• Anusha Paniraj Shanbhog
• Akshata G Kulkarni
• Anushree L D
• Akshat Singh
• Aparna M

This paper presents a pioneering application of machine learning (ML) techniques in the domain of smart farming. Leveraging ML methodologies, our approach aims to optimize various facets of agricultural practices. Specifically, we explore the integration of these techniques to enhance crop prediction and recommendation, yield prediction, rainfall prediction and fertilizer recommendation. Our model harnesses vast amounts of data from different datasets and by analyzing this data, our method maximizes productivity and profits. Through case studies, we demonstrate the effectiveness of ML in addressing the challenges faced by traditional farming methods. Furthermore, we discuss strategies for overcoming obstacles such as data quality issues and computational complexity, emphasizing the potential for widespread implementation of smart farming practices. This research contributes to the advancement of sustainable and efficient agriculture, promising improved yields, reduced resource consumption, and enhanced profitability for farmers worldwide.

smart farming, machine learning, crop recommendation, fertilizer recommendation, yield prediction, rainfall prediction, crop prediction

"Smart Farming using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.g609-g614, March-2024, Available :http://www.jetir.org/papers/JETIR2403682.pdf

"Smart Farming using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppg609-g614, March-2024, Available at : http://www.jetir.org/papers/JETIR2403682.pdf