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

• URL: http://arxiv.org/abs/2509.12274v1
• Date: Sun, 14 Sep 2025 03:48:22 GMT
• Title: Developing an aeroponic smart experimental greenhouse for controlling irrigation and plant disease detection using deep learning and IoT
• Authors: Mohammadreza Narimani, Ali Hajiahmad, Ali Moghimi, Reza Alimardani, Shahin Rafiee, Amir Hossein Mirzabe,
• Abstract summary: The primary objective of this research study was to develop and test a smart aeroponic greenhouse on an experimental scale.<n>The status of Geranium plant and environmental conditions are continuously monitored through the integration of the internet of things (IoT) and artificial intelligence (AI)
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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