外文摘要：High-throughput phenotyping of grapevine leafroll disease (GLD) at the canopy scale helps develop fast and effective management in viticulture. However, detecting GLD efficiently in a vineyard is challenging owing to the limited adaptation of prior art. Therefore, we propose a novel convolutional neural network called GLDCNet to improve GLD recognition using unmanned aerial vehicle-based imagery. The effectiveness of the GLDCNet is attributed to the four new network designs used and is validated through ablation experiments. The GLDCNet achieves a classification accuracy of 99.57% using the RGB dataset and obtains more efficient and accurate results than nine other state-of-the-art methods. Furthermore, we systematically evaluated the impacts of image spatial resolution and vegetation indexes on the classification performance of the model. Experimental results suggest that improving image spatial resolution is more cost-effective than enhancing multispectral information for improving GLD recognition. Our proposed method offers a rapid, scalable, and accurate diagnostic protocol for detecting GLD in vineyards.
外文关键词：UAV;deep learning;vegetation index;multispectral;Spatial resolution;Grapevine leafroll disease
作者：Yang, Peng;Su, Jinya;Su, Baofeng;Zheng, Zhouzhou;Liu, Yixue;Liu, Dizhu;Song, Yuyang;Fang, Yulin
作者单位：Chinese Acad Agr Sci;Minist Agr & Rural Affairs;McGill Univ;Northwest A&F Univ;Southeast Univ;Shaanxi Key Lab Agr Informat Percept & Intelligen
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE
期刊影响因子：0.0
出版年份：2024
出版刊次：218
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台