Multiscale Feature Pyramid Network-Enabled Deep Learning and IoT-Based Pest Detection System Using Sound Analytics in Large Agricultural Field

Modern farming techniques can now be implemented globally and at a reasonable cost. In the annals of agriculture, this is a significant turning point moment. The widespread dissemination of information and the advent of cutting-edge technologies have facilitated this revolution. However, pests cause substantial harm to farmland, which has economic, ecological, and societal consequences. As a result, there is a need to employ sophisticated computational methods to eradicate pests before they cause irreparable harm. Recently, ML-based studies have focused on agricultural issues. The study's primary objective is to develop a workable method for identifying pests in big agricultural fields using dependable pest analytics made possible by the Internet of Things. The proposed method incorporates several different sound pre-processing techniques drawn from the field of sound analytics. BPF, Triangular window, Kaiser window, FFT method, DFT, and PLP are all examples. A Multiscale Feature Pyramid Network (MFP-Net) model was trained, tested, and validated using data from an analysis of 650 pest sounds. Compared to the existing MS-ALN, YOLOv5, Faster RCNN, and ResNet-50 models, the recommended MFP-Net model correctly identified the pest with a 99.76% accuracy, 99.03% recall, and 99.06% F1-score. The capability of this research to detect pests in large agricultural fields early on is the primary reason for its significance. As a direct result, the number of crops produced will increase, improving the economic growth of the farmers, the nation, and the world.