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

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Shahi, T. B., Xu, C.-Y., Neupane, A., Fleischfresser, D. B., O'Connor, D. J., Wright, G. C. and Guo, W. (2023) Peanut yield prediction with UAV multispectral imagery using a cooperative machine learning approach. Electronic Research Archive, 31 (6). pp. 3343-3361. ISSN 2688-1594


Article Link: https://doi.org/10.3934/era.2023169

Publisher URL: https://www.aimspress.com/article/doi/10.3934/era.2023169


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.

Item Type:Article
Corporate Creators:Department of Agriculture and Fisheries, Queensland
Business groups:Crop and Food Science
Keywords:unmanned aerial vehicle multispectral imagery AgriTech AgTech machine learning simulated annealing peanut yield prediction random forest support vector machine XGBoost
Subjects:Agriculture > Agriculture (General) > Methods and systems of culture. Cropping systems
Agriculture > Agriculture (General) > Farm machinery and farm engineering
Plant culture > Fruit and fruit culture > Nuts
Technology > Technology (General)
Live Archive:27 Jun 2023 01:48
Last Modified:27 Jun 2023 01:48

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