Prediction of Crop Compliant System Using Machine Learning Techniques


Authors : Dr. A. Sudhir Babu; Ch. Yoditha; G. Navya

Volume/Issue : Volume 9 - 2024, Issue 3 - March

Google Scholar : https://tinyurl.com/29ae49jb

Scribd : https://tinyurl.com/mryhmdpu

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAR402

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Abstract : Machine learning techniques are used to analyze agriculture production using the Agriculture Production dataset. Crop compliant prediction, essential for informed agricultural decisions, relies on factors such as weather conditions and crop management practices. Through analysis of the dataset, various machine learning models are applied to provide insights crucial for farmers and agricultural stakeholders. These insights aid in crop selection and farming practices, optimizing decisions based on climatic conditions and soil characteristics. The trained model facilitates crop prediction and recommendation, mitigating financial risks for farmers and promoting optimal crop yields.

Keywords : Machine Learning, Crop Yield Prediction, Weather Conditions, Climatic Conditions, Soil Characteristics.

Machine learning techniques are used to analyze agriculture production using the Agriculture Production dataset. Crop compliant prediction, essential for informed agricultural decisions, relies on factors such as weather conditions and crop management practices. Through analysis of the dataset, various machine learning models are applied to provide insights crucial for farmers and agricultural stakeholders. These insights aid in crop selection and farming practices, optimizing decisions based on climatic conditions and soil characteristics. The trained model facilitates crop prediction and recommendation, mitigating financial risks for farmers and promoting optimal crop yields.

Keywords : Machine Learning, Crop Yield Prediction, Weather Conditions, Climatic Conditions, Soil Characteristics.

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