Iot Based Low-Cost Precision Indoor Farming

Madhu Lekha Guntaka, Purdue University

Abstract

There is a growing demand for indoor farm management systems that can track plant growth, allow automatic control and aid in real-time decision making. Internet of Thing (IoT)-based solutions are being applied to meet these needs and numerous researchers have created prototypes for meeting specific needs using sensors, algorithms, and automations. However, limited studies are available that report on comprehensive large-scale experiments to test various aspects related to availability, scalability and reliability of sensors and actuators used in low-cost indoor farms. The purpose of this study was to develop a low-cost, IoT devices driven indoor farm as a testbed for growing microgreens and other experimental crops. The testbed was designed using off-the-shelf sensors and actuators for conducting research experiments, addressing identified challenges, and utilizing remotely acquired data for developing an intelligent farm management system. The sensors were used for collecting and monitoring electrical conductivity (EC), pH and dissolved oxygen (DO) levels of the nutrient solution, light intensity, environmental variables, and imagery data. The control of light emitting diodes (LEDs), irrigation pumps, and camera modules was carried out using commercially available components. All the sensors and actuators were remotely monitored, controlled, and coordinated using a cloud-based dashboard, Raspberry Pis, and Arduino microcontrollers. To implement a reliable, real-time control of actuators, edge computing was used as it helped in minimizing latency and identifying anomalies. Decision making about overall system performance and harvesting schedule was accomplished by providing alerts on anomalies in the sensors and actuators and through installation of cameras to predict yield of microgreens, respectively. A split-plot statistical design was used to evaluate the effect of lighting, nutrition solution concentration, seed density, and day of harvest on the growth of microgreens. This study complements and expands past efforts by other researchers on building a low cost IoT-based indoor farm. While the experience with the testbed demonstrates its real-world potential of conducting experimental research, some major lessons were learnt along the way that could be used for future enhancements.

Degree

M.Sc.

Advisors

Aggarwal, Purdue University.

Subject Area

Agriculture|Nutrition|Artificial intelligence|Horticulture|Information Technology|Web Studies

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