A high performance-oriented AI-enabled IoT-based pest detection system using sound analytics in large agricultural field

Abstract

The agricultural industry has enormous potential to meet the world's growing need for nutritious and healthful food. However, pests destroy a significant portion of the harvest and reduce quality; thus, farmers struggle to discover pests on their farms. Traditional pest identification methods require scientists with extensive field experience correctly identify pests based on their physical characteristics. Pesticides harm food and agriculture. IoT technology uses many affordable sensor devices to collect real-time data on pest-related agricultural development characteristics. The research paper aims to develop an AI-enabled real-time, IoT-based automatic pest detection system using sound pest analytics and IoT networks in the sizeable agricultural field. The proposed method used audio pre-processing techniques in sound analytics such as HPF, Hann window, hop window, FFT, DFT, STFT, and MFCC algorithm for denoising the pest sound, removing the spectral leakage, converting overlapping to non-overlapping frames, converting the time to the frequency domain, determining frequency spectrum, detecting sinusoidal frequency and internal component, extracting the MFCCs feature respectively. The characteristics and other statistical metrics were collected from 500 pest sounds and trained, validated, and evaluated using the CNN, LSTM, Bi-LSTM, and CNN-Bi-LSTM network models. The extracting features and different statistical measurements were compared during the testing process. The proposed system integrated the CNN and Bi-LSTM techniques called CNN-Bi-LSTM model for training, validating, and testing. From the experimental results, it has been observed that, the proposed CNN-Bi-LSTM model achieved 98.91 % accuracy, 96.8 % sensitivity, 97.96 % specificity, 98.54 % recall, 98.63 % precision, and 98.58 % F1 score, which is better compared to CNN, LSTM, Bi-LSTM and also existing ANN, DCNN, and VGG-16 state-of-the-art techniques.