{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T05:20:31Z","timestamp":1763011231916,"version":"3.45.0"},"reference-count":36,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,6]],"date-time":"2025-11-06T00:00:00Z","timestamp":1762387200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Plant Sci."],"abstract":"<jats:p>\n                    As an invasive species in China,\n                    <jats:italic>Pomacea canaliculata<\/jats:italic>\n                    severely impacts crop quality and yield, necessitating effective monitoring for food security. To address the challenges in detecting its eggs in paddy fields\u2014including feature contamination, stem and leaf occlusion, and dense targets\u2014we propose an enhanced YOLOv8n-based algorithm. The method introduces omni-dimensional dynamic convolution (ODConv) in the backbone network to improve target feature extraction, constructs a Slim-neck structure to optimize feature processing efficiency, and designs a receptive-field attention head (RFAHead) for detection refinement. Experimental results demonstrate that the improved model achieves 3.3% and 4.2% higher mAP@0.5 and mAP@0.5:0.95 than the original YOLOv8. It outperforms Faster R-CNN, YOLOv3-tiny, YOLOv5, YOLOv6, YOLOv7-tiny, YOLOv9-t, YOLOv10n, and YOLOv11n by 18.2%, 12.4%, 5.2%, 10.8%, 11.6% 5.0%, 3.8%, and 3.4% in mAP@0.5 and 20.6%, 17.5%, 8.1%, 15.6%, 16.1%, 7.0%, 7.7%, and 6.5% in mAP@0.5:0.95, respectively. Visual analysis confirms enhanced recognition of small and occluded targets through improved feature learning. This model enables accurate and rapid detection of\n                    <jats:italic>Pomacea<\/jats:italic>\n                    eggs in rice fields, offering technical support for invasive species control.\n                  <\/jats:p>","DOI":"10.3389\/fpls.2025.1683763","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T05:17:14Z","timestamp":1763011034000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Object detection algorithm for eggs of Pomacea canaliculata in a paddy field environment"],"prefix":"10.3389","volume":"16","author":[{"given":"Guang Qi","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jing","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui Ning","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dian","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gang","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,11,13]]},"reference":[{"key":"B1","volume-title":"Yolo v3-tiny: object detection and recognition using one stage improved model","author":"Adarsh","year":"2020"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.13195\/j.kzyjc.2019.1319","article-title":"Object detection via learning occluded features based on generative adversarial networks","volume":"36","author":"An","year":"2021","journal-title":"Control Decision"},{"key":"B3","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-031-46311-2_5","article-title":"Tiny-YOLOv7: Tiny Object Detection Model for Drone Imagery","volume-title":"Image and Graphics. 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