Deep learning algorithms have the advantages of clear structure and high accuracy in image recognition. Accurate identification of pests and diseases in crops can improve the pertinence of pest control in farmland, which is beneficial to agricultural production. This paper proposes a DCNN-G model based on deep learning and fusion of Google data analysis, using this model to train 640 data samples, and then using 5000 test samples for testing, selecting 80% as the training set and 20% as the test set, and compare the accuracy of the model with the conventional recognition model. Research results show that after degrading a quality level 1 image using the degradation parameters above, 9 quality level images are obtained. Use YOLO’s improved network, YOLO-V4, to test and validate images after quality level classification. Images of different quality levels, especially images of adjacent levels, are subjectively observed by human eyes, and it is difficult to distinguish the quality of the images. Using the algorithm model proposed in this article, the recognition accuracy is 95%, which is much higher than the basic 84% of the DCNN model. The quality level classification of crop disease and insect pest images can provide important prior information for the understanding of crop disease and insect pest images and can also provide a scientific basis for testing the imaging capabilities of sensors and objectively evaluating the image quality of crop diseases and pests. The use of convolutional neural networks to realize the classification of crop pest and disease image quality not only expands the application field of deep learning but also provides a new method for crop pest and disease image quality assessment.

中文翻译：

---

基于深度学习的农作物病虫害图像识别

深度学习算法具有结构清晰，图像识别精度高的优点。准确识别农作物病虫害，可以提高农田病虫害防治的针对性，有利于农业生产。本文提出了一种基于深度学习和Google数据分析融合的DCNN-G模型，利用该模型训练640个数据样本，然后使用5000个测试样本进行测试，选择80％作为训练集，选择20％作为测试进行设置，并将模型的准确性与常规识别模型进行比较。研究结果表明，使用上述降级参数对质量等级为1的图像进行降级后，可获得9个质量等级的图像。使用YOLO改进的网络YOLO-V4在质量等级分类后测试和验证图像。人眼主观地观察到不同质量水平的图像，尤其是相邻水平的图像，并且难以区分图像的质量。使用本文提出的算法模型，识别精度为95％，远高于DCNN模型的基础84％。农作物病虫害图像的质量等级分类可以为了解农作物病虫害图像提供重要的先验信息，也可以为测试传感器的成像能力，客观评价农作物病虫害图像质量提供科学依据。害虫。