Weed and Crop Detection by Combining Crop Row Detection and K-means Clustering in Weed Infested Agricultural Fields

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...

Full description

Saved in:
Bibliographic Details
Published in2022 IEEE/SICE International Symposium on System Integration (SII) pp. 985 - 990
Main Authors Ota, Kumpei, Louhi Kasahara, Jun Younes, Yamashita, Atsushi, Asama, Hajime
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.01.2022
Subjects
Automation
Crops
Spraying
System integration
Training data
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2474-2325
DOI:10.1109/SII52469.2022.9708815