Edge detection for weed recognition in lawns

Abstract The rapid propagation of weeds is a major issue for turfgrass management (both ornamental and sports turf). While pesticides can ensure weed eradication, they pose a risk to human health and the environment. In this context, the early detection of weeds can allow a dramatic reduction in the amount of pesticide required. Here we present the use of edge detection techniques to identify the presence of these invasive plants in ornamental lawns and sports turf. Regarding the former, images from small experimental plots in the facilities of IMIDRA were used while images for the latter were taken on a golf course. Up to 12 different filters for edge detection were tested on the images collected. Aggregation techniques, with a range of cell values, were applied to the results of the three most effective filters (sharpening (I), sharpening (II), and Laplacian) to minimise the number of false positives. After the tests with different cell sizes, two filters were selected for more in-depth analysis. Box plots were selected to define the best cell size and identify the filter with the best performance. The sharpening (I) filter and the aggregation technique with the minimum value and a cell size of 10 offered the best results. Finally, we determined the most appropriate threshold value on the basis of the number of false positives, false negatives, and derived indexes (Precision, Recall, and F1-Score). A threshold of 78 gave the best performance. The results achieved with this methodology differed slightly between ornamental and sports turf.