Multispectral Image Segmentation in Agriculture: A Comprehensive Study
  on Fusion Approaches
Nuno Cunha Tiago Barros Mário Reis Tiago Marta Cristiano Premebida Urbano J. Nunes
Multispectral imagery is frequently incorporated into agricultural tasks,
providing valuable support for applications such as image segmentation, crop
monitoring, field robotics, and yield estimation. From an image segmentation
perspective, multispectral cameras can provide rich spectral information,
helping with noise reduction and feature extraction. As such, this paper
concentrates on the use of fusion approaches to enhance the segmentation
process in agricultural applications. More specifically, in this work, we
compare different fusion approaches by combining RGB and NDVI as inputs for
crop row detection, which can be useful in autonomous robots operating in the
field. The inputs are used individually as well as combined at different times
of the process (early and late fusion) to perform classical and DL-based
semantic segmentation. In this study, two agriculture-related datasets are
subjected to analysis using both deep learning (DL)-based and classical
segmentation methodologies. The experiments reveal that classical segmentation
methods, utilizing techniques such as edge detection and thresholding, can
effectively compete with DL-based algorithms, particularly in tasks requiring
precise foreground-background separation. This suggests that traditional
methods retain their efficacy in certain specialized applications within the
agricultural domain. Moreover, among the fusion strategies examined, late
fusion emerges as the most robust approach, demonstrating superiority in
adaptability and effectiveness across varying segmentation scenarios. The
dataset and code is available at https://github.com/Cybonic/MISAgriculture.git.