Abstract
The presence of pests in soil costs the agriculture industry billions of dollars every year since it reduces crop yields and raises preventive costs. The pest detection in soil is vital for maintaining healthy crops, optimizing pest management, and ensuring economic and ecological sustainability. There are several invasive and non-invasive methods available for pest detection, where invasive methods are costly as well as time-consuming compared to the non-invasive methods. From various non-invasive methods, audio-based pest detection in the soil is one of the effective, low-cost tools. The generation of pest sounds is random in nature and contains a lot of inactive and background noisy portions in the recorded sound signals. To reduce the unnecessary computations in analyzing the inactive portions, an improved audio activity detection algorithm has been designed in this paper using Short Time Energy features for segmentation, which provides an average of 20% less computational requirements as compared to the baseline models. In the second step, the Forward Forward Algorithm has been used for its benefits in enhanced numerical stability, simplified computations, and enhanced precision over traditional back propagation-based algorithms. For improved performance in the detection of pests in soil, the traditional FF algorithm has been further updated by using root mean square in the goodness and loss function calculation. Through the comparative analysis with several baseline models, it has been observed that the proposed method consistently provides an average of 5% enhanced performance.