Please use this identifier to cite or link to this item:
Title: Deep Learning and Internet of Things Integrated Farming during COVID-19 in India
Authors: Abhishek, P.
Ramesh, V.
Keywords: Deep learnin
data driven agriculture
irrigation infrastructure
Issue Date: 30-Sep-2020
Publisher: ХОГОКЗ / KRPOCH Publishing
Citation: Abhishek, P., Ramesh, V. (2020). Deep Learning and Internet of Things Integrated Farming during COVID-19 in India. International Journal of Education and Science, 3(3), 10-18. doi:10.26697/ijes.2020.3.2
Series/Report no.: International Journal of Education аnd Science, 3(3)
Abstract: Background and Aim of Study: Deep learning and Internet of things (IoT) technologies have great potential for their application in various fields, including agriculture. Agriculture is a central pillar of the Indian economy. Agriculture is largest livelihood provider in India. Agriculture employed more than 50% of the Indian work force and contributed 17–18% to country’s GDP. Indian agriculture sector has been facing several challenges because of COVID-19 restrictions. Outbreak of corona virus in India and the consequent lockdown, unfortunately, also coincided with the country’s peak harvesting time of a variety of crops of the season. Across India, a massive agricultural crisis is due to COVID-19 shutdown. The aim of the study: to explore the possibilities of using Deep learning and IoT technologies as a tool to handle many problems in agriculture domain such as lack of irrigation infrastructure, market infrastructure and transport infrastructure etc. Materials and Methods: We have studied various problems faced by Indian farmers during this lockdown and various steps taken by Indian government to tackle this global pandemic of COVID-19. This study introduced possible solutions for improvement by using Deep learning based Internet of things ecosystem that helps in gathering information from farmers such location-based information, crop health information and environmental constraints. Results: We proposed an IoT based agriculture framework to monitor and analyse crop health by using Deep learning remotely. This framework promotes a fast development of agricultural modernization, realize smart agriculture and effectively solve the problems concerning agriculture. Our research findings indicate that Deep learning provides high accuracy, outperforming existing commonly used data processing techniques. Conclusions: Data-driven agriculture, with the help of internet of things and Deep learning techniques, sets the grounds for the sustainable agriculture of the future. This study proposed the future advanced farm management systems through Deep learning and IoT technologies to solve various problem faced by Indian farmers during COVID-19 pandemic.
ISSN: Print ISSN: 2618-0553; Online ISSN: 2618-0561
Other Identifiers: DOI: 10.26697/ijes.2020.3.2
Appears in Collections:Vol. 3, No. 3, 2020

Files in This Item:
File Description SizeFormat 
ijes.2020.3.2.pdf1.08 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.