Academic Journal
SHAP-Backed Hybrid Ensemble Model for Rice and Wheat Forecasting in Data-Scarce Environments.
| Τίτλος: | SHAP-Backed Hybrid Ensemble Model for Rice and Wheat Forecasting in Data-Scarce Environments. |
|---|---|
| Συγγραφείς: | Negi, Harendra Singh, Dimri, Sushil Chandra |
| Πηγή: | International Journal of Mathematical, Engineering & Management Sciences; Jun2026, Vol. 11 Issue 3, p1395-1423, 29p |
| Θεματικοί όροι: | Ensemble learning, Shapley Additive Explanations, Agricultural forecasts, Precision farming, Machine learning, Random forest algorithms |
| Γεωγραφικοί όροι: | Uttarakhand (India) |
| Περίληψη: | As the issue of agricultural sustainability has continued to increase, there has been a need to use data based solutions to improve agricultural productivity. This paper proposes a machine learning system combining Random Forest and XGBoost to combine prediction-forecasting crop yield and classification of crop type rice and wheat in Indian state of Uttarakhand. The model is tested using a library of 6, 000 samples containing 12 soil and climatic characteristics and measured on regression and classification quality. The hybrid ensemble with hyperparameter optimization and verified on the basis of 10-fold cross-validation performed better than single base learners in all measures. It achieved a classification accuracy of 96.3 and R² = 0.927. Statistically significant developments that were formed using paired t-tests were set at p = 0.05. The SHAP and ablation analysis found out nitrogen, rainfall, and pH as the most influential features. The forecasted framework provides a better generalizability, interpretability, and computational effectiveness, which is appropriate to be applied in the designs of real life in precision agronomy. The new result is novel, interpretable, and high-performative to crop yield intelligence in data-scarce areas and provides a contribution to this study. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Mathematical, Engineering & Management Sciences is the property of Ram Arti Publishers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Header | DbId: edb DbLabel: Complementary Index An: 195826940 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.4189453125 |
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| Items | – Name: Title Label: Title Group: Ti Data: SHAP-Backed Hybrid Ensemble Model for Rice and Wheat Forecasting in Data-Scarce Environments. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Negi%2C+Harendra+Singh%22">Negi, Harendra Singh</searchLink><br /><searchLink fieldCode="AR" term="%22Dimri%2C+Sushil+Chandra%22">Dimri, Sushil Chandra</searchLink> – Name: TitleSource Label: Source Group: Src Data: International Journal of Mathematical, Engineering & Management Sciences; Jun2026, Vol. 11 Issue 3, p1395-1423, 29p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Ensemble+learning%22">Ensemble learning</searchLink><br /><searchLink fieldCode="DE" term="%22Shapley+Additive+Explanations%22">Shapley Additive Explanations</searchLink><br /><searchLink fieldCode="DE" term="%22Agricultural+forecasts%22">Agricultural forecasts</searchLink><br /><searchLink fieldCode="DE" term="%22Precision+farming%22">Precision farming</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Random+forest+algorithms%22">Random forest algorithms</searchLink> – Name: SubjectGeographic Label: Geographic Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Uttarakhand+%28India%29%22">Uttarakhand (India)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: As the issue of agricultural sustainability has continued to increase, there has been a need to use data based solutions to improve agricultural productivity. This paper proposes a machine learning system combining Random Forest and XGBoost to combine prediction-forecasting crop yield and classification of crop type rice and wheat in Indian state of Uttarakhand. The model is tested using a library of 6, 000 samples containing 12 soil and climatic characteristics and measured on regression and classification quality. The hybrid ensemble with hyperparameter optimization and verified on the basis of 10-fold cross-validation performed better than single base learners in all measures. It achieved a classification accuracy of 96.3 and R² = 0.927. Statistically significant developments that were formed using paired t-tests were set at p = 0.05. The SHAP and ablation analysis found out nitrogen, rainfall, and pH as the most influential features. The forecasted framework provides a better generalizability, interpretability, and computational effectiveness, which is appropriate to be applied in the designs of real life in precision agronomy. The new result is novel, interpretable, and high-performative to crop yield intelligence in data-scarce areas and provides a contribution to this study. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of International Journal of Mathematical, Engineering & Management Sciences is the property of Ram Arti Publishers and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.33889/IJMEMS.2026.11.3.057 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 29 StartPage: 1395 Subjects: – SubjectFull: Uttarakhand (India) Type: general – SubjectFull: Ensemble learning Type: general – SubjectFull: Shapley Additive Explanations Type: general – SubjectFull: Agricultural forecasts Type: general – SubjectFull: Precision farming Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Random forest algorithms Type: general Titles: – TitleFull: SHAP-Backed Hybrid Ensemble Model for Rice and Wheat Forecasting in Data-Scarce Environments. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Negi, Harendra Singh – PersonEntity: Name: NameFull: Dimri, Sushil Chandra IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 24557749 Numbering: – Type: volume Value: 11 – Type: issue Value: 3 Titles: – TitleFull: International Journal of Mathematical, Engineering & Management Sciences Type: main |
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