Urban Farming Growth Monitoring System Using Artificial Neural Network (ANN) and Internet of Things (IOT)

• Published in: Journal of Advanced Research in Applied Sciences and Engineering Technology
• Main Author: Abdul Kader M.M.M.; Mansor M.N.; Mustafa W.A.; Razali Z.B.; Gunny A.A.N.; Setumin S.; Osman M.K.; Idris M.; Akbar M.F.; Kunasakaran P.; Zainol M.Z.; Mizam N.S.S.
• Format: Article
• Language: English
• Published: Semarak Ilmu Publishing 2025
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85191515587&doi=10.37934%2faraset.43.1.144159&partnerID=40&md5=3a3813b96700e1ae54b66aff7c133d73

As an introduction to this project, the growth-related traits, such as above-ground biomass and leaf area, are critical indicators to characterize the growth of indoor lettuce plants. Currently, non-destructive methods for estimating growth-related traits are subject to limitations in that the methods are susceptible to noise and heavily rely on manually designed features. It is also one of the problem statements in this project. Based on this project the next problem is manual control of nutrients may cause quality issues to the lettuce plant. If the nutrient supply is too much or less, it will disturb the growth of the lettuce plant either the lettuce plant is dead or stunted. This project is about urban farming growth monitoring system using Artificial Neural Network (ANN) and Internet of Things (IoT). In this project, a method for monitoring the growth of indoor lettuce plants was proposed by using digital images and an ANN using Deep Learning Architecture. DLA is mostly developed by the software of MATLAB or Python to insert and run the coding. DLA is mostly used for image detection, pattern recognition, and natural language processing through the graph for Neural Network. Next, the Internet of Things (IoT) is a medium to store images of indoor lettuce plant growth into the Cloud (Google Drive). Furthermore, it takes indoor lettuce plant images as the input, an ANN was trained to learn the relationship between images and the corresponding growth-related traits with other fixed parameters. The pH level parameters were controlled by other fixed parameters to take the images of indoor lettuce plant growth. The parameters used in this project are temperature and humidity. This helps to compare the results of Artificial Neural Network (ANN), widely adopted methods were also used. Concisely, this project is expected to develop the Deep Learning Architecture using an Artificial Neural Network (ANN) with digital images as a robust tool for the monitoring of the growth of indoor lettuce plants every 30 minutes per day. Generally, focused on an urban farming growth monitoring system using Artificial Neural Network (ANN) and the Internet of Things (IoT). © 2025, Semarak Ilmu Publishing. All rights reserved.