Publication Date: 2024/02/29
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.
DOI: https://doi.org/10.5281/zenodo.10725442
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT24FEB1278.pdf
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