A Hybrid IoT and Machine Learning Framework for Smart Greenhouse Automation in Sustainable Agriculture

Venkataramanan V; Mrunalini Pimpale; Vijay Kapure; Pankaj Mishra; Aarya Rokade; Tulsi Bhushan; Jitendra Singh

doi:10.54392/irjmt2545

Authors

Venkataramanan V Department of Information Technology, K. J. Somaiya School of Engineering (formerly K. J. Somaiya College of Engineering), Somaiya Vidyavihar University, Mumbai, India. Author https://orcid.org/0000-0001-7270-3221
Mrunalini Pimpale Department of EXTC, D.J Sanghvi College of Engineering, Vile Parle, Mumbai, India. Author https://orcid.org/0000-0002-9099-4680
Vijay Kapure Department of Applied Science and Humanities, Xavier Institute of Engineering, Mahim, Mumbai, India. Author https://orcid.org/0000-0002-6543-7647
Pankaj Mishra Department of Information Technology, K. J. Somaiya School of Engineering (formerly K. J. Somaiya College of Engineering), Somaiya Vidyavihar University, Mumbai, India. Author https://orcid.org/0000-0001-9331-1841
Aarya Rokade Department of EXTC, D.J Sanghvi College of Engineering, Vile Parle, Mumbai, India. Author
Tulsi Bhushan Department of EXTC, D.J Sanghvi College of Engineering, Vile Parle, Mumbai, India. Author https://orcid.org/0009-0001-4375-086X
Jitendra Singh Department of EXTC, D.J Sanghvi College of Engineering, Vile Parle, Mumbai, India. Author https://orcid.org/0009-0001-9619-7826

DOI:

https://doi.org/10.54392/irjmt2545

Keywords:

Smart Greenhouse, IoT, Automation, Precision Agriculture, Sustainable Farming

Abstract

Growing demand for green farming has expedited the progress of smart greenhouse technology. This paper presents a smart greenhouse system combining Internet of Things (IoT) platforms with autonomous controls and artificial intelligence (AI) to improve the efficiency of crop production and minimize the utilization of resources. Greenhouse management under traditional technique is usually influenced by ineffective controls and dependency on human intervention, which includes wastage of resources and reduced yields. The newly proposed system provides real-time tracking of climate factors—temperature, humidity, light intensity, and soil moisture—by means of a network of sensors. Irrigation, ventilation, and lighting functions are managed by machine learning (ML) algorithms that predict optimal climatic conditions. The system utilizes cost-effective ESP8266 and ESP32 microcontrollers and MQTT for data transmission at low costs. Experimental validation confirmed a 30% reduction in water consumption and 15% increase in crop yield, and energy efficiency improved to traditional systems. The system is flexible for different scales of greenhouses and types of crops. The results confirmed that the integration of IoT with AI provides a scalable, green solution to green farming and resource management.

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