Intelligent Crop Yielding System: Integrating Machine Learning for Enhanced Agricultural Decision-Making
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Intelligent Crop Yielding System: Integrating Machine Learning for Enhanced Agricultural Decision-Making
Author: Dheeksha R, Mr.B.Manogaran M.C.A.,
Affiliation: M.Sc Computer Science
Institution: kongunadu arts and science college
Email: dheeksharajesh11@gmail.com, manogaranb_cs@kongunaducollege.ac.in
Abstract:Agriculture faces persistent challenges such as unpredictable yields, soil degradation, and crop diseases, particularly in regions reliant on traditional farming practices. This paper presents the development of an intelligent Crop Yielding System built on the Python Django framework, incorporating machine learning models for crop recommendations, fertilizer optimization, yield prediction, and disease detection. The system processes soil, climate, and crop data to deliver actionable insights through user-friendly dashboards and visualizations. Key features include responsive web interfaces, role-based access, and real-time predictions using libraries like Matplotlib and Plotly. Evaluations demonstrate improved decision making for farmers, with predictions aiding in reducing losses and enhancing productivity. This integrated platform bridges gaps in existing fragmented tools, promoting sustainable farming.
Keywords: Crop recommendation, Yield prediction, Machine learning, Django framework, Disease detection, Precision agriculture
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