Manning Agricultural Productivity to Reduce the Supply Gap with the Help of Artificial Intelligence

  • Rajan Gunabalan, Makarand Balaji Thakoor

Abstract

Artificial intelligence and Machine learning is all about using data for efficient inferences and predicting the future and decisions.These decisions are made human like, by machines, machine learning and big data is having a greater impact in the way we live. Scholars and scientists are looking Machine learning as a pioneer opportunity to create a positive impact among our day to day life, especially in the field of agriculture domains. .The research reviews and project popular Machine learning model used in the field of agriculture such as a) crop management (crop yielding, Fruit picking weed and diseases detection, b) soil management C) water management the paper aims to introduce different types of machine learning methods and algorithms used in machine learning and how machine learning enach the agriculture, by implementing the machine learning into farmers by remote sensor. The farmers will get benefited in decision making such as risk reduction, quality seed selection, and easy monitoring with software’s.At the same time, the present study also focuses on the way Artificial Intelligence and IoT, if introduced in agricultural sector, can boost on the productivity of the sector in a sustainable manner.

Published
2021-11-22
How to Cite
Rajan Gunabalan, Makarand Balaji Thakoor. (2021). Manning Agricultural Productivity to Reduce the Supply Gap with the Help of Artificial Intelligence. Design Engineering, 14818-14828. Retrieved from http://www.thedesignengineering.com/index.php/DE/article/view/6605
Section
Articles