IMAL: An Improved Meta-learning Approach for Few-shot Classification of Plant Diseases

Abstract

The timely identification of plant diseases is crucial for the production of crops. For this problem, many excellent and state-of-the-art algorithms based on deep learning have emerged currently. However, these algorithms still have problems such as poor generalization, difficulty in learning and adapting to new tasks, and extreme reliance on large-scale data. This study introduces an improved meta-learning approach(IMAL) for the few-shot classification of plant diseases, which can produce good generalization performance on new tasks with only a small amount of data and several steps of gradient update. In IMAL, the model-agnostic meta-learning approach with strong generalization capability is used as the overall algorithm framework, a fresh loss function called soft-center loss is adopted to conquer the problem of the poor distinguishing ability of the softmax classifier for features, and the Parametric Rectified Linear Unit (PReLU) activation function is utilized to enhance the model fitting ability with negligible additional computational cost and overfitting risk. The experiment results of plant diseases identification confirmed that the proposed IMAL approach is superior to many current few-shot learning approaches.