The Rise of Intelligent Greenhouses Using IoT, AI, and Robotics for Sustainable Farming

The review shows that smart greenhouses using IoT, artificial intelligence, and robotics can greatly improve crop yields, resource efficiency, and sustainability by enabling real-time monitoring, intelligent decision-making, and automation. However, widespread adoption is still limited by high costs, data gaps, and weak integration between these technologies, pointing to the need for simpler and more scalable solutions.

This review, produced by researchers from Sultan Qaboos University in Oman, spanning the Department of Soils, Water, and Agricultural Engineering, the Remote Sensing and GIS Research Center, and the Department of Mechanical and Industrial Engineering, examines how advanced technologies are reshaping greenhouse agriculture to meet global food challenges. As climate change, population growth, land degradation, and water scarcity intensify pressure on food systems, greenhouses are increasingly seen as a practical solution. By creating controlled growing environments, they allow crops to be produced year-round with higher yields, better quality, and lower water and fertilizer use than traditional open-field farming. The article positions greenhouses as a cornerstone of modern, sustainable agriculture, particularly in arid and resource-constrained regions.

From Traditional to Smart Greenhouses

The paper explains how greenhouses have evolved from manually managed structures into sophisticated, technology-driven systems. In the past, farmers relied heavily on experience and constant physical monitoring to control temperature, humidity, and light. Today, smart greenhouses automate these processes using sensors, controllers, and software, reducing labour needs and improving consistency. Research reviewed in the article shows that upgrading conventional greenhouses with intelligent control systems can significantly cut energy use and production costs while stabilizing crop performance. However, the transition is not simple, as it requires reliable hardware, advanced control algorithms, and coordination between multiple technologies.

The Role of IoT in Monitoring and Control

A major focus of the review is the Internet of Things (IoT), which forms the digital backbone of smart greenhouses. IoT systems connect sensors that measure temperature, humidity, soil moisture, light, and carbon dioxide to communication networks and cloud or edge platforms. This setup allows farmers to monitor conditions in real time and adjust them remotely. The article reviews many examples, from low-cost experimental systems to more advanced commercial designs using technologies such as Wi-Fi, LoRa, NB-IoT, Zigbee, and MQTT. These systems often improve water efficiency, reduce crop stress, and support early detection of problems. At the same time, the authors point out common weaknesses, including high setup costs, maintenance demands, data security concerns, and limited testing outside controlled or short-term experiments.

How Artificial Intelligence Helps Crops Grow

Artificial intelligence, especially machine learning and deep learning, is presented as the “brain” of intelligent greenhouses. AI models analyze large volumes of data to predict climate conditions, estimate yields, detect diseases, and monitor plant growth. The review highlights the widespread use of deep learning models such as YOLO, Faster R-CNN, EfficientNet, and ResNet, many of which achieve very high accuracy in identifying plant diseases or recognizing fruits. These tools can match or even exceed human performance in specific tasks. Still, the authors caution that many models are trained on small, crop-specific datasets collected under ideal conditions, which limits their ability to work reliably across different crops, seasons, and greenhouse environments.

Robots and the Road Ahead

The article also explores how robotics is bringing physical automation into greenhouses. Robots are increasingly used for harvesting, spraying, disease inspection, plant handling, and environmental monitoring. Equipped with cameras, sensors, and AI models, these machines can navigate greenhouse rows and perform precise tasks with minimal human input. Several case studies show promising results, such as robots harvesting fruits or detecting diseases with high accuracy. However, widespread adoption remains slow due to high costs, mechanical complexity, and sensitivity to crop layout and lighting conditions.

The review stresses that while IoT, AI, and robotics have each advanced rapidly, they are rarely fully integrated into unified systems. Data limitations, energy constraints, and lack of standard benchmarks remain major obstacles. The authors argue that future research should focus on simpler, more affordable, and better-integrated solutions. If these challenges are addressed, intelligent and autonomous greenhouses could play a vital role in delivering sustainable, high-quality food production in an increasingly uncertain world.