Monitoring the leaf damage by the rice leafroller with deep learning and ultra-light UAV,Pest Management Science

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Monitoring the leaf damage by the rice leafroller with deep learning and ultra-light UAV
Pest Management Science ( IF 3.8 ) Pub Date : 2024-09-12 , DOI: 10.1002/ps.8401
Lang Xia _{1,

2} , Ruirui Zhang _{1,

2} , Liping Chen _{3,

4} , Longlong Li _{3,

4} , Tongchuan Yi _{3,

4} , Meixiang Chen _{1,

2}

Affiliation

Rice leafroller is a serious threat to the production of rice. Monitoring the damage caused by rice leafroller is essential for effective pest management. Owing to limitations in collecting decent quality images and high-performing identification methods to recognize the damage, studies recommending fast and accurate identification of rice leafroller damage are rare. In this study, we employed an ultra-lightweight unmanned aerial vehicle (UAV) to eliminate the influence of the downwash flow field and obtain very high-resolution images of the damaged areas of the rice leafroller. We used deep learning technology and the segmentation model, Attention U-Net, to recognize the damaged area by the rice leafroller. Further, a method is presented to count the damaged patches from the segmented area.

中文翻译：

利用深度学习和超轻型无人机监测稻卷叶机叶片损伤

稻卷叶蟾对水稻生产构成严重威胁。监测稻卷叶蟀造成的损害对于有效的害虫管理至关重要。由于收集质量良好的图像和高性能识别方法来识别损伤的局限性，建议快速准确地识别水稻卷叶蟾损伤的研究很少见。在这项研究中，我们采用了超轻型无人机（UAV）来消除下洗流场的影响，并获得水稻卷叶机受损区域的非常高分辨率的图像。我们使用深度学习技术和分割模型 Attention U-Net 来识别水稻卷叶机的受损区域。此外，提出了一种从分割区域计数受损斑块的方法。

更新日期：2024-09-12

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