Multiobjective Optimization and Decision-Making of Low-Carbon Environmental Parameters in AIoT-Enabled Controlled Environment Agriculture

Co-optimizing air temperature (AT) and CO2 concentration (CO2) for AIoT systems in controlled environment agriculture (CEA) has raised sustained attentions to balance energy efficiency and crop productivity. To tackle this issue, we propose a region-based co-optimization strategy that combines crop-environment interaction modeling with a multi-objective optimization algorithm to obtain the optimal ranges of AT and CO2. Experimental data are collected by measuring single-leaf photosynthetic rate (AL) in lettuce cultivated under diverse environmental conditions. AL is used as the primary crop growth indicator and modeled using support vector regression (SVR). Based on the prediction model, novel fitness functions and constraints are established considering biomass accumulation, energy consumption, and AIoT system actions. Subsequently, a hybrid multi-stage method integrating the non-dominated sorting genetic algorithm II (NSGAII) and differential evolutionary (DE) algorithm is employed to determine the non-dominated solution (NDS) sets for different conditions. Finally, the optimal solution within the NDS sets is calculated using the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method constrained by curvature feature. The rationalities of the proposed methods are verified by lettuce and cucumber samples. Experimental validation with lettuce cultivation across multiple environments demonstrates significant advantages over conventional threshold-based methods: 18.0% improvement in carbon use efficiency, 40.7% reduction in AIoT system operations, and 94.7% photosynthetic efficiency retention. This research achieves a groundbreaking integration of agronomic models, intelligent algorithms, and AIoT systems, establishing a novel approach for sustainable agricultural management that simultaneously optimizes production efficiency and resource utilization.