Agriculture monitoring system based on internet of things by deep learning feature fusion with classification

This research proposed novel technique in crop monitoring system using machine learning-based classification using UAV. To monitor and operate activities from remote locations, UAVs extended their freedom of operation. For smart farming, it's significant to use UAV prospects. On the other hand, the cost and convenience of using UAVs for smart-farming may be a major factor in farmers' decisions to use UAVs in farming. The IoT-based module is used to update the database with monitored data. Using this method, live data should be updated soon, and it can help in crop cultivation identification. Research also monitor climatic conditions using live satellite data. The data is collected as well as classified for detecting crop abnormality based on climatic conditions and pre-historic data based on cultivation for the field also this monitoring system will differ-entiate weeds and crops. Simulation results show accuracy, precision, specificity for trained data by detecting the crop abnormality.