Plant Leaf Diseases Classification Based on Few-Shot Learning

Abstract

Plant leaf disease is one of the important factors affecting the normal growth of plants. It is important to accurately identify the types of leaf disease and take effective measures to ensure the increase of crop production. In this study, we propose a plant leaf diseases classification method based on Few-Shot Learning (FSL), which can obtain good identification accuracy in the classification task with few samples. By changing the parameter setting in FSL, some key features affecting classification accuracy are revealed. The proposal of this research method expands the application scope of artificial intelligence in agriculture, which has important practical significance.