Improved vegetation segmentation with ground shadow removal using an HDR camera

• Published in: Precision agriculture Vol. 19; no. 2; pp. 218 - 237
• Main Authors: Suh, Hyun K., Hofstee, Jan Willem, van Henten, Eldert J.
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.04.2018; Springer Nature B.V
• Subjects: Agricultural land; Agriculture; Algorithms; Atmospheric Sciences; Biomedical and Life Sciences; Cameras; Chemistry and Earth Sciences; Computer Science; High dynamic range; Illumination; Image processing; Life Sciences; Physics; Real time; Remote Sensing/Photogrammetry; Segmentation; Shadow detection and remove; Shadows; Soil Science & Conservation; Statistics for Engineering; Vegetation; Vegetation segmentation; Weed control; Weeds; Weed control; High dynamic range; Image processing; Vegetation segmentation; Shadow detection and remove
• ISSN: 1385-2256; 1573-1618
• DOI: 10.1007/s11119-017-9511-z

A vision-based weed control robot for agricultural field application requires robust vegetation segmentation. The output of vegetation segmentation is the fundamental element in the subsequent process of weed and crop discrimination as well as weed control. There are two challenging issues for robust vegetation segmentation under agricultural field conditions: (1) to overcome strongly varying natural illumination; (2) to avoid the influence of shadows under direct sunlight conditions. A way to resolve the issue of varying natural illumination is to use high dynamic range (HDR) camera technology. HDR cameras, however, do not resolve the shadow issue. In many cases, shadows tend to be classified during the segmentation as part of the foreground, i.e., vegetation regions. This study proposes an algorithm for ground shadow detection and removal, which is based on color space conversion and a multilevel threshold, and assesses the advantage of using this algorithm in vegetation segmentation under natural illumination conditions in an agricultural field. Applying shadow removal improved the performance of vegetation segmentation with an average improvement of 20, 4.4, and 13.5% in precision, specificity and modified accuracy, respectively. The average processing time for vegetation segmentation with shadow removal was 0.46 s, which is acceptable for real-time application (<1 s required). The proposed ground shadow detection and removal method enhances the performance of vegetation segmentation under natural illumination conditions in the field and is feasible for real-time field applications.