2023
DOI: 10.3389/fpls.2023.1128399
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Classification and localization of maize leaf spot disease based on weakly supervised learning

Abstract: Precisely discerning disease types and vulnerable areas is crucial in implementing effective monitoring of crop production. This forms the basis for generating targeted plant protection recommendations and automatic, precise applications. In this study, we constructed a dataset comprising six types of field maize leaf images and developed a framework for classifying and localizing maize leaf diseases. Our approach involved integrating lightweight convolutional neural networks with interpretable AI algorithms, … Show more

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Cited by 5 publications
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“…We used data augmentation techniques such as rotation, horizontal flip, zoom, and shear to diversify training images [ 23 ] and enhance the model’s performance [ 24 ]. Images are resized to a common size of 224x224 pixels and normalized in the range [0, 1] to scale pixel values [ 25 , 26 ].…”
Section: Methodsmentioning
confidence: 99%
“…We used data augmentation techniques such as rotation, horizontal flip, zoom, and shear to diversify training images [ 23 ] and enhance the model’s performance [ 24 ]. Images are resized to a common size of 224x224 pixels and normalized in the range [0, 1] to scale pixel values [ 25 , 26 ].…”
Section: Methodsmentioning
confidence: 99%