A Deep Hybrid Convolutional Neural Network (CNN)–Transformer Approach for Early Detection of Tomato Leaf Diseases

Abstract

The early and effective diagnosis of tomato leaf diseases is very important to enhance crop yield and reduce economic loss in precision agriculture. The conventional image-based methods are typically based on single architecture model, which cannot capture fine-grained lesion details and global contextual patterns simultaneously in the real-field. To this end, we introduce a deep hybrid Convolutional Neural Network (CNN) –Transformer architecture by combining ConvNeXt Large (ConvNeXt-L) (as local feature extractor) and Swin Transformer (as global context encoder). The concatenated features vector is then fed to a shallow classifier to predict the disease. The model was tested on two datasets, namely a field dataset in agriculture areas from Madhya Pradesh (India) and a benchmark tomato leaf dataset. Experimental results revealed that the proposed scheme achieved accuracy of 92.83% on a primary dataset, and performance was significantly high with an accuracy of up to 95.65% in terms of generalization rate for computing technique models from various environmental conditions.

Plant phenotyping relevance

トマト葉の病害状態を画像から推定するCNN–Transformer手法を提案し、複数データセットで性能検証しており、植物フェノタイピング手法が中心である。

Code and data availability

The paper uses a public Tomato Leaves Dataset from GTS AI as its secondary/external validation dataset for tomato leaf disease classification. The primary field dataset from Madhya Pradesh is not stated as publicly available, and no author analysis code or trained model is reported as deposited.