Research Projects

The project aims to detect RPW early using efficient signal processing from a bioacoustics sensor and particle size characterization from a microwave resonator sensor. The project aims to combine the signals from both sensors, filter noise during bioacoustics signal processing, and fuse the resultant signals. The microwave resonating sensor will characterize RPW stages, avoiding false positives. A scalable neural network predictive model will be implemented for early detection of infestations at the edge of the Internet of Farming Things (IoFT) network, providing a user-friendly IoT framework for pest control of Cocos Nucifera. The main objectives of the proposal include designing a smart RPW management tool with multiple sensors, a flexible prototype, measurement system, and power management with a radio-frequency front end for data processing system, fabricating a microwave resonator sensor, detecting hidden breeding states of RPW, integrating multi-array multi-sensor elements, enabling edge computing within the RPW management system, enabling real-time connectivity between the device and edge gateway, enhancing smartness through intelligent signal processing, visual application interface, automatic early detection, machine learning analysis, edge processing, filtering, anomaly deduction, and reduced delay and overhead. The project will validate the RPW management tool in an IoFT environment with real-time grading and provide appropriate control measures. The Food and Agriculture Organization (FAO) of the United Nations (UN) has called for immediate action to combat RPW and stop its spread.