Meta-knowledge guided Bayesian optimization framework for robust crop yield estimation

Tunio, Muhammad Hanif and Li, Jian Ping and Zeng, Xiaoyang and Akhtar, Faijan and Shah, Syed Attique and Ahmed, Awais and Yang, Yu and Heyat, Md Belal Bin (2024) Meta-knowledge guided Bayesian optimization framework for robust crop yield estimation. Journal of King Saud University - Computer and Information Sciences, 36 (1). p. 101895. ISSN 2213-1248

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Abstract

Accurate pre-harvest crop yield estimation is vital for agricultural sustainability and economic stability. The existing yield estimating models exhibit deficiencies in insufficient examination of hyperparameters, lack of robustness, restricted transferability of meta-models, and uncertain generalizability when applied to agricultural data. This study presents a novel meta-knowledge-guided framework that leverages three diverse agricultural datasets and explores meta-knowledge transfer in frequent hyperparameter optimization scenarios. The framework’s approach involves base tasks using LightGBM and Bayesian Optimization, which automates hyperparameter optimization by eliminating the need for manual adjustments. Conducted rigorous experiments to analyze the meta-knowledge transformation of RGPE, SGPR, and TransBO algorithms, achieving impressive R2 values (0.8415, 0.9865, 0.9708) using rgpe_prf meta-knowledge transfer on diverse datasets. Furthermore, the framework yielded excellent results for mean squared error (MSE), mean absolute error (MAE), scaled MSE, and scaled MAE. These results emphasize the method’s significance, offering valuable insights for crop yield estimation, benefiting farmers and the agricultural sector.

Item Type:	Article
Identification Number:	10.1016/j.jksuci.2023.101895
Dates:	Date Event 15 December 2023 Accepted 4 January 2024 Published Online
Uncontrolled Keywords:	Crop yield estimation, Agricultural datasets, Meta-knowledge, Hyper-parameter optimization, Knowledge transfer
Subjects:	CAH06 - agriculture, food and related studies > CAH06-01 - agriculture, food and related studies > CAH06-01-02 - agricultural sciences CAH06 - agriculture, food and related studies > CAH06-01 - agriculture, food and related studies > CAH06-01-07 - food sciences CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence
Divisions:	Faculty of Computing, Engineering and the Built Environment > College of Computing
Depositing User:	Syed Attique Shah
Date Deposited:	22 Jan 2024 14:42
Last Modified:	22 Jan 2024 14:42
URI:	https://www.open-access.bcu.ac.uk/id/eprint/15141

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