International Journal of Scientific Research and Engineering Development

Tamil Nadu, being a coastal state, faces uncertainty in agricultural production due to frequent climatic variations and environmental changes. Although the state has a large cultivable area and a growing population, the expected level of productivity has not been achieved. In earlier decades, farmers relied mainly on traditional knowledge and word-of-mouth practices for crop cultivation, but these methods have become unreliable under present climatic conditions. Agricultural activities depend on factors such as rainfall, temperature, soil characteristics, humidity, and seasonal variations, which generate large volumes of data. The rapid growth of information technology has created new opportunities in agricultural sciences by enabling effective data analysis. In this context, modern computational and machine learning techniques play an important role in extracting insights from agricultural data. These techniques support prediction and decision-making for challenges such as crop selection, crop rotation, water requirement estimation, and fertilizer usage. Due to dynamic environmental conditions, efficient systems are needed to support farmers. This study highlights the role of data analytics in developing a crop recommendation system that suggests suitable crops based on climatic conditions, productivity, and seasonal factors.

Sona G, Sivaranjani P S, Beryl Gifty Monisha D, "Crop Recommendation System Based on Productivity Using Machine Learning Algorithm" International Journal of Scientific Research and Engineering Development, V9(4): Page(1258-1266) July-August 2026. ISSN: 2581-7175. www.ijsred.com. Published by Scientific and Academic Research Publishing.