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The aim is to create an integrated system that detects early crop stress to help farmers make informed decisions.

March 29, 2026Open Access

Integrated Smart Crop Management System Using Explainable Artificial Intelligence (XAI)

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The aim is to create an integrated system that detects early crop stress to help farmers make informed decisions.
Developed an AI/ML-based system for early crop stress detection.
Processed multiple data types including soil parameters, weather forecasts, and farmer notes.
Utilized ensemble models like Random Forest and XGBoost optimized through grid search and cross-validation.
Implemented a RESTful backend for data handling and model execution with XAI integration.
Achieved accurate stress forecasts for the next 7 to 10 days.
Provided interpretable predictions using SHAP methods on benchmark datasets.
Demonstrated effectiveness for resource-limited users with multiple data entry options.

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Research develops an AI-based system detecting early crop stress in farming, indicating improved yield management.

M.K et al. (2026) studied this question.

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