TSSC: a new deep learning model for accurate pea leaf disease identification.

Abstract

Problem Accurate diagnosis of plant diseases is crucial for ensuring crop yield and food safety. This study aims to explore a deep learning based intelligent recognition methods for plant leaf diseases to solve the automatic recognition problem of various pea leaf diseases. Methodology We propose a novel deep learning framework called TSSC. First, a three-neighbor channel attention is designed to promote the effectiveness of feature extraction. Second, a complementary squeeze and excitation mechanism is introduced to enhance the ability to extract key features. Finally, a split attention module is embedded to reduce model complexity. Results The experimental results demonstrate that the proposed model achieves an overall classification accuracy of 99.61% and outperforms other excellent deep learning models. Contribution The currently proposed system provides an effective solution for image recognition of complex plant diseases and has reference value for the development of mobile disease detection equipment.

Plant phenotyping relevance

エンドツーエンドの葉画像から植物病害を識別する新規深層学習モデルを開発・評価しており、植物の病害状態を直接推定する方法が中心である。

Code and data availability

The paper's pea leaf disease image dataset (7,750 laboratory-collected images) and TSSC model/code are not publicly deposited. The Data Availability Statement only offers the material via inquiry to the corresponding authors, so access requires contacting them.

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