INDIAN CROP YIELD ANALYSIS AND PREDICTION

Abstract

Agriculture is one of the most important sectors of the Indian economy and provides employment and food resources for a large population. Crop yield can vary significantly due to factors such as rainfall, temperature, soil characteristics, irrigation, crop type, fertilizer usage, and cultivation area. Analyzing these factors can help identify patterns in agricultural production and support better planning. The Indian Crop Yield Analysis and Prediction system is designed to analyze historical agricultural data and predict crop yield using data analytics and Machine Learning techniques. The system uses information such as state, district, crop type, cultivated area, production, rainfall, temperature, soil properties, irrigation, and fertilizer usage. Data preprocessing is performed to clean and prepare the dataset for analysis. The system performs exploratory data analysis to understand crop production and yield patterns across different states, districts, crops, and years. Statistical measures and visualizations are used to compare crop performance and identify changes in agricultural productivity. Charts, graphs, maps, and dashboards can provide an easyto-understand representation of the collected agricultural information. The prediction module uses selected agricultural features to train a Machine Learning model for estimating crop yield. Different algorithms can be evaluated based on suitable performance metrics, and the model with appropriate performance can be used for prediction. The system can accept relevant agricultural inputs and generate an estimated yield value for the selected crop or region. Overall, the proposed system combines agricultural data analysis with predictive modeling to provide useful insights into Indian crop production. It can support farmers, agricultural researchers, analysts, and policymakers in understanding historical yield patterns and planning agricultural activities. Future enhancements can include real-time weather data, satellite imagery, soil sensors, crop disease detection, irrigation recommendations, and advanced yield forecasting.