• SharathKumar B S
• Venugopal M Bhat
• Shaikh Saifulla
• G. Tarun Kumar
• Dr. Sunitha K

India forms a part of the top three makers of several different crops worldwide and serves as a well-known agriculture center. While Indian farmers play a crucial part in agriculture business, a large percentage of them are still at the lower end of the social scale. Even with a few technology fixes, farmers still struggle to recognize the most lucrative and viable crops for their land given the variety of soil types in different parts of the world. This study presents a crop suggestion system that predicts the best crop drawn from a thorough examination of several factors, including location, kind of soil, yield, price upon sale, and more. It does this by using both a Convolutional Neural Network (CNN) design and a Random Forest (RF) Model. An accuracy rate of 75% is anticipated from the CNN design, while the RF Algorithm may produce an accuracy rate of 98%.

Random Forest, Image classification, Deep learning, Convolutional Neural Network, MobileNet v2.

"Soil Analysis and Crop Recommendation using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.g212-g218, May-2024, Available :http://www.jetir.org/papers/JETIR2405626.pdf

"Soil Analysis and Crop Recommendation using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppg212-g218, May-2024, Available at : http://www.jetir.org/papers/JETIR2405626.pdf