A comparative study of RGB and multispectral imaging for weed detection in precision agriculture

Abstract

Precision agriculture and specifically mechanical weed control systems have the potential to positively impact our environment by reducing the use of herbicides. In recent years, multispectral cameras have become more and more accessible, which raises the question whether the additional costs of such cameras are worth the potential benefits. In this study, we recorded and annotated a multispectral instance segmentation dataset for sugar beet crop and weed detection. We trained Mask-RCNN models on the RGB and multispectral data in a transfer learning approach and extensively evaluated and compared the results for different scenarios. We found that the multispectral data can improve the weed detection performance significantly in many cases.