An Intelligent Decision-Support Framework for Real-Time Crop Stress Prediction and Precision Intervention

Abstract

Precision agriculture faces a critical challenge: detecting crop stress before irreversible physiological damage occurs. Traditional monitoring methods—visual inspection and laboratory analysis—are reactive, time-consuming, and subjective, often detecting stress only after visible symptoms emerge. This study presents an AI-based prototype for real-time environmental stress prediction in greenhouse agriculture, integrating multi-sensor data acquisition, a modular prediction engine, and an interactive monitoring dashboard. The system was developed in Python 3.14 using FastAPI for REST API services, Streamlit for real-time visualization, and scikit-learn and OpenCV for machine learning and computer vision components. Six stress scenarios were evaluated: healthy, water stress, heat stress, nutrient stress, acid stress, and random multi-stress conditions. Results demonstrated the engine's ability to classify multiple coexisting stresses with confidence scores ranging from 0.70 to 0.85, while maintaining decision stability under extreme environmental fluctuations (soil moisture: 5%–85%; temperature: 15°C–45°C) without generating false-positive alerts. The temporal analysis over a 60 s continuous monitoring window confirmed sub-2 s end-to-end latency and complete traceability of every decision. Unlike single-label classifiers, the proposed engine preserves the multi-label nature of stress states, providing actionable probabilistic outputs aligned with the biological reality of field conditions. The layered architecture ensures seamless migration from simulated to physical sensors (Raspberry Pi, DHT22, capacitive soil moisture, pH, NPK, camera). Future work will address hardware integration, edge optimization, field deployment in commercial greenhouses, and closed-loop autonomous control. The prototype offers a reproducible, open, and extensible foundation for AI-driven precision agriculture.