Image Recognition for Floating Waste Monitoring in a Traditional Surface Irrigation System

In the traditional surface irrigation system of Vega Baja del Segura (Spain), large amounts of floating waste accumulate at certain points of the river, irrigation channels and drainage ditches, causing malfunctioning of the irrigation network and rising social problems related to the origins of waste. This work proposes a standardized and quick methodology to characterize the floating waste to detect changes in its amount and components. A dataset was created with 477 images of floating plastic items in different environments and was used for training an algorithm based on YOLOv5s. The mean Average Precision of the trained algorithm was 96.9%, and the detection speed was 81.7 ms. Overhead photographs were taken with an unmanned aerial vehicle at strategic points of the river and channels, and its automatic count of floating objects was compared with their manual count. Both methods showed good agreement, confirming that water bottles were the most abundant (95%) type of floating waste. The automatic count reduced the required time and eliminated human bias in image analysis of the floating waste. This procedure can be used to test the reach of corrective measures implemented by local authorities to prevent floating waste in the river.