{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,10]],"date-time":"2026-06-10T05:42:11Z","timestamp":1781070131975,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2024,11,2]],"date-time":"2024-11-02T00:00:00Z","timestamp":1730505600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key R&amp;D Program of China","award":["2021ZD0110901"],"award-info":[{"award-number":["2021ZD0110901"]}]},{"name":"National Key R&amp;D Program of China","award":["CAAS-ASTIP-2024-AII"],"award-info":[{"award-number":["CAAS-ASTIP-2024-AII"]}]},{"name":"Science and Technology Innovation Program of AII-CAAS","award":["2021ZD0110901"],"award-info":[{"award-number":["2021ZD0110901"]}]},{"name":"Science and Technology Innovation Program of AII-CAAS","award":["CAAS-ASTIP-2024-AII"],"award-info":[{"award-number":["CAAS-ASTIP-2024-AII"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Technological advances have dramatically improved precision agriculture, and accurate crop classification is a key aspect of precision agriculture (PA). The flexibility and real-time nature of UAVs have led them to become an important tool for acquiring agricultural data and enabling precise crop classification. Currently, crop identification relies heavily on complex high-precision models that often struggle to provide real-time performance. Research on lightweight models specifically for crop classification is also limited. In this paper, we propose a crop classification method based on UAV visible-light images based on PP-LiteSeg, a lightweight model proposed by Baidu. To improve the accuracy, a pyramid pooling module is designed in this paper, which integrates adaptive mean pooling and CSPC (Convolutional Spatial Pyramid Pooling) techniques to handle high-resolution features. In addition, a sparse self-attention mechanism is employed to help the model pay more attention to locally important semantic regions in the image. The combination of adaptive average pooling and the sparse self-attention mechanism can better handle different levels of contextual information. To train the model, a new dataset based on UAV visible-light images including nine categories such as rice, soybean, red bean, wheat, corn, poplar, etc., with a time span of two years was created for accurate crop classification. The experimental results show that the improved model outperforms other models in terms of accuracy and prediction performance, with a MIoU (mean intersection ratio joint) of 94.79%, which is 2.79% better than the original model. Based on the UAV RGB images demonstrated in this paper, the improved model achieves a better balance between real-time performance and accuracy. In conclusion, the method effectively utilizes UAV RGB data and lightweight deep semantic segmentation models to provide valuable insights for crop classification and UAV field monitoring.<\/jats:p>","DOI":"10.3390\/rs16214099","type":"journal-article","created":{"date-parts":[[2024,11,4]],"date-time":"2024-11-04T09:52:54Z","timestamp":1730713974000},"page":"4099","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Crop Classification from Drone Imagery Based on Lightweight Semantic Segmentation Methods"],"prefix":"10.3390","volume":"16","author":[{"given":"Zuojun","family":"Zheng","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China"},{"name":"Institute of Agricultural Information, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianghao","family":"Yuan","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Hebei Agricultural University, Baoding 071001, China"},{"name":"Institute of Agricultural Information, Chinese Academy of Agricultural Sciences, Beijing 100081, China"},{"name":"Academy of National Food and Strategic Reserves Administration, Beijing 100039, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Yao","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Information, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongxun","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2621-9222","authenticated-orcid":false,"given":"Qingzhi","family":"Liu","sequence":"additional","affiliation":[{"name":"Information Technology Group, Wageningen University and Research, 6700 HB Wageningen, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2447-2905","authenticated-orcid":false,"given":"Leifeng","family":"Guo","sequence":"additional","affiliation":[{"name":"Institute of Agricultural Information, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"4322","DOI":"10.1109\/TII.2020.3003910","article-title":"From Industry 4.0 to Agriculture 4.0: Current Status, Enabling Technologies, and Research Challenges","volume":"17","author":"Liu","year":"2020","journal-title":"IEEE Trans. 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