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Mechanized Functioning of Tomato and Lettuce Greenhouses Using IoT and Machine Learning Techniques
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Author(s): Shipra Saraswat (ABES Institute of Technology, Ghaziabad, India), Rijwan Khan (ABES Institute of Technology, Ghaziabad, India), Ayush Gupta (ABES Institute of Technology, Ghaziabad, India)and Abhay Mehta (ABES Institute of Technology, Ghaziabad, India)
Copyright: 2023
Pages: 19
Source title:
Convergence of Cloud Computing, AI, and Agricultural Science
Source Author(s)/Editor(s): Avinash Kumar Sharma (ABES Institute of Technology, India), Nitin Chanderwal (University of Cincinnati, USA)and Rijwan Khan (Galgotias University, India)
DOI: 10.4018/979-8-3693-0200-2.ch013
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Abstract
This chapter explores the mechanized functioning of tomato and lettuce greenhouses through the integration of internet of things (IoT) and machine learning techniques. It highlights the transformative impact of these technologies on the agricultural industry, enabling enhanced productivity, optimized resource management, and improved crop yield. By leveraging IoT, greenhouses become interconnected systems that allow real-time monitoring and control of environmental parameters. Machine learning (ML) algorithms examine data collected from sensors and historical records to predict crop growth patterns, disease outbreaks, and pest infestations. This enables proactive decision-making and efficient resource allocation. The adoption of IoT in greenhouse farming not only automates operations but also promotes sustainable practices, reducing labor costs and optimizing resource utilization. Overall, this chapter showcases the potential of IoT and ML technologies in revolutionizing greenhouse farming for increased efficiency, sustainability, and food security of tomato and lettuce crops.
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