Citrus Disease and Pest Recognition Algorithm Based on Migration Learning

Abstract

Citrus is the largest fruit production in the world. Owing to the damage by various pest diseases, the production of citrus is reduced and the quality is getting worse and worse every year. The recognition and control of the citrus diseases are very important. By now the main measures we take to control them is sowing pesticides, which is not good for the environment and do harm to the soil greatly. The technology of image identification can recognize what kind of citrus disease they have with high efficiency and low cost, which is also environmentally friendly and is not limited by time and space. It is our top priority to apply it to recognize and prevent the disease from citrus. In order to detect citrus pest disease and control them automatically, we studied the pests and traits of citrus leaves and their multi-fractal characteristics and methods for figuring pests and diseases, and created a model for detecting leaf images of citrus. We use Keras and Tensorflow to build the model. To reduce recognition loss and improve accuracy, we put the citrus photos into the model and train it persistently. After examining, the recognition accuracy of citrus greening disease of 120 images can reach 96%. The experimental result shows that the model can recognize citrus diseases with high accuracy and robustness.