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 this https URL

本研究针对农业应用中的图像分割问题，通过结合 RGB 和 NDVI 作为输入，比较了不同的融合方法在作物行检测中的效果，研究发现，传统的边缘检测和阈值法等经典分割方法在需要精确的前景 - 背景分离任务中具有竞争力，融合策略中的后期融合表现为最有效的方法，在不同的分割场景中展现出卓越的适应性和效果。

农业中的多光谱图像分割：融合方法的综合研究

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

Autonomous navigation in agricultural environments is often challenged by
varying field conditions that may arise in arable fields. The state-of-the-art
solutions for autonomous navigation in these agricultural environments will
require expensive hardware such as RTK-GPS. This paper presents a robust crop
row detection algorithm that can withstand those variations while detecting
crop rows for visual servoing. A dataset of sugar beet images was created with
43 combinations of 11 field variations found in arable fields. The novel crop
row detection algorithm is tested both for the crop row detection performance
and also the capability of visual servoing along a crop row. The algorithm only
uses RGB images as input and a convolutional neural network was used to predict
crop row masks. Our algorithm outperformed the baseline method which uses
colour-based segmentation for all the combinations of field variations. We use
a combined performance indicator that accounts for the angular and displacement
errors of the crop row detection. Our algorithm exhibited the worst performance
during the early growth stages of the crop.

该论文提出了一种利用卷积神经网络进行作物排检测的算法，可以在农田环境中应对各种变化，仅使用 RGB 图像作为输入，并在可视伺服算法中获得更好的性能表现。