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Identification of Soybean Leaf Diseases via Deep Learning
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Identification of Soybean Leaf Diseases via Deep Learning
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Journal Article
Identification of Soybean Leaf Diseases via Deep Learning
2019
Overview
We propose a novel approach for identifying soybean leaf diseases in the natural environment by convolutional neural network (CNN). AlexNet, GoogLeNet and ResNet were utilized for transfer learning. Firstly, 27 models were obtained by setting different batch sizes and the number of iterations. Then, the effects of CNN structure on identification performance were explored. The optimal model is based on ResNet and has the highest accuracy of 94.29%. In the parameter settings of the optimal network, the number of iterations and batch size are 1056 and 16, respectively, and the training depth is 140. Overall, the proposed method is effective for identifying soybean leaf diseases in the natural environment.
Publisher
Springer Nature B.V
Subject
