基于深度学习的小麦病害识别方法研究与实现

Research and Implementation of Wheat Disease Identification Method Based on Deep Learning

摘要: 小麦是一种重要的粮食作物，在我国的需求量很大，因此其种植面积也在不断增加。但是，随着小麦种植面积的增加，也出现了越来越多的病害，这些病害都会对小麦的生长和产量产生负面影响。随着人工智能水平的提高，现在小麦病害识别技术已经融合了深度学习、计算机视觉和图像处理等先进技术，可以对小麦病害进行自动识别和分类，并且取得显著的成就。本项目结合深度学习技术将YOLOv8模型应用在小麦病害识别中，构建基于卷积神经网络的小麦病害识别方法，实现对小麦病害的快速、准确识别和定位。通过准确识别小麦病害，农民和农业技术人员能够迅速采取针对性的防治措施，防止病害的扩散和加重，从而保障小麦的正常生长和发育，提高产量和品质。其次，准确的病害识别能够避免农民盲目使用农药和化肥，有助于降低农业生产成本，减少农药残留，保护生态环境。

Abstract: Wheat is an important food crop, which is in great demand in our country, so its cultivation area is also increasing. However, as the area under wheat cultivation increases, more and more diseases have emerged, all of which can negatively affect the growth and yield of wheat. With the improvement of the level of artificial intelligence, wheat disease identification technology has integrated advanced technologies such as deep learning, computer vision and image processing, which can automatically identify and classify wheat diseases, and has made remarkable achievements. Combined with deep learning technology, the YOLOv8 model was applied to wheat disease identification, and a wheat disease identification method based on a convolutional neural network was constructed, which realized the rapid and accurate identification and localization of wheat diseases. By accurately identifying wheat diseases, farmers and agricultural technicians can quickly take targeted control measures to prevent the spread and aggravation of the disease, thereby ensuring the normal growth and development of wheat and improving yield and quality. Secondly, accurate disease identification can prevent farmers from blindly using pesticides and fertilizers, reduce unnecessary input diseases, reduce agricultural production costs, and finally, reduce pesticide residues and protect the ecological environment.