Food security, which has currently attracted much attention, requires minimizing crop damage by timely detection of plant diseases. Therefore, the automatic identification and diagnosis of plant diseases are highly desired in agricultural information. In this paper, we propose a novel approach to identify plant diseases. The method is divided into two parts: starting with the enhancement of the artificial neural network, the extracted pixel values and feature values are input to the enhanced artificial neural network for the image segmentation; then, following the establishment of a CNN based model, the segmented images are input to the proposed CNN model for the image classification. The proposed approach shows an impressive performance in the experimental analyses. It achieved an average accuracy of 93.75% to identify the crop diseases under the complex background conditions, and the validation accuracy was, on average, 10% higher than that of the conventional method. Additionally, almost all the plant disease samples were correctly detected by the proposed approach, and thus the recall rate achieved 100%. The experimental finding presents a substantial performance relative to other state-of-the-art methods and demonstrates the efficiency and extensibility of the proposed approach.

粮食安全目前引起了广泛关注，它要求通过及时发现植物病害来最大程度地减少对作物的损害。因此，在农业信息中非常需要自动识别和诊断植物病害。在本文中，我们提出了一种识别植物病害的新方法。该方法分为两部分：从增强的人工神经网络开始，将提取的像素值和特征值输入到增强的人工神经网络中进行图像分割。然后，在基于CNN的模型建立之后，将分割后的图像输入到建议的CNN模型中进行图像分类。所提出的方法在实验分析中显示出令人印象深刻的性能。它的平均准确度为93。在复杂的背景条件下识别作物病害的识别率为75％，并且验证准确性平均比传统方法高10％。此外，通过该方法可以正确检测出几乎所有植物病害样品，因此召回率达到了100％。实验结果表明，相对于其他现有技术，该方法具有显着的性能，并证明了该方法的效率和可扩展性。