Field pea leaf disease classification using a deep learning approach.

Abstract

Field peas are grown by smallholder farmers in Ethiopia for food, fodder, income, and soil fertility. However, leaf diseases such as ascochyta blight, powdery mildew, and leaf spots affect the quantity and quality of this crop as well as crop growth. Experts use visual observation to detect field pea disease. However, this approach is expensive, labor-intensive, and imprecise. Therefore, in this study, we presented a transfer learning approach for the automatic diagnosis of field pea leaf diseases. We classified three field pea leaf diseases: Ascochyta blight, leaf spot, and powdery mildew. A softmax classifier was used to classify the diseases. A total of 1600 images of both healthy and diseased leaves were used to train, validate, and test the pretrained models. According to the experimental results, DenseNet121 achieved 99.73% training accuracy, 99.16% validation accuracy, and 98.33% testing accuracy after 100 epochs. we expect that this research work will offer various benefits for farmers and farm experts. It reduced the cost and time needed for the detection and classification of field pea leaf disease. Thus, a fast, automated, less costly, and accurate detection method is necessary to overcome the detection problem.

Plant phenotyping relevance

圃場エンドウ葉の病害状態を画像から自動分類する深層学習手法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 paper's field pea leaf image dataset (1600 images from Kulumsa Agricultural Research Centre) is provided as supplementary S1 Dataset (ZIP), but access is stated to be by request from the corresponding author, not publicly deposited. No author analysis code, trained model checkpoints, or public repository URL is给定.

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