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
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.
