Faster-PestNet: A Lightweight Deep Learning Framework for Crop Pest Detection and Classification

Abstract

One of the most significant risks impacting crops is pests, which substantially decrease food production. Further, prompt and precise recognition of pests can help harvesters save damage and enhance the quality of crops by enabling them to take appropriate preventive action. The apparent resemblance between numerous kinds of pests makes examination laborious and takes time. The limitations of physical pest inspection are required to be addressed, and a novel deep-learning approach called the Faster-PestNet is proposed in this work. Descriptively, an improved Faster-RCNN approach is designed using the MobileNet as its base network and tuned on the pest samples to recognize the crop pests of various categories and given the name of Fatser-PestNet. Initially, the MobileNet is employed for extracting a distinctive set of sample attributes, later recognized by the 2-step locator of the improved Faster-RCNN model. We have accomplished a huge experimentation analysis over a complicated data sample named the IP102 and acquired an accuracy of 82.43%. Further, a local crops dataset is also collected and tested on the trained Faster-PestNet approach to show the generalization capacity of the proposed model. We have confirmed through analysis that the presented work can tackle numerous sample distortions like noise, blurring, light variations, and size alterations and can accurately locate the pest along with the associated class label on the leaf of numerous types and sizes. Both visual and stated performance values confirm the effectiveness of our model.