Advanced International Journal for Research

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FarmGPT: An Explainable AI Powered Smart Agriculture System for Crop Recommendation and Decision Support

Author(s) Mr. Arun T, Ms. Pooja Sri M, Prof. Priya K
Country India
Abstract Agriculture remains a critical pillar of the global economy, yet farmers face persistent challenges in optimal crop selection due to dynamic environmental
conditions and a lack of interpretable guidance. While machine learning (ML) models offer high predictive accuracy, their black-box nature limits adoption among nontechnical users. This paper presents FarmGPT, an explainable AI-based crop recommendation and agricultural decision support system. FarmGPT employs a Random Forest classifier achieving 98.2% accuracy, trained on agro
meteorological parameters including nitrogen (N), phosphorus (P), potassium (K), temperature, humidity, soil pH, and rainfall. SHAP (SHapley Additive exPlanations) is integrated to provide transparent, feature-level justifications
for each recommendation. The system further incorporates a CNN-based plant disease detection module trained on the PlantVillage dataset, a regression-based yield prediction engine, real-time weather integration via OpenWeatherMap API, and an INR profit estimation module. Built on a full
stack Flask–Node.js–React architecture, FarmGPT delivers comprehensive, real-time agricultural intelligence through a conversational interface. Evaluation results confirm 98.2% crop recommendation accuracy, 90% disease detection
accuracy in user trials, and a System Usability Scale (SUS) score of 79.3.
Keywords Explainable AI, Crop Recommendation, Random Forest, SHAP, CNN, Plant Disease Detection, Precision Agriculture, Smart Farming
Field Engineering
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-06-21
DOI https://doi.org/10.63363/aijfr.2026.v07i03.6414
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