Predictive analysis of smart agriculture using IoT-based UAV and propagation models of machine learning. (4th November 2022) - Kumarasamy, M.; Pattanaik, Balachandra; Dwivedi, Jaiprakash Narain; Ramji, B.R.; Ponnusamy, Muruganantham; Nagaraj, V.
Every year, unfavourable weather conditions cause many crops to fail. Every time, over 12 million dollar losses are recorded. This article provides a proper background for delivering the yield's current state. The project proposes to employ IoT-based unmanned aerial vehicles (UAVs) and tensor-flow machine learning to estimate crop yields. This framework enhances agricultural yield accuracy by using UAVs. The IoT-enabled UAV module captures data and texts it to the farmer or rancher. The data cloud storage's server uses MQTT for safe data transmission. The cloud server leverages UAV for continuous surveillance and harvest forecasts. Predictive analysis using propagation model has an accuracy of roughly 85% compared to real-time analysis for the same crops at the farm.