Authors :
Meka.Viswas; Kedarisetti.Sri.Naga.Veera.Venkata.Swami.Subha.Surya; Kathula.Ramu; Medapati. Rama.Phaneendra Reddy
Volume/Issue :
Volume 9 - 2024, Issue 2 - February
Google Scholar :
http://tinyurl.com/4u7kx8u3
Scribd :
http://tinyurl.com/yc4rczt6
DOI :
https://doi.org/10.5281/zenodo.10725442
Abstract :
Agriculture is an important part of the Indian
economy and more than half of the country's population
earns their living from agriculture. Agriculture is an
important part of the growth of human civilization
through the raising of domesticated animals that produce
food that enables people to survive. Machine learning is
used to predict crop yield based on parameters such as
rainfall, yield and weather. In addition to being an
important decision-making tool for crop yield prediction,
machine learning also supports crop production and crop
production-related decision-making. commonly used
algorithms It is a neural network device. Weather, climate
and other environmental factors pose a long-term threat to
agriculture. Machine learning (ML) is important because
it provides decision support tools for crop forecasting
(CYP) that can help make decisions such as which crops to
plant and how during the growing season. The main
limitation of neural networks is to reduce the relative error
and efficiency of crop yield prediction. The main objective
of crop forecasting is to improve crop production and
various models are used to achieve this goal. This research
helps make agriculture more efficient by demonstrating
machine learning's ability to predict crop yields with high
levels of productivity. Design can be a decision support
tool for farmers, enabling them to make informed
decisions on crop management, resource allocation and
risk mitigation, ultimately increasing agricultural
sustainability and food security. Using the results of this
study, farmers will be able to make informed decisions by
determining the yield of their crops before planting on
their farms.
Keywords :
Crop_Yield_Prediction; Logistic_Regression; Naive Bayes; Random Forest; Dataset.
Agriculture is an important part of the Indian
economy and more than half of the country's population
earns their living from agriculture. Agriculture is an
important part of the growth of human civilization
through the raising of domesticated animals that produce
food that enables people to survive. Machine learning is
used to predict crop yield based on parameters such as
rainfall, yield and weather. In addition to being an
important decision-making tool for crop yield prediction,
machine learning also supports crop production and crop
production-related decision-making. commonly used
algorithms It is a neural network device. Weather, climate
and other environmental factors pose a long-term threat to
agriculture. Machine learning (ML) is important because
it provides decision support tools for crop forecasting
(CYP) that can help make decisions such as which crops to
plant and how during the growing season. The main
limitation of neural networks is to reduce the relative error
and efficiency of crop yield prediction. The main objective
of crop forecasting is to improve crop production and
various models are used to achieve this goal. This research
helps make agriculture more efficient by demonstrating
machine learning's ability to predict crop yields with high
levels of productivity. Design can be a decision support
tool for farmers, enabling them to make informed
decisions on crop management, resource allocation and
risk mitigation, ultimately increasing agricultural
sustainability and food security. Using the results of this
study, farmers will be able to make informed decisions by
determining the yield of their crops before planting on
their farms.
Keywords :
Crop_Yield_Prediction; Logistic_Regression; Naive Bayes; Random Forest; Dataset.
