  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.
