Utilizing multispectral imaging for improved weed and crop detection

Conventional agriculture relies heavily on herbicides for weed control. Smart farming, particularly through the
use of mechanical weed control systems, has the potential to reduce the herbicide usage and the associated
negative impact on our environment. The growing accessibility of multispectral cameras in recent times poses
the question if their added expenses justify the potential advantages they offer. In this study we compare the
weed and crop detection performance between RGB and multispectral VIS-NIR imaging data. Therefore, we
created and annotated a multispectral instance segmentation dataset for sugar beet crop and weed detection.
We trained Mask-RCNN models on the RGB images and on images composed of different vegetation indices
calculated from the multispectral data. The outcomes are thoroughly analysed and compared across various
scenarios. Our findings indicate that the use of vegetation indices can significantly improve the weed detection
performance in many situations.