使用TomFormer早期准确检测番茄叶部病害
Early and Accurate Detection of Tomato Leaf Diseases Using TomFormer
AI总结：
本文提出TomFormer模型，融合视觉Transformer与CNN，用于番茄叶部病害的早期准确检测，在三个数据集上取得87%、81%、83%的mAP，优于现有方法，并计划应用于机器人实时诊断。
摘要：
番茄叶部病害对番茄种植者构成重大挑战，导致作物生产力大幅下降。及时准确地识别番茄叶部病害对于成功实施病害管理策略至关重要。本文介绍了一种基于Transformer的模型，称为TomFormer，用于番茄叶部病害检测。本文的主要贡献包括以下几点：首先，我们提出了一种新颖的番茄叶部病害检测方法，通过采用融合模型，结合了视觉Transformer和卷积神经网络。其次，我们旨在将所提出的方法应用于Hello Stretch机器人，以实现番茄叶部病害的实时诊断。第三，我们通过与YOLOS、DETR、ViT和Swin等模型进行比较来评估我们的方法，证明了其能够取得最先进的结果。为了进行实验，我们使用了三个番茄叶部病害数据集，即KUTomaDATA、PlantDoc和PlantVillage，其中KUTomaDATA是从阿联酋阿布扎比的一个温室中收集的。最后，我们对模型的性能进行了全面分析，并深入讨论了方法中固有的局限性。TomFormer在KUTomaDATA、PlantDoc和PlantVillage数据集上表现良好，平均精度（mAP）得分分别为87%、81%和83%。在mAP方面的比较结果表明，我们的方法具有鲁棒性、准确性、高效性和可扩展性。此外，它可以很容易地适应新的数据集。我们相信，我们的工作通过有效减轻作物损失和提高作物产量，具有显著影响番茄产业的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
