Early and Accurate Detection of Tomato Leaf Diseases Using TomFormer

TL;DR

This paper introduces TomFormer, a transformer-based model that combines visual transformers and CNNs for real-time, accurate detection of tomato leaf diseases, outperforming existing models across multiple datasets.

Contribution

The paper presents a novel fusion model called TomFormer that integrates transformers and CNNs for improved tomato leaf disease detection and demonstrates its effectiveness on several datasets.

Findings

Achieved state-of-the-art mAP scores of 87%, 81%, and 83% on three datasets.

Demonstrated robustness, accuracy, efficiency, and scalability of the proposed model.

Validated the model's potential for real-time diagnosis on a robot platform.

Abstract

Tomato leaf diseases pose a significant challenge for tomato farmers, resulting in substantial reductions in crop productivity. The timely and precise identification of tomato leaf diseases is crucial for successfully implementing disease management strategies. This paper introduces a transformer-based model called TomFormer for the purpose of tomato leaf disease detection. The paper's primary contributions include the following: Firstly, we present a novel approach for detecting tomato leaf diseases by employing a fusion model that combines a visual transformer and a convolutional neural network. Secondly, we aim to apply our proposed methodology to the Hello Stretch robot to achieve real-time diagnosis of tomato leaf diseases. Thirdly, we assessed our method by comparing it to models like YOLOS, DETR, ViT, and Swin, demonstrating its ability to achieve state-of-the-art outcomes. For the purpose of the experiment, we used three datasets of tomato leaf diseases, namely KUTomaDATA, PlantDoc, and PlanVillage, where KUTomaDATA is being collected from a greenhouse in Abu Dhabi, UAE. Finally, we present a comprehensive analysis of the performance of our model and thoroughly discuss the limitations inherent in our approach. TomFormer performed well on the KUTomaDATA, PlantDoc, and PlantVillage datasets, with mean average accuracy (mAP) scores of 87%, 81%, and 83%, respectively. The comparative results in terms of mAP demonstrate that our method exhibits robustness, accuracy, efficiency, and scalability. Furthermore, it can be readily adapted to new datasets. We are confident that our work holds the potential to significantly influence the tomato industry by effectively mitigating crop losses and enhancing crop yields.