Department of Plant Pathology, North Dakota State University, Fargo, ND, USA.
Department of Plant Sciences and Landscape Architecture, University of Maryland, College Park, MD, USA.
Sci Rep. 2023 Mar 29;13(1):5133. doi: 10.1038/s41598-023-31942-9.
Plant diseases introduce significant yield and quality losses to the food production industry, worldwide. Early identification of an epidemic could lead to more effective management of the disease and potentially reduce yield loss and limit excessive input costs. Image processing and deep learning techniques have shown promising results in distinguishing healthy and infected plants at early stages. In this paper, the potential of four convolutional neural network models, including Xception, Residual Networks (ResNet)50, EfficientNetB4, and MobileNet, in the detection of rust disease on three commercially important field crops was evaluated. A dataset of 857 positive and 907 negative samples captured in the field and greenhouse environments were used. Training and testing of the algorithms were conducted using 70% and 30% of the data, respectively where the performance of different optimizers and learning rates were tested. Results indicated that EfficientNetB4 model was the most accurate model (average accuracy = 94.29%) in the disease detection followed by ResNet50 (average accuracy = 93.52%). Adaptive moment estimation (Adam) optimizer and learning rate of 0.001 outperformed all other corresponding hyperparameters. The findings from this study provide insights into the development of tools and gadgets useful in the automated detection of rust disease required for precision spraying.
植物病害给全球的食品生产行业带来了重大的产量和质量损失。早期发现疫情可能会导致更有效的疾病管理，并有可能减少产量损失和限制过度的投入成本。图像处理和深度学习技术在早期区分健康植物和感染植物方面显示出了有前景的结果。在本文中，评估了四种卷积神经网络模型（Xception、Residual Networks (ResNet)50、EfficientNetB4 和 MobileNet）在检测三种商业重要大田作物锈病中的潜力。该数据集包含了在田间和温室环境中捕获的 857 个阳性和 907 个阴性样本。使用 70%和 30%的数据分别对算法进行了训练和测试，测试了不同优化器和学习率的性能。结果表明，EfficientNetB4 模型在疾病检测方面最为准确（平均准确率为 94.29%），其次是 ResNet50（平均准确率为 93.52%）。自适应矩估计（Adam）优化器和学习率为 0.001 的表现优于所有其他对应的超参数。这项研究的结果为开发有用的工具和小工具提供了思路，这些工具可用于自动化检测锈病，从而实现精准喷洒。
