Irrigation Detection by Car: Computer Vision and Sensing for the Detection and Geolocation of Irrigated and Non-irrigated Farmland

Irrigation can greatly increase the income of smallholder farmers in sub-Saharan Africa. By providing information about current irrigation utilization, or lack thereof, we seek to encourage investment in irrigation systems and their supporting infrastructure. In this paper, we describe the design, prototyping, and testing of a novel, cost-effective, and reliable computer vision system that is capable of locating irrigated plots at scale. Our system will be mounted to a vehicle and record the depth of objects in the camera’s view while the vehicle is in motion. The GPS coordinates of objects are computed based on estimated depth, vehicle coordinates, and orientation, available from included sensors. We tested our prototype on objects at various distances from the system and achieved feasible accuracy with acceptable error in the estimated depth. In the future, we hope to deploy the system in parts of sub-Saharan Africa, to detect and geolocate irrigated agricultural plots during the dry season. Then we plan to use that collected data to inform and train machine learning models that use remote sensing and satellite imagery.