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- W4360914189 abstract "Agriculture has a substantial impact on the global economy. One of the main threats to agriculture in the long term is climate change and other environmental factors. The primary determinant of the crop yield is weather conditions (e.g., rain, temperature, etc.). Historical data, e.g., weather, soil, and historic crop yield, are all taken into account for yield predictions. Previously developed models had lower accuracy, even though they had used many datasets that cluttered the models. This paper introduces machine learning methods to predict the food crop yield capacity of 10 food crops, that are widely consumed around the world. Several machine learning algorithms, including Random Forest Regressor, Linear Regression and decision tree Classifier are implemented on various data sets to perform a crop yield prediction. The random forest regression model is used to achieve higher accuracy using different datasets like temperature, rainfall, yield etc. It has been found that the accuracy for Multiple Linear Regression is 11.9%, the accuracy for Random Forest Regression is 99.8% and the accuracy for decision tree classifier is 97.8%." @default.
- W4360914189 created "2023-03-26" @default.
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- W4360914189 date "2023-01-01" @default.
- W4360914189 modified "2023-09-23" @default.
- W4360914189 title "Prediction Analysis of Crop and Their Futuristic Yields Using Random Forest Regression" @default.
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- W4360914189 doi "https://doi.org/10.1007/978-3-031-27915-7_50" @default.
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