Empowering Agriculture: A Machine Learning-Based Decision Support System for Crop Selection and Profitability Analysis
Keywords:
K-Nearest Neighbours (KNN), Support Vector Classifier(SVC), Gaussian Naive Bayes Classifier, Decision Tree, Ensemble Classifier, Stacking Ensemble Technique, XGBoost, Profitability.Abstract
Modern agriculture is increasingly reliant on technological advancements to optimize productivity and decision-making processes. This paper introduces a sophisticated web application designed to help farmers in selecting the appropriate crops for their soil, considering soil composition, weather conditions, market trends, and financial factors. The application integrates various machine learning methodologies to deliver personalized recommendations tailored to each farmer's specific circumstances. Initially, farmers provide details of soil mineral percentages, focusing on Nitrogen (N), Phosphorus (P), and Potassium (K) to the application. Subsequently, real-time weather data is retrieved from a Weather API to evaluate environmental suitability. An ensemble machine learning model, comprising Support Vector Classifier(SVC), K-Nearest Neighbours (KNN), Gaussian Naive Bayes Classifier, and Decision tree, is deployed to identify the top three crops best suited to the soil. To refine decision-making further, a Stacking Ensemble technique is employed, leveraging Regression algorithms such as Random Forest and XGBoost Regressors to forecast the market selling price of each crop based on historical data. This empowers farmers to assess both suitability and profitability concurrently. Additionally, the application provides detailed insights into cultivation costs per acre, aiding farmers in financial planning and risk management. Furthermore, instructional videos on crop cultivation practices are integrated to facilitate knowledge transfer and skill enhancement among farmers. Through elucidating the architecture, functionality, and performance metrics of the web application, this paper underscores it’s potential to revolutionize agricultural decision-making processes, enhance farm productivity, and ultimately contribute to sustainable rural livelihoods.
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