Key points are not available for this paper at this time.
The research explores grape farming resilience enhancement in Sangli district through machine learning for crop yield prediction, emphasizing soil health factors. It integrates historical data, focusing on soil nutrients, moisture, and composition, to analyze their impact on grape productivity. The study's insights offer potential for machine learning to optimize grape cultivation strategies, crucial in a region where grape farming is significant. A historical perspective showcases the evolution of grape yields influenced by climate, soil quality, technology, and disease management between 1980 and 2020. The progression suggests steady growth, likely due to agricultural advancements and improved practices. The paper discusses predictive analytics' broad utility across sectors and its specific applications in agriculture, particularly in crop yield forecasting and price projection, aiding farmers in decision-making and market timing. The research methodology proposes a strategy engine using machine learning algorithms to forecast grape crop yields by integrating weather, soil, and yield data, aiming for tailored farming decisions. Ultimately, the study underscores machine learning's transformative potential in optimizing agricultural practices, enhancing productivity, and strengthening grape farming resilience in Sangli. The application of these findings holds promise for sustainable and efficient grape cultivation, contributing to agricultural advancements and economic growth in the region.
Patil et al. (Wed,) studied this question.