论文标题
基于移动的香蕉疾病检测的深度学习模型
Mobile-Based Deep Learning Models for Banana Diseases Detection
论文作者
论文摘要
坦桑尼亚的小农户因缺乏早期发现香蕉疾病的工具而受到挑战。这项研究旨在开发一种移动应用程序，用于使用深度学习的早期检测1 fusarium wilt Race 1和黑色Sigatoka香蕉疾病。我们使用了3000个香蕉叶图像的数据集。我们在RESNET152和InceptionV3卷积神经网络体系结构上预先培训了我们的模型。 RESNET152的精度为99.2％，而InceptionV3的精度为95.41％。在使用Android手机部署时，我们选择了InceptionV3，因为与RESNET152相比，它的内存需求较低。实际环境上的移动应用检测到了两种疾病，其置信水平为99％的捕获叶子区域。该结果表明，使用一种工具来早日发现疾病的工具，有可能提高小农户的香蕉产量。
Smallholder farmers in Tanzania are challenged on the lack of tools for early detection of banana diseases. This study aimed at developing a mobile application for early detection of Fusarium wilt race 1 and black Sigatoka banana diseases using deep learning. We used a dataset of 3000 banana leaves images. We pre-trained our model on Resnet152 and Inceptionv3 Convolution Neural Network architectures. The Resnet152 achieved an accuracy of 99.2% and Inceptionv3 an accuracy of 95.41%. On deployment using Android mobile phones, we chose Inceptionv3 since it has lower memory requirements compared to Resnet152. The mobile application on real environment detected the two diseases with a confidence level of 99% of the captured leaf area. This result indicates the potential in improving the yield of bananas by smallholder farmers using a tool for early detection of diseases.
