Explainable Fertilizer Recommendation An XGBoost–SHAP Framework with Semantic Knowledge Retrieval for Precision Agriculture

Registration ID: IJNRD_328234

Published ID: IJNRD2609057

: https://doi.org/10.56975/ijnrd.v11i9.328234

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Dr. Shweta Marigoudar, Jyothika B R, Reethu S

Sentence transformers, machine learning interpretability, XGBoost, SHAP, fertilizer recommendation, precision agriculture, and explainable AI.

Crop yield, soil health, and input costs are all directly impacted by the choice of fertilizer, but the majority of machine-learning-based recommendation systems operate as opaque predictors with no agronomic rationale for their results. This paper introduces an explainable fertilizer recommendation system that combines post-hoc interpretability and semantic text retrieval with a gradient-boosted decision tree classifier. An XGBoost multi-class classifier is trained over eleven fertilizer categories using soil and climate descriptors (temperature, moisture, rainfall, pH, nitrogen, phosphorous, potassium, organic carbon, soil type, and crop type). In order to bridge the gap between numerical prediction and practical explanation, SHapley Additive exPlanations (SHAP) are calculated using a TreeExplainer to quantify each feature's contribution at both the global and per-prediction level. Additionally, a Sentence-Transformer-based knowledge base retrieves an agronomic comment related to the suggested fertilizer that is readable by humans. The model achieves 98. 39 percent accuracy, 98. 38 percent weighted F1-score, 98. 45 percent precision, 98. 39 percent recall, and 99. 27 percent ± 0. 27 percent 5-fold stratified cross-validation accuracy on a held-out test split. While local explanations demonstrate that individual predictions are often controlled by a single dominant nutrient deficiency or surplus, in line with accepted agronomic reasoning, SHAP analysis identifies phosphorous, soil pH, and potassium as the dominant global predictors. The resulting pipeline offers a clear, feature-level, natural-language justification that can be used in decision-support tools for farmers and agricultural extension agents in addition to an accurate recommendation.

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Dr. Shweta Marigoudar, Jyothika B R, & Reethu S (September-2026). Explainable Fertilizer Recommendation An XGBoost–SHAP Framework with Semantic Knowledge Retrieval for Precision Agriculture. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 11(9), a559-a566. https://doi.org/10.56975/ijnrd.v11i9.328234

Volume 11 Issue 9, September-2026

Pages : a559-a566

Paper Reg. ID: IJNRD_328234

Published Paper Id: IJNRD2609057

Research Area: Other area not in list

Author Type: Indian Author

Country: Davanagere, Karnataka, India

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Crossref DOI: https://doi.org/10.56975/ijnrd.v11i9.328234

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

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