Spectral image classification of asymptomatic peanut leaf diseases based on deep learning algorithms.

Abstract

Peanut leaf diseases have a major impact on peanut yield and quality. Timely, rapid, and accurate early diagnosis and control of peanut leaf diseases are key to ensuring high quality and yield of peanuts. This work focuses on the early diagnosis of peanut diseases and pests and conducts systematic research on the hardware system for imaging and spectral sensing of peanut plant leaves, as well as the software for deep learning classification algorithms. First, we designed a system that can separately obtain multispectral reflectance and fluorescence images and collect multispectral images of three asymptomatic peanut leaf diseases, including scab, scorch spot, and anthracnose. Second, we constructed a convolutional neural network to extract the basic features of spectral images. Third, an adaptive channel attention mechanism is introduced to update the weights of different channels. Fourth, a sparse second-order attention mechanism driving network is constructed to enhance the discriminative ability of deep feature information. Finally, the classification is completed utilizing the Softmax classifier. The experimental results demonstrate that the spectral image information improves the robustness of deep learning models to data transformation and achieves a high-precision classification score of 98.45% for asymptomatic peanut leaf diseases. Compared to traditional optical devices and software algorithms, the proposed multispectral imaging system and deep learning algorithm significantly improve detection ability and classification accuracy, which can assist botanists in making more accurate diagnoses of peanut leaf diseases.

Plant phenotyping relevance

ピーナッツ葉の病害状態を対象に、マルチスペクトル・蛍光画像取得システムと深層学習分類手法を開発・評価しており、植物病害表現型の取得・判定が中心的な技術貢献である。

Code and data availability

The supplied article blocks describe a custom multispectral imaging system and deep learning model for classifying asymptomatic peanut leaf diseases, but contain no data availability, code availability, or repository statements. The 9,665-image dataset, model code, and trained checkpoints are not linked to any public,

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