Explainable AI-driven interpretation of environmental drivers of tomato fruit expansion in smart greenhouses using IoT sensing

Tomato fruit expansion is a key physiological process that determines fruit size, marketability, and yield, yet its quantitative and threshold-based response to microclimatic factors in smart greenhouses has been insufficiently studied. This study develops an IoT-driven sensing framework combined with explainable artificial intelligence (XAI) to interpret the environmental drivers of fruit expansion. A robust environmental monitoring system continuously captured key factors including air and soil temperature, humidity, light intensity, CO₂ concentration, soil moisture, and soil electrical conductivity. These variables were fed into a Random Forest regression model enhanced with SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDPs) for interpretability. Results revealed that soil temperature (~ 21.8 °C), light intensity, and soil electrical conductivity were the most influential drivers of fruit expansion, each exhibiting distinct threshold behaviors, and the proposed IoT-XAI framework achieved R² = 0.82 with an MSE of 0.0046, confirming both predictive accuracy and interpretability. Our approach transforms raw sensor data into actionable insights for precision climate and fertigation management, supporting sustainable smart agriculture through interpretable machine learning.