Explainable AI and IoT-Based Precision Agriculture Framework to Minimize CO2 Emissions

The global CO2 emissions are brought about by ineffective usage of resources and the absence of real-time monitoring of the environment. To solve this, the explainable AI approach that includes ionized IoT is suggested so that precision agriculture could be realized with a minimal carbon footprint. The system is based on real-time sensor data concerning soil, weather, and crop tending, processed through a hybrid Temporal Convolutional Network (TCN) and LSTM model, and is explainable with a SHAP-based algorithm that fully explains the decisions made. The route and input optimization is achieved using Ant Colony Optimization to save on fuel utilization. The average daily reductions of CO2 emissions, the efficiency of optimization in resources, and CO2 prediction error are 28.6%, 83.2% and 0.45 kg/ha when evaluated experimentally. Besides, the system latency is ensured to be low, 180 ms, providing responsiveness in real-time. Compared to the traditional models, the suggested framework presents superior performance on the grounds of emission sensitivity, the interpretability of the model, and scalability. The results indicate that explainable AI has the potential to support an environmentally sustainable and intelligent farming system.