• Alankari Eveen
• Jami Chiranjeevi
• Singisetti Sanjana
• Simhadri Venkata Ramana
• Valurouthu Saiteja

This paper designs a new method in precision agronomy forecasting on a basis of machine learning techniques. The careful handling of fertilizers is inevitable for enhancing crop productivity and reducing ecological impact. The orthodox approach to fertilizer application is mostly imprecise and can lead to either overuse or misuse, causing improper resource utilization and detriment to the environment. This system uses machine learning to analyze a huge number of factors, such as soil characteristics, type of crop, weather conditions and the historic harvest records. The system implements the use of modern machine learning algorithms such as decision trees, random forests, logistic regression and SVM in order to discover complex relationships between the input variables and optimal fertilizer recommendations. In summary, we provide novel insights into precision agriculture based on our use of machine learning to enhance the fertilizer management techniques.

fertilizer, soil measurement, recommender, machine learning, random forest classifier,SVM.

"Enhancing Agriculture Productivity Through Fertilizer Recommendation System Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 4, page no.n712-n717, April-2025, Available :http://www.jetir.org/papers/JETIR2504D87.pdf

"Enhancing Agriculture Productivity Through Fertilizer Recommendation System Using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 4, page no. ppn712-n717, April-2025, Available at : http://www.jetir.org/papers/JETIR2504D87.pdf