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

Article

Xiong, Yiyi, Chiau, Lucas Mauro Rogerio, Wenham, Kylie, Collins, Marisa and Chapman, Scott C.. 2024. "Utilisation of unmanned aerial vehicle imagery to assess growth parameters in mungbean (Vigna radiata (L.) Wilczek)." Crop and Pasture Science. 75 (1). https://doi.org/10.1071/CP22335
Article Title

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

ERA Journal ID5177
Article CategoryArticle
AuthorsXiong, Yiyi, Chiau, Lucas Mauro Rogerio, Wenham, Kylie, Collins, Marisa and Chapman, Scott C.
Journal TitleCrop and Pasture Science
Journal Citation75 (1)
Article Number CP22335
Number of Pages15
Year2024
PublisherCSIRO Publishing
Place of PublicationAustralia
ISSN0004-9409
1836-0947
1836-5795
Digital Object Identifier (DOI)https://doi.org/10.1071/CP22335
Web Address (URL)https://www.publish.csiro.au/CP/CP22335
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.

Keywordsbiomass; fractional light interception; leaf area; mungbean physiology; radiation use efficiency; RGB images and vegetation indices
Related Output
Is supplemented byCorrigendum to: Utilisation of unmanned aerial vehicle imagery to assess growth parameters in mungbean (Vigna radiata (L.) Wilczek)
Contains Sensitive ContentDoes not contain sensitive content
ANZSRC Field of Research 20203099. Other agricultural, veterinary and food sciences
Public Notes

This article has been corrected. Please see the Related Output.

Byline AffiliationsUniversity of Queensland
Eduardo Mondlane University, Mozambique
La Trobe University
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