Multispectral Image Segmentation in Agriculture: A Comprehensive Study on Fusion Approaches

TL;DR

This study compares fusion approaches for multispectral image segmentation in agriculture, highlighting the effectiveness of classical methods and the robustness of late fusion strategies in crop row detection tasks.

Contribution

It provides a comprehensive comparison of classical and deep learning segmentation methods using multispectral data, emphasizing the advantages of late fusion in agricultural applications.

Findings

Classical segmentation methods can compete with deep learning in specific agricultural tasks.

Late fusion strategies are more robust and adaptable across different segmentation scenarios.

Traditional methods remain effective in noise reduction and feature extraction in agriculture.

Abstract

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.