【摘要】 目前,典型的一些农作物叶病害诊断主要依靠人工,但该方式耗时费力。针对大豆、棉花、水稻、小麦和玉米5类典型农作物的常见叶病害诊断问题,提出了一种基于卷积神经网络的典型农作物叶病害识别方法。从Plantvillage数据库以及其他网站收集典型农作物的叶病害图像,并对这些图像进行预处理,构建了含12 836张的数据集。参照AlexNet框架,构建8层卷积神经网络,采用迁移学习训练网络,最后通过测试集验证网络的识别准确率和损失值。分析不同的卷积神经网络的性能,实验结果表明:本算法对典型农作物的叶病害有良好的识别效果;迁移学习模式下,学习率为0.001时本算法在训练集的识别准确率约为99.47%,在测试集的识别准确率约为96.18%。更多还原

【Abstract】 At present, the identification and diagnosis of some typical crop leaf diseases mainly rely on artificial method, which is time-consuming and laborious. Aimed at the diagnosis of common leaf diseases of five typical crops of soybean, cotton, rice, wheat and maize, a recognition method of typical leaf diseases of crops based on convolution neural network was proposed. Leaf disease images of typical crops were collected from the Plantvillage database and some other sites, and these images were pretreated to build a database of 12 836 sheets. Referring to AlexNet framework, an eight-layer convolutional neural network was constructed, and the transfer learning training network was adopted. Finally, the recognition accuracy and loss value of the network were verified by the test set.The performance of different convolutional neural networks was analyzed. The experimental results showed that the algorithm performed well in identifying typical crop leaf diseases. Under the transfer learning mode, with the learning rate of 0.001, the recognition accuracy of the algorithm in the training set was about 99.47%, and about 96.18%in the test set.更多还原