SProtoNet: self-supervised ProtoNet for plant leaf disease few-shot classification

Plant leaf disease few-shot classification can learn from a few samples to recognize the disease class automatically, which can protect the agriculture yield and quality. However, the support set of previous works still very big, which introduce 50 samples per class for training, which is still relatively large. Therefore, the training way of previous work is not in line with the few-shot classification. In this work, we propose a new network SProtoNet, which introduce the self-supervised learning and our proposed Prototype feature extractor to learn a more discrete feature space. And in order to test our proposed SProtoNet, we introduce the typical 5way1shot and 5way5shot to evaluate. Furthermore, we also propose a new baseline, and our network can achieve the state-of-the-art performance in the Plant village dataset.