Empowering Crop Selection with Ensemble Learning and K-means Clustering: A Modern Agricultural Perspective

Agriculture is an intricate blend of scientific principles and practical techniques that facilitate the growth of crops and the cultivation of livestock. It involves the careful cultivation of the land to produce essential food, fibers, and various other agricultural products.

Effective agricultural planning fosters self-sufficiency in food production, offers a source of income for farmers, and contributes to government revenue. This research focuses on utilizing ensemble learning techniques and K-means clustering to predict optimal crop types for specific environmental conditions and categorize crops according to their environmental requirements. This approach aims to refine crop selection strategies significantly.

The study, employing a comprehensive dataset, applies these advanced methods, yielding accurate predictions and deeper insights into the interaction between crops and their growing environments.

These findings suggest a potential revolution in agricultural decision-making, highlighting the benefits of these methodologies in enhancing crop yield, reducing environmental impact, and promoting sustainable agricultural practices. The outcomes underscore the impact of data-driven approaches in modern agriculture, offering a promising direction for future agricultural development.