Block-scale Oil Palm Yield Prediction Using Machine Learning Approaches Based on Landsat and MODIS Satellite Data Article Swipe

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Yuhao Ang , Helmi Zulhaidi Mohd Shafri , Yang Ping Lee , Shahrul Azman Bakar , Haryati Abidin , Shaiful Jahari Hashim , Mohd Na’aim Samad , Nik Norasma Che’ya , Mohd Roshdi Hassan , H. S. Lim , Rosni Abdullah , Yusri Yusup , Syahidah Akmal Muhammad , Sin Yin Teh , Mohamed Barakat A. Gibril ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.37934/araset.45.1.90107

Due to environmental threats and weather uncertainty concerns, oil palm yield prediction is crucial for sustaining crop production. This can be achieved through machine learning and utilising remotely sensed data to predict crop yield. However, the comparative studies on remotely sensed data in adopting the machine learning models are still limited due to the data accessibility. Therefore, we compare and evaluate the prediction accuracy between different satellites, namely MODIS and Landsat-7, using machine learning algorithms and the topology of deep neural networks. Random forest and stacking outperformed linear regression, ridge regression, and lasso regression for both Landsat-7 NDVI (R2= 0.78–0.80; RMSE=1.00- 1.26 tonnes per hectare; MAE=0.77- 0.79 tonnes per hectares; MAPE=0.03-0.04 tonnes per hectare) and MODIS NDVI (R2= 0.60–0.65 tonnes per hectares; RMSE= 2.72–2.81 tonne per hectares; MAE= 1.42-1.55, MAPE= 1.01- 1.02 tonnes per hectares). The Landsat-7 NDVI revealed that neural networks with a deeper network topology (R2= 0.85; RMSE= 1.42 tonnes per hectare; MAE=0.57 tonnes per hectares; MAPE=0.06 tonnes per hectare) outperformed neural networks with a baseline and broader network topologies in terms of performance. In contrast, MODIS-NDVI revealed that the neural network with a wider network topology had the highest overall prediction accuracy and the lowest prediction error (R2= 0.75; RMSE= 2.81 tonnes per hectare; MAE=2.27 tonnes per tonnes; MAPE= 0.13). Because of its higher spatial resolution in comparison to MODIS, landsat-7 NDVI used in neural networks with a deep network topology provided the best model performance. Although the use of NDVI as a single input factor may cause uncertainty in some extents, it is an efficient and reliable method for improving yield estimation with the use of medium-resolution satellites, which has important implications for early warning towards the reduction in yield production.

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Aerospace Engineering

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Block (permutation group theory) Scale (ratio) Palm oil Satellite Remote sensing Yield (engineering) Satellite imagery Palm Environmental science Computer science Cartography Geography Mathematics Engineering Agroforestry Quantum mechanics Aerospace engineering Materials science Geometry Metallurgy Physics

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Type: article
Language: en
Landing Page: https://doi.org/10.37934/araset.45.1.90107
PDF: https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/download/3287/4805
OA Status: hybrid
Cited By: 2
References: 54
Related Works: 10
OpenAlex ID: https://openalex.org/W4398137793

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DOI: https://doi.org/10.37934/araset.45.1.90107

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Title: Block-scale Oil Palm Yield Prediction Using Machine Learning Approaches Based on Landsat and MODIS Satellite Data

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Language: en

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Publication year: 2024

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Publication date: 2024-05-17

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Authors: Yuhao Ang, Helmi Zulhaidi Mohd Shafri, Yang Ping Lee, Shahrul Azman Bakar, Haryati Abidin, Shaiful Jahari Hashim, Mohd Na’aim Samad, Nik Norasma Che’ya, Mohd Roshdi Hassan, H. S. Lim, Rosni Abdullah, Yusri Yusup, Syahidah Akmal Muhammad, Sin Yin Teh, Mohamed Barakat A. Gibril

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Landing page: https://doi.org/10.37934/araset.45.1.90107

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PDF URL: https://semarakilmu.com.my/journals/index.php/applied_sciences_eng_tech/article/download/3287/4805

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Concepts: Block (permutation group theory), Scale (ratio), Palm oil, Satellite, Remote sensing, Yield (engineering), Satellite imagery, Palm, Environmental science, Computer science, Cartography, Geography, Mathematics, Engineering, Agroforestry, Quantum mechanics, Aerospace engineering, Materials science, Geometry, Metallurgy, Physics

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Cited by: 2

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