Research on cassava disease classification using the multi-scale fusion model based on EfficientNet and attention mechanism.

Abstract

Cassava disease is one of the leading causes to the serious decline of cassava yield. Because it is difficult to identify the characteristics of cassava disease, if not professional cassava growers, it will be prone to misjudgment. In order to strengthen the judgment of cassava diseases, the identification characteristics of cassava diseases such as different color of cassava leaf disease spots, abnormal leaf shape and disease spot area were studied. In this paper, deep convolutional neural network was used to classify cassava leaf diseases, and image classification technology was used to recognize and classify cassava leaf diseases. A lightweight module Multi-scale fusion model (MSFM) based on attention mechanism was proposed to extract disease features of cassava leaves to enhance the classification of disease features. The resulting feature map contained key disease identification information. The study used 22,000 cassava disease leaf images as a data set, including four different cassava leaf disease categories and healthy cassava leaves. The experimental results show that the cassava leaf disease classification model based on multi-scale fusion Convolutional Neural Network (CNN) improves EfficientNet compared with the original model, with the average recognition rate increased by nearly 4% and the average recognition rate up to 88.1%. It provides theoretical support and practical tools for the recognition and early diagnosis of plant disease leaves.

Plant phenotyping relevance

キャッサバ葉の病斑・形状・面積という植物状態を画像から分類する深層学習手法を提案・評価しており、病害表現型の取得・推定が中心である。

Code and data availability

The paper analyzes the public Kaggle Cassava Leaf Disease Classification dataset (22,000 images, four disease classes plus healthy), explicitly linked in its data availability statement. No author code or model checkpoints are shared.