Abstract
Machine learning is incontestably among the strongest and powerful technology in the world. It is a tool for turning data into knowledge. In the past 50 years, there has been an explosion of data. This mass of data is inefficient unless it is explored by us and the patterns are found. Machine learning methods are used to locate the underlying patterns in data that we would otherwise struggle to discover. The hidden patterns and comprehension about a problem may be used to forecast future events and execute all sorts of decision-making. This paper is dedicated to the applications of machine learning in the agricultural production system where different machine learning techniques like linear regression, ensemble method, and decision tree are applied to predict crop yield production by using favorable weather conditions.
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Ujjainia, S., Gautam, P., Veenadhari, S. (2021). A Machine Learning Approach Towards Increased Crop Yield in Agriculture. In: Swain, D., Pattnaik, P.K., Athawale, T. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1311. Springer, Singapore. https://doi.org/10.1007/978-981-33-4859-2_20
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DOI: https://doi.org/10.1007/978-981-33-4859-2_20
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