• Sahithya Ullas
• Nisarga K.M
• Inchara M
• Impana H
• Ayeesha Siddiqha

As agriculture faces increasing pressure from climate change, limited natural resources, and growing population demands, the need for intelligent, data-driven support systems has become more urgent. Machine Learning (ML)-based crop recommendation systems are proving to be valuable tools that assist farmers in selecting the most suitable crops based on factors like soil properties, weather patterns, and regional agricultural practices. This literature survey reviews recent developments in such systems, comparing different machine learning models, their accuracy, and performance evaluation methods. It also discusses current limitations and areas where existing systems fall short. By compiling and analyzing findings from various studies, this paper offers insights that can inform future advancements in precision agriculture and contribute to more sustainable, efficient farming practices.

Machine Learning, Crop Recommendation, Precision Agriculture, Soil Analysis, Environmental Data, Random Forest, IoT in Agriculture, Smart Farming, Data-Driven Decision Support, Sustainable Agriculture

"Survey Paper on Machine Learning-Based Crop Recommendation Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 5, page no.j834-j843, May-2025, Available :http://www.jetir.org/papers/JETIR2505A76.pdf

"Survey Paper on Machine Learning-Based Crop Recommendation Systems", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 5, page no. ppj834-j843, May-2025, Available at : http://www.jetir.org/papers/JETIR2505A76.pdf