Developing an aeroponic smart experimental greenhouse for controlling irrigation and plant disease detection using deep learning and IoT

2025-09-13

AI Summary

To address inefficient management in aeroponic systems caused by delayed detection of water stress and plant diseases, this study develops an intelligent experimental greenhouse system integrating Internet of Things (IoT) and deep learning. The system employs multi-source environmental sensors for real-time monitoring and closed-loop control of temperature, humidity, and nutrient solution flow rate. A lightweight, VGG-19–based image recognition model is proposed for end-to-end detection of drought stress and rust disease on geranium leaves. Experimental results demonstrate a classification accuracy of 92%, outperforming InceptionV3 and InceptionResNetV2, while offering superior timeliness and consistency compared to human expert assessment. This work pioneers the first fully automated “perceive–analyze–decide–act” pipeline in aeroponics, establishing a reusable AIoT paradigm for smart controlled-environment agriculture.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Efficient ML / Green AI
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Controlling environmental conditions and monitoring plant status in greenhouses is critical to promptly making appropriate management decisions aimed at promoting crop production. The primary objective of this research study was to develop and test a smart aeroponic greenhouse on an experimental scale where the status of Geranium plant and environmental conditions are continuously monitored through the integration of the internet of things (IoT) and artificial intelligence (AI). An IoT-based platform was developed to control the environmental conditions of plants more efficiently and provide insights to users to make informed management decisions. In addition, we developed an AI-based disease detection framework using VGG-19, InceptionResNetV2, and InceptionV3 algorithms to analyze the images captured periodically after an intentional inoculation. The performance of the AI framework was compared with an expert's evaluation of disease status. Preliminary results showed that the IoT system implemented in the greenhouse environment is able to publish data such as temperature, humidity, water flow, and volume of charge tanks online continuously to users and adjust the controlled parameters to provide an optimal growth environment for the plants. Furthermore, the results of the AI framework demonstrate that the VGG-19 algorithm was able to identify drought stress and rust leaves from healthy leaves with the highest accuracy, 92% among the other algorithms.

Problem

• Developing IoT-controlled aeroponic greenhouse for optimized irrigation
• Detecting plant diseases using deep learning algorithms
• Monitoring environmental conditions for informed crop management decisions

Innovation

• IoT platform for environmental control
• AI disease detection using VGG-19
• Continuous monitoring of plant conditions