The Future of Agriculture: IoT, AI and Blockchain Technology for Sustainable Farming

Framework Based on IoT, AI, and Blockchain for Smart Access to Government Agricultural Schemes

Author(s): D. Vinodha*, M. J. Buvana, S. Rajalakshmi, J. Jenefa, E. A. Mary Anita and Maria Lapina

Pp: 142-163 (22)

DOI: 10.2174/9789815274349124010011

* (Excluding Mailing and Handling)

Abstract

Agriculture plays an important part in most countries, such as India. A survey says that 54.6% of the total labor force of India is engaged in agriculture and its connected activities. The government is announcing many schemes to facilitate agriculture and support farmers. But most of the farmers are from poor families and are not able to reach the government schemes when they are really in need. Also, it is required to observe and measure the inter and intra-field variability in crops to enjoy the complete benefits of government schemes. This can be done with the advancements in the field of the Internet of Things. Information related to the impact of natural calamities on the agricultural field, malfunctions in the machinery used for cropping, yielding level, and health status of crops can be measured using the technology of IoT (Internet of Things) and analyzed using AI (Artificial Intelligence). Blockchain plays a critical role in replacing traditional means of data storage and exchanging agricultural data with a more trustworthy, immutable, transparent, and decentralized approach. By keeping all the transactions related to government schemes in blockchain, the possible crimes in the form of false data by the intermediate dealers acting between the farmers and the government can be addressed. This, in turn, allows useful government schemes to reach the farmer in time. We propose to develop a theoretical model using IoT, AI, and blockchain, which can assist the farmers in benefitting from the appropriate schemes announced by the government in time and achieving precise agriculture. 


Keywords: Artificial intelligence, Blockchain, Drone monitoring system, Internet of Things, Learning model, Multispectral imaging sensors, Precision agriculture, Unmanned aerial vehicles.

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