Developing an automated IoT-based indoor farming system with optimal LED light quality control for leafy vegetable growth

• Main Authors: Bamba, Andre' Mitchelle L., Corpus, Elmark Joseph L., Enghoy, Martin Adrian C., Gabay, Hanna Camille B.
• Format: text
• Language: English
• Published: Animo Repository 2023
• Subjects: Internet in agriculture; Internet of things; Artificial light gardening; Computer Engineering
• Online Access: https://animorepository.dlsu.edu.ph/etdb_ece/29; https://animorepository.dlsu.edu.ph/context/etdb_ece/article/1037/viewcontent/Developing2_an_Automated_IoT_based_Indoor_Farming_System_with_Opti.pdf
• Institution: De La Salle University

With the Internet of Things (IoT) smart farms can be developed which enables for more precise monitoring and control over your crops, enhancing productivity and efficiency. Lighting also plays an important role in farming and plant growth, which makes lighting systems a crucial part of a smart farming system. Existing systems lack dynamic control over their lighting systems, and traditional lighting systems or natural light has been shown to be lacking compared to specific light treatment when cultivating crops. This thesis aims to develop an automated IoT-based Indoor Farming System with the control of the LED Light Quality that is optimal for lettuce, spinach, and green amaranth integrated with existing indoor smart farming monitoring and control features using sensors and actuators. The proposed indoor farming system comprises main controllers, water treatment, and light treatment modules, which are equipped with monitoring and control features using sensors and actuating devices. The Raspberry Pi 3B+ acts as the web-hosting device and the central unit for running the automation program, establishing a medium of communication between the monitoring and control mechanisms with the web application and treatment modules. After running tests on the prototype the results have shown that a light control system was successfully made where you can adjust the light intensity and the light spectrum of the LED lights through the web application. In addition the web application successfully displays real-time information from the sensors and responds accordingly to user input, allowing for monitoring and control. A cost-benefit analysis was conducted and it can be seen that without hiring an operator for one unit cultivating lettuce for a year not only is economically feasible but also gives a return on investment. The crop yield was also measured and it can be seen that our objectives of a higher crop yield were met. In conclusion, a smart indoor hydroponics system with optimal LED light control is a viable farming method. Index Terms— IoT, Indoor Farming System, Hydroponics, Optimal LED, Light Quality