PDF(Pubmed)
Abstract：
Crop diseases can significantly affect various aspects of crop cultivation, including crop yield, quality, production costs, and crop loss. The utilization of modern technologies such as image analysis via machine learning techniques enables early and precise detection of crop diseases, hence empowering farmers to effectively manage and avoid the occurrence of crop diseases. The proposed methodology involves the use of modified MobileNetV3Large model deployed on edge device for real-time monitoring of grape leaf disease while reducing computational memory demands and ensuring satisfactory classification performance. To enhance applicability of MobileNetV3Large, custom layers consisting of two dense layers were added, each followed by a dropout layer, helped mitigate overfitting and ensured that the model remains efficient. Comparisons among other models showed that the proposed model outperformed those with an average train and test accuracy of 99.66% and 99.42%, with a precision, recall, and F1 score of approximately 99.42%. The model was deployed on an edge device (Nvidia Jetson Nano) using a custom developed GUI app and predicted from both saved and real-time data with high confidence values. Grad-CAM visualization was used to identify and represent image areas that affect the convolutional neural network (CNN) classification decision-making process with high accuracy. This research contributes to the development of plant disease classification technologies for edge devices, which have the potential to enhance the ability of autonomous farming for farmers, agronomists, and researchers to monitor and mitigate plant diseases efficiently and effectively, with a positive impact on global food security.
摘要：
作物病害可以显著影响作物种植的各个方面，包括作物产量，质量,生产成本，和作物损失。利用现代技术，如通过机器学习技术进行图像分析，可以早期和精确地检测作物病害，从而使农民能够有效地管理和避免作物病害的发生。所提出的方法涉及使用部署在边缘设备上的改进的MobileNetV3Large模型来实时监测葡萄叶病，同时减少计算内存需求并确保令人满意的分类性能。为了增强MobileNetV3Large的适用性，添加了由两个致密层组成的定制层，每个后面跟着一个dropout层，有助于减轻过拟合，并确保模型保持有效。其他模型之间的比较表明，所提出的模型优于那些平均列车和测试精度分别为99.66％和99.42％，精确地，召回，F1评分约为99.42%。该模型使用自定义开发的GUI应用程序部署在边缘设备（NvidiaJetsonNano）上，并从具有高置信度值的保存和实时数据进行预测。Grad-CAM可视化用于识别和表示影响卷积神经网络（CNN）分类决策过程的图像区域，具有很高的准确性。这项研究有助于边缘设备植物病害分类技术的发展，有可能增强农民自主耕作的能力，农学家,和研究人员有效地监测和减轻植物病害，对全球粮食安全产生积极影响。
