数字农科院2.0

Comprehensive analysis of hyperspectral features for monitoring canopy maize leaf spot disease

文献类型： 外文期刊

作者： Yali Bai;Chenwei Nie;Xun Yu;Mingyue Gou;Shuaibing Liu;Yanqin Zhu;Tiantian Jiang;Xiao Jia;Yadong Liu;Fei Nan;Liming Li;Bedir Tekinerdogan;Yang Song;Qingzhi Liu;Xiuliang Jin

作者机构：

关键词： Canopy;Hyperspectral features;Leaf spot;Reinforcement learning;Unmanned aerial vehicle (UAV)

期刊名称： Computers and Electronics in Agriculture

ISSN： 0168-1699

年卷期： 2024 年 225 卷

页码：

收录情况： SCIE（2024版） ; ; EI（2024版）

摘要： Accurate quantification of hyperspectral features altered by plant disease infection is pivotal for effective disease management. However, the sensitivity of hyperspectral features to plant disease progression remains elusive, primarily because these features are often influenced by plant growth and environmental factors in addition to the specific disease. This study explores the sensitivity of biophysical and spectral features as indicators for maize adaptation to leaf spot disease. Using high-resolution UAV hyperspectral imaging, we captured maize adaptation dynamics over 30 days post-infection. We evaluated the sensitivity and importance of hyperspectral features for disease monitoring, including biophysical parameters retrieved by the PROSAIL model, and spectral features, including spectral reflectance, vegetation indices (VIs), and wavelet features (WFs). Our findings reveal that WFs first indicate disease response as early as 6 days after infection (DAI), followed by VIs at DAI 8, and variations in chlorophyll content (Cab) become apparent by DAI 10. The Cab, plant senescence reflectance index (PSRI), and normalized photosynthetic reflectance index (NPRI) are determined to be important features at the early stage of the disease. Our experimental results show that the different feature sets are complementary at the early and severe stages of the disease. Our classification models integrating Cab, VIs, and WFs showed higher overall accuracy than models using only spectral features or VIs, with a maximum improvement of 9.36 %. However, these feature sets are redundant in the mild and initial severe disease stages, where models using only spectral features achieve the highest overall accuracy of 86.21 %. This study underscores the novel insights by offering an understanding of plant responses to disease infection and enhancing early detection strategies.

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