Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables

Jui, S. Janifer Jabin and Ahmed, A. A. Masrur and Bose, Aditi and Raj, Nawin ORCID: https://orcid.org/0000-0002-8364-2644 and Sharma, Ekta and Soar, Jeffrey ORCID: https://orcid.org/0000-0002-4964-7556 and Chowdhury, Md Wasique Islam (2022) Spatiotemporal Hybrid Random Forest Model for Tea Yield Prediction Using Satellite-Derived Variables. Remote Sensing, 14 (3):805. pp. 1-18.

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Abstract

Crop yield forecasting is critical for enhancing food security and ensuring an appropriate food supply. It is critical to complete this activity with high precision at the regional and national levels to facilitate speedy decision-making. Tea is a big cash crop that contributes significantly to economic development, with a market of USD 200 billion in 2020 that is expected to reach over USD 318 billion by 2025. As a developing country, Bangladesh can be a greater part of this industry and increase its exports through its tea yield and production with favorable climatic features and land quality. Regrettably, the tea yield in Bangladesh has not increased significantly since 2008 like many other countries, despite having suitable climatic and land conditions, which is why quantifying the yield is imperative. This study developed a novel spatiotemporal hybrid DRS-RF model with a dragonfly optimization (DR) algorithm and support vector regression (S) as a feature selection approach. This study used satellite-derived hydro-meteorological variables between 1981 and 2020 from twenty stations across Bangladesh to address the spatiotemporal dependency of the predictor variables for the tea yield (Y). The results illustrated that the proposed DRS-RF hybrid model improved tea yield forecasting over other standalone machine learning approaches, with the least relative error value (11%). This study indicates that integrating the random forest model with the dragonfly algorithm and SVR-based feature selection improves prediction performance. This hybrid approach can help combat food risk and management for other countries.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current – Faculty of Health, Engineering and Sciences - School of Mathematics, Physics and Computing (1 Jan 2022 -)
Faculty/School / Institute/Centre: Current - Faculty of Business, Education, Law and Arts - School of Business (18 Jan 2021 -)
Date Deposited: 15 Mar 2022 23:25
Last Modified: 15 Mar 2022 23:56
Uncontrolled Keywords: Bangladesh; Hybrid model; Machine learning; Meteorological variables; Satellite information; Tea yield
Fields of Research (2020): 46 INFORMATION AND COMPUTING SCIENCES > 4609 Information systems > 460999 Information systems not elsewhere classified
30 AGRICULTURAL, VETERINARY AND FOOD SCIENCES > 3002 Agriculture, land and farm management > 300206 Agricultural spatial analysis and modelling
Socio-Economic Objectives (2020): 26 PLANT PRODUCTION AND PLANT PRIMARY PRODUCTS > 2699 Other plant production and plant primary products > 269999 Other plant production and plant primary products not elsewhere classified
Identification Number or DOI: https://doi.org/10.3390/rs14030805
URI: http://eprints.usq.edu.au/id/eprint/47048

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