Leveraging Machine Learning for Improved Agricultural Risk Management

India's population is predicted to reach 1.67 billion by 2050 [1]. The growing urbanized population has higher food demands. Crop production must be enhanced in light of the massive population growth. The greatest impediment to maximizing production is pests and insects and the harm they create. Several machine-learning approaches are implemented to prevent crop damage and boost agricultural output. Our research includes a literature overview on ML approaches applied in agriculture, with an emphasis on the identification, detection, and prediction of crop-damaging diseases and pests. It seeks to further the advancement of smart farming and precision agriculture. The dataset's precise and dependable crop yield forecasts can benefit initiatives aimed at assisting small-scale farmers, advocating for sustainable farming methods, and guaranteeing food security for everyone. It includes a broad variety of environmental and agricultural parameters that can analyze the relationship between different factors and crop yields.