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Plant Sci."],"abstract":"<jats:p>To address the challenge of difficult small target recognition in the early detection of Pine Wilt Disease (PWD), this study proposes an efficient Unmanned Aerial Vehicle remote sensing detection model named ESE-PWDNet (Efficient Small-scale Early PWD Detection Network). Using a DJI Air3 UAV platform, a multi-temporal and multi-view high-resolution dataset of early-stage PWD was independently constructed in the Tangshan Forest Area, Jiangning District, Nanjing City, Jiangsu Province. Based on this dataset, a key module\u2014the Efficient Visual Linear Unit (EFVLU)\u2014was designed. Serving as the foundational building block of ESE-PWDNet, the EFVLU combines with convolutional modules (Conv) to form the backbone network, which efficiently captures global dependencies and improves the detection of small targets through global context while reducing computational complexity. Furthermore, inspired by the PANet architecture and utilizing the Attention State Space Block (ASSB), a novel neck network was designed to empower the model with efficient high-resolution image processing capabilities while maintaining high computational efficiency. In the prediction head, the introduction of the Efficient Multi-scale Attention (EMA) mechanism and the Lightweight Shared Detail Enhanced Convolutional Detection Head (LSDECD) comprehensively enhances the model\u2019s perception and localization capabilities for small targets with almost no additional inference computational cost. Experiments on the constructed multi-environment early PWD dataset demonstrate that ESE-PWDNet significantly improves the recognition performance of tiny disease targets in complex scenes. The final model maintains high inference efficiency, achieving a Precision (P) of 75.9% and a Recall (R) of 75.1%, with a low computational complexity of 6.5 GFLOPs and 2.6M parameters. Its comprehensive performance outperforms mainstream comparative models. This research provides a reliable technical solution and data foundation for the early and precise UAV remote sensing monitoring of forestry pests.<\/jats:p>","DOI":"10.3389\/fpls.2026.1841429","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T06:05:42Z","timestamp":1780898742000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["ESE-PWDNet: an efficient early-stage pine wilt disease detection network"],"prefix":"10.3389","volume":"17","author":[{"given":"Zhemin","family":"Ma","sequence":"first","affiliation":[{"name":"College of Information Science and Technology, Nanjing Forestry University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fang","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Electronic Engineering, Nanjing XiaoZhuang University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haifeng","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Nanjing Forestry University","place":["Nanjing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,8]]},"reference":[{"key":"B1","first-page":"764","article-title":"Deformable convolutional networks","volume-title":"16th IEEE international conference on computer vision (ICCV)","author":"Dai","year":"2017"},{"key":"B2","volume-title":"Transformers are SSMs: Generalized models and efficient algorithms through structured state space duality","author":"Dao","year":"2024"},{"key":"B3","doi-asserted-by":"publisher","first-page":"43","DOI":"10.3390\/rs13142837","article-title":"Deep learning in forestry using UAV-acquired RGB data: A practical review","volume":"13","author":"Diez","year":"2021","journal-title":"Remote Sens."},{"key":"B4","first-page":"13733","article-title":"RepVGG: Making VGG-style convnets great again","author":"Ding","year":"2021"},{"key":"B5","volume-title":"An image is worth 16x16 words: Transformers for image recognition at scale","author":"Dosovitskiy","year":"2020"},{"key":"B6","article-title":"Hyper-YOLO: When visual object detection meets hypergraph computation","volume":"47","author":"Feng","year":"2024","journal-title":"IEEE Transactions on Pattern Anal. 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