The objective of this project is to analyze how Artificial Intelligence can predict crop yields and assess climate change impacts on agriculture. It focuses on using data-driven models to improve farming decisions, enhance productivity, and support sustainable agricultural practices under changing environmental conditions.
Study the importance of agriculture in the economy and challenges posed by climate change.
Understand the concept of crop yield prediction and its role in agricultural planning.
Explore the fundamentals of Artificial Intelligence, Machine Learning, and predictive analytics.
Identify key factors affecting crop yield such as rainfall, temperature, soil quality, and irrigation.
Collect or use secondary datasets related to weather patterns and crop production.
Perform data cleaning and preprocessing to prepare datasets for analysis.
Apply machine learning models (e.g., regression, decision trees) for yield prediction.
Analyze the impact of climate variables on crop productivity.
Use tools like Python, Excel, or Power BI for analysis and visualization.
Compare predicted yields with historical data to evaluate model accuracy.
Identify trends and patterns related to climate change and agricultural output.
Develop dashboards to present insights on crop yield predictions and risks.
Suggest strategies for farmers to improve productivity using AI insights.
Evaluate limitations such as data availability, model accuracy, and environmental variability.
Provide recommendations for policymakers and agribusiness firms to promote sustainable agriculture.
