Automated Diagnosis of Tomato Leaf Diseases with Attention-Enhanced Feature Aggregation

Abstract

Tomato crop yield can be enhanced by using advanced agricultural technologies when plant leaf diseases are detected early. This article proposes a new tomato disease classification model, called Multiscale Parallel Feature Aggregation Network with Attention Fusion (MPFAN-AF), that classifies diseases from leaf images. This model comprises parallel convolutional branches that extract multiscale features to obtain rich data, which are then fused through an attention-based fusion module that performs global average pooling and weights the channels. This model enables the network to optimize patterns associated with the disease while disregarding the diseased area’s background noise. Generalization performance is improved with dropout and L2 weight decay. The refined features are passed through a lightweight multi-layer perceptron for classification. Evaluated on a benchmark tomato leaf disease dataset, MPFAN-AF outperforms conventional Convolutional Neural Networks (CNNs) and existing attention-based models across accuracy, precision, recall, and F1-score. Overall, MPFAN-AF delivers an efficient, accurate, and interpretable solution for automated disease diagnosis in precision agriculture.