Broccoli Seedling Pest Damage Degree Evaluation Based on Machine Learning Combined with Color and Shape Features

Abstract The degree of pest damage evaluation on corps in the field environment is very important for precision spraying pesticides. In this paper, we proposed an image processing method to identify the wormholes in the image of broccoli seedlings, and then to evaluate the damage of the broccoli seedlings by pests. The broccoli seedlings were taken as the research object. The ratio of wormhole areas to broccoli seedling leaves areas (Rw) was used to describe the pest damage degree. An algorithm was developed to calculate the ratio of wormhole areas to broccoli seedling leaves areas. Firstly, broccoli seedling leaves were segmented from the background and the area of the leaves was obtained. There were some holes in segmentation results due to pest damage and other reasons. Then, a classifier based on machine learning was developed to classify the wormholes and other holes. Twenty-four features, including color features and shape features of the holes, were used to develop classifiers. After identifying wormholes from images, the area of the wormholes was obtained and the degree of pest damage to broccoli seedling was calculated. The determination coefficient (R2) between the algorithm calculated pest damage degree and manually labeled pest damage degree was 0.85. The root-mean-square error (δ) was 0.02. Results demonstrated that the color and shape were able to effectively segment wormholes from leaves of broccoli seedlings and evaluate the degree of pest damage. This method could provide references for precision spraying pesticides.