Every country's foundation is its agricultural industry, which drives about half of global economic growth. Accurate farming is crucial for crop evaluation and pest control. Old-fashioned pest detection is unstable and gives inaccurate forecasts. These surveillance tactics are intrusive, expensive, and vulnerable to assumptions. Pest sounds can be caught with minimum effort by utilizing IoT networks. Deep learning algorithms, species distribution range assessments, and nature monitoring can automatically identify and categorize pest sounds. The IoT-driven computerized components in this research's unique pest identification system used machine learning on insect sound recordings. The Butterworth filter, Blackman and Flattop window, Ultraspherical Filter, Rife-Vincent Window, Cosine-Tapered Window, FFT, DFT, STFT, PNCC, RASTA-PLPCC, LSFCC, sound detectors, and PID sensors were employed. The intended study used the HFDLNet for training, testing, and validation, examining 7,200 pest sounds from 72 species to determine their unique characteristics and statistical attributes. The proposed model has 99.87 % accuracy, 99.96 % sensitivity, 99.88 % specificity, 99.96 % recall, 99.93 % F1 score, and 99.98 % precision. This study outperforms Inception-ResNet-v2, FRCNN ResNet-50, Fatser-PestNet, MD-YOLO, YOLOv5m, MAM-IncNet, and Xception. The suggested solution uses IoT networks and pest sound analysis to establish a pest preventive and management strategy and a solar-powered generator to power IoT devices across vast agricultural fields.