ABSTRACT
Root-knot nematodes, genus Meloidogyne spp., are among the most destructive parasites of cultivated plants. The characteristic symptom of this disease is gall formation in the root system. Genetic resistance is one of the most efficient and economic methods of minimal environmental impact to control this endoparasite, and gall index has been used to select resistant varieties. However, this method is based on visual assessment of galls and is therefore a time-consuming and error-prone technique. Thus, this study aims to develop an automated computational method for Meloidogyne javanica gall counting. The proposed method was composed of five steps: visual counting of galls, image acquisition by a scanner, optimization of parameters based on the image group and image counting. Lettuce root showed the best results, with 1% mean relative error, while tomato root had the worst result, showing 32% mean relative error. The mean relative error for all tested roots was 13%.
Keywords
Nematodes; Circular Correlation; Computer Vision; Genetic Algorithm
