Research on apple leaf spot anomaly detection based on Swin-CNN
Addressing the issue of potential failure in timely detection when new diseases emerge,a novel approach for dealing with apple leaf spot diseases has been introduced:anomaly detection.A universal image anomaly enhancement network(Swin-CNN)was adopted,which only needs to be trained once to detect diseased leaves that had never been seen before.Swin-CNN was trained on a set of anomaly free images using random masks to learn spatial contextual features between anomalies and background in an unsupervised manner.The experimental results showed that Swin-CNN was the optimal model,achieving an accuracy of 88.1%in identifying apple leaf spot disease,84.0%in identifying apple leaf brown spot disease,87.0%in identifying apple leaf gray spot disease,and 97.6%in identifying apple leaf rust disease.This method can provide a theoretical basis for early warning of apple leaf disease spots and serve as a reference for the detection of other crop diseases.
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