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.

该论文介绍了一种基于 Transformer 的模型 TomFormer 用于番茄叶病检测，并通过与其他模型的比较验证了其在番茄叶病检测中的优越表现，展示了其鲁棒性、准确性、效率和可扩展性以及其在农业领域中的潜在影响。

利用 TomFormer 早期和准确地检测番茄叶子疾病

Early and Accurate Detection of Tomato Leaf Diseases Using TomFormer

Due to the lack of more efficient diagnostic tools for monkeypox, its spread
remains unchecked, presenting a formidable challenge to global health. While
the high efficacy of deep learning models for monkeypox diagnosis has been
demonstrated in related studies, the overlook of inference speed, the parameter
size and diagnosis performance for early-stage monkeypox renders the models
inapplicable in real-world settings. To address these challenges, we proposed
an ultrafast and ultralight network named Fast-MpoxNet. Fast-MpoxNet possesses
only 0.27M parameters and can process input images at 68 frames per second
(FPS) on the CPU. To counteract the diagnostic performance limitation brought
about by the small model capacity, it integrates the attention-based feature
fusion module and the multiple auxiliary losses enhancement strategy for better
detecting subtle image changes and optimizing weights. Using transfer learning
and five-fold cross-validation, Fast-MpoxNet achieves 94.26% Accuracy on the
Mpox dataset. Notably, its recall for early-stage monkeypox achieves 93.65%. By
adopting data augmentation, our model's Accuracy rises to 98.40% and attains a
Practicality Score (A new metric for measuring model practicality in real-time
diagnosis application) of 0.80. We also developed an application system named
Mpox-AISM V2 for both personal computers and mobile phones. Mpox-AISM V2
features ultrafast responses, offline functionality, and easy deployment,
enabling accurate and real-time diagnosis for both the public and individuals
in various real-world settings, especially in populous settings during the
outbreak. Our work could potentially mitigate future monkeypox outbreak and
illuminate a fresh paradigm for developing real-time diagnostic tools in the
healthcare field.

通过开发一种名为 Fast-MpoxNet 的超快超轻网络，结合关注点特征融合模块和多辅助损失增强策略以优化模型能力，使用迁移学习和五折交叉验证，实现了 94.26% 的准确率和 0.80 的实用性分数，在猴痘早期病情诊断方面达到 93.65% 的召回率，并开发了一个名为 Mpox-AISM V2 的应用系统，能够在不同实际环境下为公众和个人提供准确而实时的诊断。