Efficient data-centric pest images identification method based on Mahalanobis entropy for intelligent agriculture

The frequent outbreak of crop pests is one of the main factors affecting crop yield. The accurate identification of pests is significant for effective pest control, which is the basis for the safe growth of crops and is also an important guarantee for high crop quality and crop yield. Computer vision based on supervised deep learning enables intelligent identification of pests, whose success is still inseparable from a large amount of labeled data, causing a large number of resource consumption due to data labeling. Therefore, an in-depth study on maximizing the value of data is essential when lacking labeled data. A new data evaluation method based on Mahalanobis distance and entropy is proposed to address the problem of lacking labeled data in intelligent pest identification. This method enables filtering high-value data, thus achieving effective pest identification performance with a small data volume. The experiment is conducted on a dataset we collected called PD-20, which shows the proposed method achieves baseline accuracy of 100% using only about 60% of the original data. Moreover, the proposed method can save at least 10% of the data volume compared with three comparison methods. To facilitate the deep integration of smart agriculture with artificial intelligence (AI), we designed an interactive framework of active learning for pest identification based on the proposed method, which lays the foundation for the application of AI in direction of agriculture.