Peanut yield prediction with UAV multispectral imagery using a cooperative machine learning approach

Tej Bahadur Shahi; Cheng-Yuan Xu; Arjun Neupane; Dayle B. Fleischfresser; Daniel J. O'Connor; Graeme C. Wright; William Guo; Tej Bahadur Shahi; Cheng-Yuan Xu; Arjun Neupane; Dayle B. Fleischfresser; Daniel J. O'Connor; Graeme C. Wright; William Guo

doi:10.3934/era.2023169

Electronic Research Archive

2023, Volume 31, Issue 6: 3343-3361. doi: 10.3934/era.2023169

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Peanut yield prediction with UAV multispectral imagery using a cooperative machine learning approach

1.
School of Engineering and Technology, Central Queensland University, Rockhampton, Australia
2.
School of Health, Medical, and Applied Sciences, Central Queensland University, Bundaberg, Australia
3.
Queensland Department of Agriculture and Fisheries, Townsville, Australia
4.
Peanut Company of Australia, Kingaroy, Australia

Received: 07 February 2023 Revised: 19 March 2023 Accepted: 30 March 2023 Published: 14 April 2023

The unmanned aerial vehicle (UAV), as a remote sensing platform, has attracted many researchers in precision agriculture because of its operational flexibility and capability of producing high spatial and temporal resolution images of agricultural fields. This study proposed machine learning (ML) models and their ensembles for peanut yield prediction using UAV multispectral data. We utilized five bands (red, green, blue, near-infra-red (NIR) and red-edge) multispectral images acquired at various growth stages of peanuts using UAV. The correlation between spectral bands and yield was analyzed for each growth stage, which showed that the maturity stages had a significant correlation between peanut yield and spectral bands: red, green, NIR and red edge (REDE). Using these four bands spectral data, we assessed the potential for peanut yield prediction using multiple linear regression and seven non-linear ML models whose hyperparameters were optimized using simulated annealing (SA). The best three ML models, random forest (RF), support vector machine (SVM) and XGBoost, were then selected to construct a cooperative yield prediction framework with both the best ML model and the ensemble scheme from the best three as comparable recommendations to the farmers.
- unmanned aerial vehicle,
- multispectral imagery,
- machine learning,
- simulated annealing,
- peanut yield prediction,
- random forest,
- support vector machine,
- XGBoost
Citation: Tej Bahadur Shahi, Cheng-Yuan Xu, Arjun Neupane, Dayle B. Fleischfresser, Daniel J. O'Connor, Graeme C. Wright, William Guo. Peanut yield prediction with UAV multispectral imagery using a cooperative machine learning approach[J]. Electronic Research Archive, 2023, 31(6): 3343-3361. doi: 10.3934/era.2023169

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Abstract

The unmanned aerial vehicle (UAV), as a remote sensing platform, has attracted many researchers in precision agriculture because of its operational flexibility and capability of producing high spatial and temporal resolution images of agricultural fields. This study proposed machine learning (ML) models and their ensembles for peanut yield prediction using UAV multispectral data. We utilized five bands (red, green, blue, near-infra-red (NIR) and red-edge) multispectral images acquired at various growth stages of peanuts using UAV. The correlation between spectral bands and yield was analyzed for each growth stage, which showed that the maturity stages had a significant correlation between peanut yield and spectral bands: red, green, NIR and red edge (REDE). Using these four bands spectral data, we assessed the potential for peanut yield prediction using multiple linear regression and seven non-linear ML models whose hyperparameters were optimized using simulated annealing (SA). The best three ML models, random forest (RF), support vector machine (SVM) and XGBoost, were then selected to construct a cooperative yield prediction framework with both the best ML model and the ensemble scheme from the best three as comparable recommendations to the farmers.

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