Askale Getnet Tigabie, Yibel Achenef Behulu, Taye Belayneh Matebie, Wubneh Gashaw Desalegn
College of Informatics, University of Gondar, Gondar, Ethiopia.
Plant Methods. 2025 May 29;21(1):72. doi: 10.1186/s13007-025-01386-5.
Maize is the most produced crop in the world, exceeding wheat and rice production. However, its yield is often affected by various leaf diseases. Early identification of maize leaf disease through easily accessible tool is required to increase the yield of maize. Recently, researchers have attempted to detect and classify maize leaf diseases using Deep Learning algorithms. However, to the best of the researcher's knowledge, nearly all the studies are concentrated on developing an offline model that can detect maize diseases. But, those models are not easily accessible to individuals and don't provide immediate feedback and monitoring. Thus, in this study, we developed a novel real-time, user-friendly maize leaf disease detection and classification mobile application. The VGG16, AlexNet, and ResNet50 models were implemented and compared their performance on maize disease detection and classification. A total of 4188 images of blight, common_rust, grey_leaf_spot, and healthy were used to train each model. Data augmentation techniques were applied to the dataset to increase the size of the dataset, which can also reduce model overfitting. Weighted cross-entropy loss was also employed to mitigate class-imbalance problems. After training, VGG16 achieved 95% of testing accuracy, AlexNet achieved 91%, and ResNet50 achieved 72% of testing accuracy. The VGG16 model outperformed the other models in terms of accuracy. Consequently, we deployed the VGG16 model into a mobile application to provide real-time disease detection and classification tool for farmers, extension officers, agribusiness managers, and policy-makers. The developed application will enhance early disease detection, decision making, and contribute to better crop management and food security.
玉米是世界上产量最高的作物，超过了小麦和水稻的产量。然而，其产量常常受到各种叶部病害的影响。需要通过易于获取的工具对玉米叶部病害进行早期识别，以提高玉米产量。最近，研究人员尝试使用深度学习算法来检测和分类玉米叶部病害。然而，据研究人员所知，几乎所有的研究都集中在开发一个能够检测玉米病害的离线模型上。但是，这些模型个人难以获取，也不提供即时反馈和监测。因此，在本研究中，我们开发了一款新颖的实时、用户友好的玉米叶部病害检测与分类移动应用程序。实现了VGG16、AlexNet和ResNet50模型，并比较了它们在玉米病害检测和分类方面的性能。总共使用了4188张枯萎病、普通锈病、灰斑病和健康状态的图像来训练每个模型。对数据集应用了数据增强技术以增加数据集的大小，这也可以减少模型过拟合。还采用了加权交叉熵损失来缓解类别不平衡问题。训练后，VGG16的测试准确率达到95%，AlexNet达到91%，ResNet50达到72%。在准确率方面，VGG16模型优于其他模型。因此，我们将VGG16模型部署到一个移动应用程序中，为农民、推广人员、农业企业经理和政策制定者提供实时病害检测和分类工具。所开发的应用程序将加强病害早期检测、决策制定，并有助于更好地进行作物管理和粮食安全。
