Quantification of species composition in grass-clover swards using RGB and multispectral UAV imagery and machine learning.

ABSTRACT:

Introduction

Growing grass-legume mixtures for forage production improves both yield productivity and nutritional quality, while also benefiting the environment by promoting species biodiversity and enhancing soil fertility (through nitrogen fixation). Consequently, assessing legume proportions in grass-legume mixed swards is essential for breeding and cultivation. This study introduces an approach for automated classification and mapping of species in mixed grass-clover swards using object-based image analysis (OBIA).

Methods

The OBIA procedure was established for both RGB and ten band multispectral (MS) images capturedby an unmanned aerial vehicle (UAV). The workflow integrated structural (canopy heights) and spectral variables (bands, vegetation indices) along with a mach

SUBMITTER: Pranga J 

PROVIDER: S-EPMC11219903 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature