Insights in the ability of high-resolution narrow band multispectral and thermal sensors to estimate cotton production in Australia

F. Devoto*, S. Reynolds-Massey-Reed, Segura C. Pinzon, M. Bell, T. McLaren, R. Awale, C. Camino, M. Bange, W. Woodgate, S. Chapman, A.B. Potgieter

*Corresponding author for this work

Research output: Contribution to conferenceConference paperAcademicpeer-review

Abstract

Cotton significantly contributes to global agriculture and provides livelihoods for approximately 100 million farmers in 80 countries. Therefore, new approaches are needed to better inform producers, in near-real time, for optimising crop management practices, increasing profitability and sustainability. Here, we investigated the potential of proximal sensing metrics, derived from multispectral and thermal bands onboard an Unmanned Aerial Vehicles (UAVs), to estimate variability in cotton production due to different agronomic practices. We employed three main approaches, including (i) multilinear regression (MR), (ii) random forest (RF) and (iii) partial least square (PLS). All methods showed significantly strong relationship with lint yield. Specifically, the MR approach explained around 88% (R2 = 0.88, RMSE = 322 kg/ha) of the variance in final yield across all plots. Further research is currently underway to explore the ability of multi-temporal, hyperspectral and radiative transfer models (RTM) to understand variability across different phenological stages in cotton management.

Original languageEnglish
Pages1510-1513
Number of pages4
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024 - Athens, Greece
Duration: 7 Jul 202412 Jul 2024

Conference/symposium

Conference/symposium2024 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2024
Country/TerritoryGreece
CityAthens
Period7/07/2412/07/24

Keywords

  • hyperspectral
  • machine learning
  • UAV
  • vegetation indices
  • yield prediction

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