Detection of tomato leaf disease in small sample under self-supervised learning
Rapid localization and accurate identification of tomato leaf diseases can help in the rational use of pesticides,thereby ensuring the quality and yield of tomatoes.In order to address the problem of poor performance of existing detection methods for tomato leaf disease,a self-supervised detection method for small sample tomato leaf disease was proposed.Firstly,a set of shared weight backbone networks were used to extract semantic features of tomato leaves in the visual space.Then,the visual semantic features were input into a deep auto-encoder network,and the feature encoding network was optimized by calculating the contrast loss between the encoded and original features.Finally,the encoded features were used to guide the localization and identification of unknown tomato leaf diseases.In addition,a double loss optimization strategy was designed to obtain more robust guiding feature sets.Through testing experiments on a self-built tomato disease leaf dataset and an open-source dataset,the proposed model achieved recognition accuracies of 0.946 2 and 0.963 9 on the self-built and open-source datasets,respectively,which were superior to current state-of-the-art object detection methods.
NETL
NSTL
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