Autonomous Pest Detection System using IoT Sensors and Predictive Analysis using Deep Learning and GenAI Techniques - Rajini, S. Akila; Nandhini, K. (Journal of Neonatal Surgery, 2025)
To develop an autonomous smart pest detection system, the utilization of Smart IoT devices and adoption of deep learning techniques along with generative artificial intelligence techniques is necessary. Specifically designed for farming applications, the proposed system inbuilds an array of sensors comprising of an Infrared Sensor for nocturnal insect movement observation, an acoustic sensor for capturing sounds generated by the pests and environmental sensors like temperature, humidity, and light sensors, image sensors as well. The temperature sensor signifies its role in identifying optimal breeding conditions for pests prevalent in agricultural settings. The humidity sensor measures the moisture levels as per the pest activity and breeding. The light sensor monitors quantifies the pest behavior during different times of the day. These sensors are fabricated managing the pest ecosystem in farming. By using the widely adaptive deep learning techniques, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), the collected data is trained on the smart system for precise pest identification. The use of GenAI technique, further enhances the system by introducing a Chabot capable of interpreting observed data and supporting the potential pest-related trends according to the dynamic environmental conditions. Several test cases are performed on the proposed detection system, providing the fabricated smart system as an efficient one. The results and performance of the proposed system is well-suited for deployment in agricultural settings, with the potential that improves both the quality and quantity of crops in farming practices