Citation

Nair, Rekha R. and Babu, Tina and Nayak, Deepika and Yogarayan, Sumendra and Abdul Razak, Siti Fatimah (2026) Field Intelligence for Regenerative Agriculture in Predictive Sustainable Groundnut Farming in India. Procedia Computer Science, 283. pp. 3730-3739. ISSN 18770509

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Abstract

Groundnut, a key oilseed crop in India, faces stagnant yields due to environmental and agronomic challenges. Sustaining agriculture amid climate stress and soil degradation remains a major issue in dryland regions. Hence proposed a novel field intelligence framework to enhance groundnut yield forecasting and agroecological planning using regenerative principles. A Regenerative Agriculture Index (RAI) was developed by combining crop diversity, soil health proxy, and yield-based sustainability metrics. Genetic Algorithms (GA) were used for optimal feature selection, and XGBoost was employed for high-accuracy yield prediction. SHapley Additive Explanations(SHAP) was used to explain the model’s decision logic. The k-means clustering was applied to categorize regions into high, medium, and low sustainability zones. The model achieved an RMSE of 0.204 and R² of 0.89, outperforming traditional baselines. Results highlight the strong influence of RAI and previous yield on performance. This integrated approach supports informed decision-making for sustainable agriculture.

Item Type:	Article
Uncontrolled Keywords:	Regenerative Agriculture Index, Groundnut Yield Prediction
Subjects:	S Agriculture > S Agriculture (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 Sep 2026 00:57
Last Modified:	04 Sep 2026 00:57
URII:	http://shdl.mmu.edu.my/id/eprint/16660

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