| Summary: | This paper presents a cost and income forecast system that includes multiple linear regression models and business intelligence (BI) tools and is tailored particularly for fruit crop entrepreneurs. In the fruit crop farming sector, controlling expenses and projecting revenue may be extremely difficult for entrepreneurs. These challenges arise due to the complex and variable nature of agricultural production, market fluctuations, weather conditions, and other external factors. The major purpose is to deliver a comprehensive solution that improves decision-making by accurately estimating production costs and prospective income. Historical data on production costs, market prices, crop yields, weather conditions, and soil quality were gathered, pre-processed, and examined. Important factors were found through exploratory data analysis and included in the regression models. Variables include TSP fertilizer, NPK fertilizer, processed organic fertilizer, GML fertilizer, herbicides (basta), insecticides (Malathion 57), and output(kg). The income prediction model considers factors like crop yield, market price, and harvest quality. To guarantee their correctness and dependability, both models underwent extensive training and evaluation using measures like R-squared, mean absolute error (MAE), and root mean squared error (RMSE). Apart from predictive analytics, BI technologies were used to provide interactive data visualization, trend analysis, and reporting. These technologies help entrepreneurs get meaningful insights from data, improving strategic planning and informed decision-making. The solution provides a user-friendly interface via which business owners can enter essential data and receive real-time BI insights and projections. By providing fruit crop entrepreneurs with cutting-edge analytical tools and useful information, this integrated strategy hopes to improve financial results and promote sustainable business practices. © 2024 IEEE.
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