IoT-based monitoring and control for optimized plant growth in smart greenhouses using soil and hydroponic systems

Context:

Agriculture is under mounting pressure from climate change, natural resource depletion, and the urgent need for global food security. Smart technologies, particularly IoT-enabled greenhouse systems, offer a promising pathway to sustainably intensify crop production. However, achieving seamless real-time monitoring, autonomous control, and fault detection remains technically complex, especially in remote or off-grid regions with limited infrastructure and unstable energy supply.

Objective:

This study aims to develop a fully autonomous, intelligent greenhouse system that integrates real-time environmental monitoring, adaptive control, and embedded fault diagnosis. A key focus is on enabling continuous and reliable operation in off-grid conditions through solar energy autonomy. The system is designed to enhance crop productivity, energy efficiency, and resilience across a range of agricultural contexts, from smallholder plots to commercial-scale operations.

Methods:

The system integrates a distributed sensor network with a centralized control platform and mobile interface to monitor key agronomic and technical variables, including temperature, humidity, $C{O}_2$concentration, light intensity, irrigation flow, nutrient composition, and electrical parameters. To enable predictive maintenance, an anomaly detection module based on the Isolation Forest algorithm was implemented and trained on synthetically generated multivariate time series data representing realistic fault scenarios (e.g., irradiance drop, overheating, sensor drift, voltage imbalance). The algorithm’s performance was quantitatively assessed using confusion matrices, receiver operating characteristic (ROC) curves, and classification metrics, achieving high anomaly detection accuracy. Predefined alert thresholds were assigned to each monitored parameter and integrated with machine learning outputs to enhance diagnostic reliability. The system also features a solar energy harvesting subsystem with continuous tracking of photovoltaic voltage, current, energy yield, and battery state-of-charge, supporting fully autonomous, off-grid operation.

Results and Conclusions:

Controlled-environment testing demonstrated the system’s ability to autonomously and precisely regulate greenhouse climate parameters while ensuring operational continuity under simulated fault and energy fluctuation scenarios. The integrated fault detection module showed high diagnostic accuracy, and visual analytics were incorporated to support interpretability by non-specialist users. Although still in the pre-deployment phase, these results confirm the system’s technical robustness and readiness for field trials. A comparative analysis with existing solutions emphasizes the system’s unique contribution—namely, the integration of real-time environmental control with embedded energy self-diagnosis, addressing a critical gap in current smart greenhouse technologies.

Significance:

This study presents a novel, integrated smart greenhouse architecture that addresses key challenges in sustainable agriculture, including energy autonomy, system resilience, and intelligent environmental control. By combining real-time monitoring, machine learning–based fault detection, and solar-powered operation, the proposed system offers a scalable and modular solution adaptable to diverse farming contexts. Its design is particularly suited to regions facing energy instability and climate stress, contributing to the broader goal of climate-resilient, precision agriculture and global food security.