Weed and Crop Detection by Combining Crop Row Detection and K-means Clustering in Weed Infested Agricultural Fields
Kumpei Ota, Jun Younes Louhi Kasahara, Atsushi Yamashita, Hajime Asama
Abstract
Crop and weed detection is an essential technique for the automation of spot spraying and mechanical weeding. Previous studies developed crop and weed detection methods by using crop rows. However, those methods cannot perform with high accuracy when weeds are heavily present. The reason is that the crop row detection is adversely affected by the presence of large amounts of weed. And even if crop rows can be detected accurately, the methods wrongly label the weeds within crop rows as crop. Therefore, we propose a crop and weed detection method which can be used in presence of large amounts of weeds by combining crop row detection using depth data and crop/weed classification by k-means clustering. The experiment showed the effectiveness of the method by using images taken in unweeded cabbage field.