IOT based Automated Greenhouse Using Machine Learning Approach



Artificial Intelligence, Computer Vision, Data-analytics, Machine Learning, Neural Network


Focusing on the effect of universal food insecurity, over 60% of sub-Saharan countries are predicted to be in a state of malnourishment and yet several farming places are under drought state. The climatic condition is believed to be biannual dry seasons which is very difficult for farmers to cultivate crops due to shortage of water and poor soil fertility. Yet heavy rainfall is still a great threat for the farmers since it devastates cash crops. The use of a smart greenhouse with Artificial Intelligence to grow and protect plants in both dry and wet seasons and reduce labor-intensive human tasks and automate pervasive data analytics of daily plant status can surprisingly boost food security. Here we present a fully automated greenhouse system with artificial intelligence embedded in it that uses around 10,000 plant images in it that initially nurture plants under optimum atmospheric conditions by taking real-time decisions, detecting any kind of illness, and interestingly notifying the stage of fruit ripeness. By implementing a neural network-based computer vision approach we were able to keep track of the health status of the plants caused by several microorganisms. The obtained predictions and results accurately verify how machine learning can be used to increase gross national food security by implementing such systems in multiple farming areas without prior human involvement.


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Author Biographies

Nafees Akhter Farooqui, DIT University Dehradun

Research Scholar, School of Computing, DIT University, Dehradun, India

Amit Kumar Mishra, DIT University Dehradun

Dr. Amit Kumar Mishra is working as an Associate Professor & Head-IT of the School of Computing at DIT University, Dehradun, India.

Ritika Mehra, Dev Bhoomi Uttarakhand University, Dehradun

Dr. Ritika Mehra is working as a Professor and Dean of the School of Computer Science & Engineering at Dev Bhoomi Group of Institutions, Dehradun, Uttarakhand.


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Block diagram of smart greenhouse




How to Cite

N. A. Farooqui, A. K. Mishra, and R. Mehra, “IOT based Automated Greenhouse Using Machine Learning Approach”, Int J Intell Syst Appl Eng, vol. 10, no. 2, pp. 226–231, May 2022.



Research Article