Utilisation of unmanned aerial vehicle imagery to assess growth parameters in mungbean (Vigna radiata (L.) Wilczek)

Yiyi Xiong; Lucas Mauro Rogerio Chiau; Kylie Wenham; Marisa Collins; Scott C. Chapman

doi:10.1071/CP22335

RESEARCH ARTICLE (Open Access)

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Utilisation of unmanned aerial vehicle imagery to assess growth parameters in mungbean (Vigna radiata (L.) Wilczek)

Yiyi Xiong

^A , Lucas Mauro Rogerio Chiau

^A ^B , Kylie Wenham

^C , Marisa Collins

^A ^C ^D and Scott C. Chapman

^A ^*

+ Author Affiliations

- Author Affiliations

^A School of Agriculture and Food Sciences, The University of Queensland, Gatton, Qld 4343, Australia.

^B Department of Crop Production, Faculty of Agronomy and Forestry Engineering, Eduardo Mondlane University, Maputo 257, Mozambique.

^C Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, 306 Carmody Road, St Lucia, Qld 4072, Australia.

^D Animal, Plant and Soil Sciences, La Trobe University, Melbourne, Vic. 3086, Australia.

^* Correspondence to: scott.chapman@uq.edu.au

Handling Editor: Fernanda Dreccer

Crop & Pasture Science 75, CP22335 https://doi.org/10.1071/CP22335

Submitted: 15 October 2022 Accepted: 17 August 2023 Published: 11 September 2023

© 2024 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND)

Abstract

Context

Unmanned aerial vehicles (UAV) with red–green–blue (RGB) cameras are increasingly used as a monitoring tool in farming systems. This is the first field study in mungbean (Vigna radiata (L.) Wilzcek) using UAV and image analysis across multiple seasons.

Aims

This study aims to validate the use of UAV imagery to assess growth parameters (biomass, leaf area, fractional light interception and radiation use efficiency) in mungbean across multiple seasons.

Methods

Field experiments were conducted in summer 2018/19 and spring–summer 2019/20 for three sowing dates. Growth parameters were collected fortnightly to match UAV flights throughout crop development. Fractional vegetation cover (FVC) and computed vegetation indices: colour index of vegetation extraction (CIVE), green leaf index (GLI), excess green index (ExG), normalised green-red difference index (NGRDI) and visible atmospherically resistant index (VARI) were generated from UAV orthomosaic images.

Key results

(1) Mungbean biomass can be accurately estimated at the pre-flowering stage using RGB imagery acquired with UAVs; (2) a more accurate relationship between the UAV-based RGB imagery and ground data was observed during pre-flowering compared to post-flowering stages in mungbean; (3) FVC strongly correlated with biomass (R² = 0.79) during the pre-flowering stage; NGRDI (R² = 0.86) showed a better ability to directly predict biomass across the three experiments in the pre-flowering stages.

Conclusion

UAV-based RGB imagery is a promising technology to replace manual light interception measurements and predict biomass, particularly at earlier growth stages of mungbean.

Implication

These findings can assist researchers in evaluating agronomic strategies and considering the necessary management practices for different seasonal conditions.

Keywords: biomass, fractional light interception, ground truth data, growth parameters, leaf area, mungbean physiology, radiation use efficiency, RGB images and vegetation indices.

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