Application research of plant leaf pests and diseases base on unsupervised learning

Abstract

In agricultural productivity, detecting plant pests and diseases is extremely crucial. This research studies images of plant leaf pests and diseases from an unsupervised perspective to solve the problem that existing plant leaf disease datasets are difficult to acquire and include few types of diseases, and they cannot find the defective parts of leaves. This paper utilizes the idea of image restoration and uses a deep learning correlation model to detect and localize the abnormal regions of plant leaves. The experimental results show that the img_AUCROC and pixel_AUCROC level anomaly detection and localization achieve good results, which bring influence and reference to other peers.