Diagnosis of Fruit Tree Diseases and Pests Based on Agricultural Knowledge Graph

Abstract

In order to realize the accurate prediction of fruit tree diseases and pests in the text description, this paper combines knowledge graph, representation learning, deep neural network and other methods to construct a fruit tree disease and pest’s diagnosis model. The model first constructs a knowledge graph in the agricultural field, and encodes the knowledge in the agricultural field through the knowledge representation model, combines the description text provided by the user to obtain the representation vector of the fruit tree diseases and pests feature entity, and then passes the representation vector and the pest image representation vector through CNN-DNN-BiLSTM network recognizes fruit tree diseases and pests. Three kinds of diseases and pests of apple trees were selected in the experiment: Apple Ring Rot, Apple Scab and Adoxophyes orana. Compared with the VGG network and the BiLSTM network, the precision rate of the model in this paper has been improved by 19%, 4%, 3%, 20% and 25%, 2% on Apple Ring Rot, Apple Scab and Adoxophyes orana, respectively. It can fully integrate agricultural knowledge graph and deep learning technology, and play a positive role in improving the diagnosis of fruit tree diseases and pests.