Innovative Approaches to Agricultural Risk with Machine Learning

Agriculture is fraught with uncertainties arising from factors like weather volatility, pest outbreaks, market fluctuations, and technological advancements, posing significant challenges to farmers. By gaining insights into these risks, farmers can enhance decision-making, adopt proactive measures, and optimize resource allocation to minimize negative impacts and maximize productivity. The research introduces an innovative approach to risk prediction, highlighting its pivotal role in improving agricultural practices. Through meticulous analysis and optimization of a farmer dataset, employing preprocessing techniques, the study ensures the reliability of predictive models built on high-quality data. Utilizing Variation Inflation Factor (VIF) for feature selection, the study identifies influential features critical for accurate risk classification. Employing techniques like KNN, Random Forest, logistic regression, SVM, Ridge classifier, Gradient Boosting and XGBoost, the study achieves promising results. Among them KNN, random forest, Gradient Boosting and XGBoost scored with high accuracy of 88.46%. This underscores the effectiveness of the proposed methodology in providing actionable insights into potential risks faced by farmers, enabling informed decision- making and risk mitigation strategies.