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International Journal of Pure & Applied Bioscience (IJPAB)
Year : 2019, Volume : 7, Issue : 1
First page : (296) Last page : (305)
Article doi: :

The Economics of Applications of Artificial Intelligence and Machine Learning in Agriculture

AkshataNayak*, Lokesha H, MahinShariff and Murtuza Khan

Department of Agricultural Economics, UAS, GKVK, Bengaluru- 560 065, Karnataka
*Corresponding Author E-mail:
Received: 22.12.2018 | Revised: 28.01.2019 | Accepted: 10.02.2019  



The global population is expected to reach more than nine billion by 2050, requiring a growing in agricultural production by 70 % in order to suit the demand. Only about 10 % of this growth may come from availability of unused lands, with the result that the rest of 90% will need to come from intensification of current production3. The agriculture sector needs a huge up-gradation in order to survive the changing conditions of Indian economy. The few techniques like artificial neural networks, Information Regression Analysis, Bayesian belief network. Markov chain model, k-means clustering and support vector machine are applied in the domain of agriculture.The major categories of applications of AI in agricultureare Agricultural Robotics, Crop and Soil Health Monitoring, Predictive Agricultural Analytics and Agri Supply Chain. Machine learning (ML) will have a dramatic impact on the field of economics within a short time frame. An abundance of easily accessible and high quality data has made it easier to use learning packages for R and Python to draw statistical inferences and make hypotheses. Artificial Intelligence can impact 70 million farmers in 2020, adding $9 billion to farmer incomes. AI comes as a great boon to the agricultural sector which is heavily dependent on climatic conditions which are often unpredictable.

Key words: Artificial Intelligence, Applications, Accuracy, Learning, Technology.

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Cite this article: Nayak, A., Lokesha, H., Shariff, M. and Khan, M., The Economics of Applications of Artificial Intelligence and Machine Learning in Agriculture, Int. J. Pure App. Biosci.7(1): 296-305 (2019). doi: