Peanut leaf disease identification with deep learning algorithms.

Abstract

Peanut is an essential food and oilseed crop. One of the most critical factors contributing to the low yield and destruction of peanut plant growth is leaf disease attack, which will directly reduce the yield and quality of peanut plants. The existing works have shortcomings such as strong subjectivity and insufficient generalization ability. So, we proposed a new deep learning model for peanut leaf disease identification. The proposed model is a combination of an improved X-ception, a parts-activated feature fusion module, and two attention-augmented branches. We obtained an accuracy of 99.69%, which was 9.67%-23.34% higher than those of Inception-V4, ResNet 34, and MobileNet-V3. Besides, supplementary experiments were performed to confirm the generality of the proposed model. The proposed model was applied to cucumber, apple, rice, corn, and wheat leaf disease identification, and yielded an average accuracy of 99.61%. The experimental results demonstrate that the proposed model can identify different crop leaf diseases, proving its feasibility and generalization. The proposed model has a positive significance for exploring other crop diseases' detection. Supplementary information The online version contains supplementary material available at 10.1007/s11032-023-01370-8.

Plant phenotyping relevance

植物葉の病害状態を画像から推定する深層学習手法の開発・比較が中心であり、植物フェノタイピング手法に該当する。

Code and data availability

The article describes a peanut leaf disease deep learning model and a self-collected 9420-image dataset, but provides no public dataset, code, model, or image deposit. Data availability states 'Not applicable,' and no author URLs or repository identifiers appear in the supplied blocks. The supplementary file is a DOCX

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