Integrating Soil Nutrients and Location Weather Variables for Crop Yield Prediction


Authors : Oladipe Ebenezer Oluwole; Osaghae Edgar.O; Basaky Fredrick .D.

Volume/Issue : Volume 8 - 2023, Issue 3 - March

Google Scholar : https://bit.ly/3TmGbDi

Scribd : https://bit.ly/40kRQEN

DOI : https://doi.org/10.5281/zenodo.7825277

Abstract : - This study is described as a recommendation system that utilize data from Agricultural development program (ADP) Kogi State chapters of Nigeria and employs machine learning approach to recommend suitable crops to be planted according to input soil and climate statistics. The purpose of this study is to forecast agricultural production using machine learning techniques. The inferred classifier models here incorporate information from Random forest classifier,Gradient boost and Keras Regressor with Gradient boost exhibiting the greatest accuracy of 98.9%. Forecasts made by machine learning algorithms can now assist farmers in choosing which plant to cultivate based on key variables, including temperature, rainfall, and pH and soil nutrients as nitrogen, phosphorous and potassium. Therefore, this strategy lessens the financial losses that farmers experience when they decide to plant the improper crops. It also helps farmers in their search for new crop varieties that are suitable for cultivation in their region.

Keywords : Machine Learning, Crop Yield, Cultivation, Forecasting, Plantation, Data Mining, Agriculture.

- This study is described as a recommendation system that utilize data from Agricultural development program (ADP) Kogi State chapters of Nigeria and employs machine learning approach to recommend suitable crops to be planted according to input soil and climate statistics. The purpose of this study is to forecast agricultural production using machine learning techniques. The inferred classifier models here incorporate information from Random forest classifier,Gradient boost and Keras Regressor with Gradient boost exhibiting the greatest accuracy of 98.9%. Forecasts made by machine learning algorithms can now assist farmers in choosing which plant to cultivate based on key variables, including temperature, rainfall, and pH and soil nutrients as nitrogen, phosphorous and potassium. Therefore, this strategy lessens the financial losses that farmers experience when they decide to plant the improper crops. It also helps farmers in their search for new crop varieties that are suitable for cultivation in their region.

Keywords : Machine Learning, Crop Yield, Cultivation, Forecasting, Plantation, Data Mining, Agriculture.

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