Insect Detection and Classification Based on an Improved Convolutional Neural Network

Regarding the growth of crops, one of the important factors affecting crop yield is insect disasters. Since most insect species are extremely similar, insect detection on field crops, such as rice, soybean and other crops, is more challenging than generic object detection. Presently, distinguishing insects in crop fields mainly relies on manual classification, but this is an extremely time-consuming and expensive process. This work proposes a convolutional neural network model to solve the problem of multi-classification of crop insects. The model can make full use of the advantages of the neural network to comprehensively extract multifaceted insect features. During the regional proposal stage, the Region Proposal Network is adopted rather than a traditional selective search technique to generate a smaller number of proposal windows, which is especially important for improving prediction accuracy and accelerating computations. Experimental results show that the proposed method achieves a heightened accuracy and is superior to the state-of-the-art traditional insect classification algorithms.