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• W4381195728 abstract "According to numerous calculations, the global food output must significantly increase by 2050. Additionally, water levels have been declining, and there is a shortage of usable agricultural land. The first step to introducing smart agriculture in India is good analysis and implementation of machine learning. This research strives to present the best-fit machine learning algorithm to predict crop production in a specific area, state or season. The aim of this research is to make data-driven farming possible. By comparing various regression models namely decision tree, linear regression, XGB regression, and random forest, authors found the most accurate and fast algorithm to process the large volume of agricultural data. The data was visualized to find the top 5 crops produced in India and then the top 3 of them were visualized area-wise, state-wise, and season-wise to give a detailed analysis of crop distribution and production in India." @default.
• W4381195728 created "2023-06-20" @default.
• W4381195728 creator A5029210814 @default.
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• W4381195728 date "2023-05-11" @default.
• W4381195728 modified "2023-09-27" @default.
• W4381195728 title "A Comparative Analysis of ML Algorithms to Improve Crop Productivity Prediction" @default.
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• W4381195728 doi "https://doi.org/10.1109/icdt57929.2023.10150716" @default.
• W4381195728 hasPublicationYear "2023" @default.
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