A Systematic Literature Review on LSTM–MPC–GP Integration for Climate-Adaptive Decision Support Systems and Evaluation in Precision Agriculture
TL;DR
Findings indicate that LSTM-based climate prediction models achieve average R² > 0.9, while MPC enhances irrigation efficiency by up to 30% under uncertain conditions, while IoT infrastructures enable real-time communication and feedback across distributed farm networks, reducing latency and operational costs.
Abstract
Climate change poses a risk to sustainable agriculture through interference with irrigation timing, crop production, and resource management. Combining Artificial Intelligence with MPC and IoT can offer a basis for building climate-resilient decision support systems for precision agriculture. This study presents a Systematic Literature Review (SLR) of research published between 2019 and 2025 that investigates the synergistic use of Long Short-Term Memory LSTM neural networks, MPC, and IoT architectures for adaptive agricultural management. Following PRISMA and Kitchenham guidelines, 147 studies were screened, and 28 were analyzed using a ten-criterion Quality Assessment Checklist (QAC) with Weighted Quality Score (WQS) evaluation. Findings indicate that LSTM-based climate prediction models achieve average R² > 0.9, while MPC enhances irrigation efficiency by up to 30% under uncertain conditions. IoT infrastructures enable real-time communication and feedback across distributed farm networks, reducing latency and operational costs. The synthesis highlights an emerging shift toward closed-loop predictive–adaptive architectures. Despite significant progress, real-time field deployment remains limited due to computational and interoperability challenges. This SLR proposes a conceptual framework uniting LSTM forecasting, MPC optimization, and IoT-based monitoring for sustainable climate-resilient precision agriculture.