Background: Apples occupy a large part of agricultural production as a fruit with a high yield and also a high nutritional value. However, diseases of the fruit and leaves of apples seriously affect the quality and yield of apples. In the past, people had to rely on their own experience to control apple diseases, however, this approach was poorly accurate, inefficient and did not meet the requirements of fruit farmers. Many current methods are based on convolutional neural networks, but convolutional neural networks usually require a large amount of labelled data to train the network, and datasets in the agricultural eld can hardly meet this requirement.
Results: To solve this problem, this paper introduces zero-times learning, which can achieve equally good results even if the test object is a dataset that has never been seen before. Specifically, we give a short description of an image as a prompt word according to its category in the public dataset, form a graphical pair of the image to be trained and the corresponding prompt word, feed them into a deep convolutional neural network, and then pre-train it on a large public dataset, and migrate it directly to our dataset after the training is completed, saving a lot of resources. In addition, we also propose a new attention module WPM (Weighted-Pooling Module) to deeply mine feature vectors by combining weighted pooling operations with fully connected operations and activation functions. Through extensive experiments, we validate the effectiveness of the proposed approach of combining zero-times learning with prompt words and achieve good results on our own collected eld dataset.
Conclusions: Our work provides new ideas and resource savings for disease classification tasks in agriculture.