论文标题
图像的植物疾病检测
Plant Disease Detection from Images
论文作者
论文摘要
植物疾病的检测是一个巨大的问题，通常需要专业的帮助来检测疾病。这项研究重点是创建一个深度学习模型，该模型检测出从植物叶片图像中影响植物的疾病类型。深度学习是在卷积神经网络的帮助下通过进行转移学习来完成的。该模型是使用转移学习创建的，并通过Resnet 34和Resnet 50进行了实验，以证明判别性学习可以提供更好的结果。此方法为所使用的数据集实现了最新的结果。主要目标是降低专业帮助，以检测植物疾病，并使尽可能多的人可以使用该模型。
Plant disease detection is a huge problem and often require professional help to detect the disease. This research focuses on creating a deep learning model that detects the type of disease that affected the plant from the images of the leaves of the plants. The deep learning is done with the help of Convolutional Neural Network by performing transfer learning. The model is created using transfer learning and is experimented with both resnet 34 and resnet 50 to demonstrate that discriminative learning gives better results. This method achieved state of art results for the dataset used. The main goal is to lower the professional help to detect the plant diseases and make this model accessible to as many people as possible.
