A Study on Modular Smart Plant Factory Using Morphological Image Processing

• Main Authors: Bong-Hyun Kim, Joon-Ho Cho
• Format: Article
• Language: English
• Published: MDPI AG 2020-10-01
• Series: Electronics
• Subjects: LED module; smart IoT; image process; solar module; convergence
• Online Access: https://www.mdpi.com/2079-9292/9/10/1661

This paper is a study on a modular smart plant factory integrating intelligent solar module, LED module with high efficiency for plant growth, IoT module control system and image processing technology. The intelligent sun and modules have a corrugated structure, and the angle of the module can be adjusted to obtain a large amount of power generation. It is fully foldable for wider angles during the day and module protection at night. The LED module is designed and manufactured to distribute energy evenly over the entire wavelength range so that high efficiency can be obtained. The control system with IoT convergence technology enables control of all parts related to plant growth such as angle control of solar modules, LED lighting control, temperature/humidity control, and fan control. In particular, the control method is programmed to be controlled by a computer monitoring system and a smartphone app, so there are few places. In addition, this paper developed an image processing algorithm to extract the growth information of lettuce grown in the plant factory. The acquired images were separated into R, G, and B images using Matlab software. The applied algorithms are k-mean and improved morphological image processing. By applying this method, we can determine the area calculation and shipping of lettuce seedlings. As a result of the fusion and application of solar modules, LED modules, and IoT modules, information on plant growth and status was confirmed.