An Effective Image Classification Method for Plant Diseases with Improved Channel Attention Mechanism aECAnet Based on Deep Learning

Abstract

Since plant diseases occurring during the growth process are a significant factor leading to the decline in both yield and quality, the classification and detection of plant leaf diseases, followed by timely prevention and control measures, are crucial for safeguarding plant productivity and quality. As the traditional convolutional neural network structure cannot effectively recognize similar plant leaf diseases, in order to more accurately identify the diseases on plant leaves, this paper proposes an effective plant disease image recognition method aECA-ResNet34. This method is based on ResNet34, and in the first and the last layers of this network, respectively, we add this paper’s improved aECAnet with the symmetric structure. aECA-ResNet34 is compared with different plant disease classification models on the peanut dataset constructed in this paper and the open-source PlantVillage dataset. The experimental results show that the aECA-ResNet34 model proposed in this paper has higher accuracy, better performance, and better robustness. The results show that the aECA-ResNet34 model proposed in this paper is able to recognize diseases of multiple plant leaves very accurately.

Plant phenotyping relevance

植物葉の病害状態を画像から分類する深層学習手法を提案・比較しており、植物フェノタイピング手法が研究の中心である。

Code and data availability

The paper uses the public PlantVillage dataset (a cited third-party resource, not a paper-specific asset) and a self-collected peanut leaf disease dataset (1329 images), but no blocks contain any availability statement, deposit, or URL for the peanut dataset, the authors' code, trained models, or supplements. No paper-

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