Utilizing multispectral imaging for improved weed and crop detection

Benedikt Fischer; Pascal Gauweiler; Robin Gruna; Jürgen Beyerer

doi:10.1117/12.3023597

17 June 2024 Utilizing multispectral imaging for improved weed and crop detection

Benedikt Fischer, Pascal Gauweiler, Robin Gruna, Jürgen Beyerer

Proceedings Volume 13024, Optical Instrument Science, Technology, and Applications III; 1302407 (2024) https://doi.org/10.1117/12.3023597
Event: SPIE Optical Systems Design, 2024, Strasbourg, France

Abstract

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.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Benedikt Fischer, Pascal Gauweiler, Robin Gruna, and Jürgen Beyerer "Utilizing multispectral imaging for improved weed and crop detection", Proc. SPIE 13024, Optical Instrument Science, Technology, and Applications III, 1302407 (17 June 2024); https://doi.org/10.1117/12.3023597

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