Optimisation of natural images processing using different evolutionary algorithms

The development of image processing methods to discriminate between weed, crop and soil is an important step for precision agriculture, the main goal of which is the site-specific management of crops. The main challenge in terms of image analysis is to achieve an appropriate discrimination in outdoor field images under varying conditions of lighting, soil background texture and crop damage. This work presents several developed computer-vision-based methods for the estimation of percentages of weed, crop and soil in digital images of a crop field. These methods are interchangeable among them, having each one of them a set of input parameters that need to be adjusted differently for each image. Two different evolutionary methods (standard genetic algorithm and NSGA-II) have been used to adjust these parameters and find the best method combinations. The proposed approach can reach a correlation with real data of up to 97% for a set of images acquired from different fields and under different conditions.