IoT and Machine Learning Driven Intelligent Warehouse Monitoring: An Expanded Case Study

The digital transformation of agricultural storage systems is critical for minimizing post-harvest losses, particularly in regions like Kazakhstan that experience harsh climatic conditions and connectivity challenges. This paper proposes an Internet of Things (IoT)-based monitoring system integrating LoRaWAN-enabled sensors, edge computing modules, and three distinct machine learning (ML) algorithms—Long Short-Term Memory (LSTM), Support Vector Machine (SVM), and Random Forest (RF)—to manage key warehouse environmental parameters, including temperature, humidity, and CO2. The conduct was made a thorough literature review that covers most relevant studies. It highlights gaps in cost-effective IoT solutions for its implementations, particularly in Kazakhstan and other similar Central Asian regions. The simulated pilot deployments in two warehouses (1 and 2) confirm that the proposed solution reduces spoilage by 25–30% and yields a payback period of under one year. It’s been, also, discussed how IoT implementation along with ML model connectivity may further extend real-time monitoring and predictive analytics for smart warehouses with regard to Industry 4.0 environments.