practical application of artificial intelligence algorithms in agricultural engineering.<\/jats:p>","DOI":"10.3390\/rs16152805","type":"journal-article","created":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T10:34:43Z","timestamp":1722422083000},"page":"2805","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Pineapple Detection with YOLOv7-Tiny Network Model Improved via Pruning and a Lightweight Backbone Sub-Network"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4946-4434","authenticated-orcid":false,"given":"Jiehao","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, China"},{"name":"Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, College of Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaowen","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, College of Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenglin","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, College of Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qunfei","family":"Luo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, College of Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3223-6453","authenticated-orcid":false,"given":"Jiahuan","family":"Lu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, College of Engineering, South China Agricultural University, Guangzhou 510642, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,7,31]]},"reference":[{"key":"ref_1","first-page":"65","article-title":"Current status of pineapple production and research in China","volume":"10","author":"Liu","year":"2010","journal-title":"Guangdong Agric. 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