Our solution provides end-to-end service for farms starting from a Ground station which is a Web-based tool accessible across multiple devices to plan a mission in the region of interest in the agricultural field. Then optimized the path for the multiple UAVs to provide a power-efficient system. Each UAV was equipped with an onboard computer and camera to allowing them to collect image data. This Data was sent to our server which then uses a Deep Learning Algorithm called Convolution Neural Network to process and classify the collected data to their respective classes (either a healthy crop or diseases-affected crops/Weeds). The results of the analysis are present to the user on the web-based tool highlighting areas that are affected on the map in an easy-to-understand way. Both Simulation and field tests show that the architecture offers a number of advantages that include fault-tolerance, robustness, developer, and user-friendliness.
Sectors | Artificial intelligence, Crop farming, UAVs (drones) |
Location | Addis Ababa, Ethiopia |
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