Improving weed pressure assessment using digital images from an experience-based reasoning approach

One of the main goals of Precision Agriculture is site-specific crop management to reduce the production of herbicide residues. This paper presents a computer-based image analysis system allowing users to input digital images of a crop field, and to process these by a series of methods to enable the percentages of weeds, crop and soil present in the image to be estimated. The system includes a Case-Based Reasoning (CBR) system that, automatically and in real time, determines which processing method is the best for each image. The main challenge in terms of image analysis is achieving appropriate discrimination between weeds, crop and soil in outdoor field images under varying light, soil background texture and crop damage conditions. The performance of the developed system is shown for a set of images acquired from different fields and under different, uncontrolled conditions, such as different light, crop growth stage and size of weeds, reaching correlation coefficients with real data of almost 80%.