A Recognition Method of Soybean Leaf Diseases Based on an Improved Deep Learning Model.

Abstract

Soybean is an important oil crop and plant protein source, and phenotypic traits' detection for soybean diseases, which seriously restrict yield and quality, is of great significance for soybean breeding, cultivation, and fine management. The recognition accuracy of traditional deep learning models is not high, and the chemical analysis operation process of soybean diseases is time-consuming. In addition, artificial observation and experience judgment are easily affected by subjective factors and difficult to guarantee the accuracy of the objective. Thus, a rapid identification method of soybean diseases was proposed based on a new residual attention network (RANet) model. First, soybean brown leaf spot, soybean frogeye leaf spot, and soybean phyllosticta leaf spot were used as research objects, the OTSU algorithm was adopted to remove the background from the original image. Then, the sample dataset of soybean disease images was expanded by image enhancement technology based on a single leaf image of soybean disease. In addition, a residual attention layer (RAL) was constructed using attention mechanisms and shortcut connections, which further embedded into the residual neural network 18 (ResNet18) model. Finally, a new model of RANet for recognition of soybean diseases was established based on attention mechanism and idea of residuals. The result showed that the average recognition accuracy of soybean leaf diseases was 98.49%, and the F1-value was 98.52 with recognition time of 0.0514 s, which realized an accurate, fast, and efficient recognition model for soybean leaf diseases.

Plant phenotyping relevance

大豆葉の病害状態を画像から認識する深層学習手法の開発が研究の中心であり、植物病害表現型の取得・推定に該当する。

Code and data availability

The supplied blocks describe a self-collected soybean disease image dataset (523 images from Heilongjiang Bayi Agricultural University fields) and a custom RANet model, but contain no public dataset deposit, no author code repository, and no data availability statement with a public URL. The only URL present is the DOI

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