A Potato Leaf Disease and Pests Identification Algorithm Based on Deep Learning

Abstract

China boasts a diverse range of crops. The yield and the quality of crops exert an influence on economic development and farmers’ income. The healthy and stable development of agriculture has consistently been a goal pursued by the country. Therefore, it is of great significance to rapidly and precisely identify the types of crop diseases and pests and offer timely and accurate prevention and control measures. The recognition of traditional crop pest and disease images mainly relies on agricultural experts to recognize the color and shape of disease images. This approach is not only labor-intensive and inefficient but also subjective, and it can easily lead to misjudgment and cause the optimal treatment time to be missed. Potatoes are crucial for food processing and daily consumption. They are a representative crop that is also prone to significant economic losses due to pests and diseases. In order to solve the problem of identifying crop pest and disease images, this article combines image processing and VGG deep-learning techniques to implement an improved computer vision model and conducts experiments on seven types of pest and disease images of potato leaves. The experimental results show that compared with the traditional VGG16 model, the new model has a higher identification rate of pests and diseases on potato leaves, and it also has significant reference value for the pest and disease identification methods of other leaf crops.