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Machine learning-based spectral and spatial analysis of hyper-and multi-spectral leaf images for dutch elm disease detection and resistance screening

引用

Artificial Intelligence in Agriculture 2023年第4期10卷 26-34页

作者： Xing Wei Jinnuo Zhang Anna O.Conrad Charles E.Flower Cornelia C.Pinchot Nancy Hayes-Plazolles Ziling Chen Zhihang Song Songlin Fei Jian Jin Department of Agricultural and Biological Engineering Purdue UniversityWest LafayetteIN 47907United States USDA Forest Service Northern Research StationHardwood Tree Improvement and Regeneration CenterWest LafayetteIN 47906United States USDA Forest Service Northern Research StationDelawareOH 43015United States Department of Forestry and Natural Resources Purdue UniversityWest LafayetteIN 47907United States

diseases caused by invasive pathogens are an increasing threat to forest health,and early and accurate disease detection is essential for timely and precision forest *** recent technological advancements in spectral imaging and artificial intelligence have opened up new possibilities for plant disease detection in both crops and *** this study,dutch elm disease(DED;caused by Ophiostoma novo-ulmi,)and American elm(Ulmus americana)was used as example pathosystem to evaluate the accuracy of two in-house developed high-precision portable hyper-and multi-spectral leaf imagers combined with machine learning as new tools for forest disease ***-and multi-spectral images were collected from leaves of American elm geno-types with varied disease susceptibilities after mock-inoculation and inoculation with ***-ulmi under green-house *** traditional machine learning and state-of-art deep learning models were built upon derived spectra and directly upon spectral image *** learning models that incorporate both spectral and spatial features of high-resolution spectral leaf images have better performance than traditional machine learning models built upon spectral features alone in detecting *** and symptomatic spots on the leaves were highlighted in the deep learning model as important spatial features to distinguish leaves from inoculated and mock-inoculated *** addition,spectral and spatial feature patterns identified in the machine learning-based models were found relative to the DED susceptibility of elm *** further studies are needed to assess applications in other pathosystems,hyper-and multi-spectral leaf imagers combined with machine learning show potential as new tools for disease phenotyping in trees.

关键词： American elm dutch elm disease Hyperspectral imaging Multispectral imaging Support vector machine Convolution neural network disease phenotyping Digital forestry

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