• Dr. M. Lavanya

In this paper, we propose a prediction method for major crops in India using a combination of K-means clustering and Modified K Nearest Neighbor (KNN) classification algorithms. Given that agriculture is the primary livelihood for over 40 percent of the state's population and the global population is projected to increase by one third between 2010 and 2050, with a corresponding 60 percent rise in demand for crop production, accurate prediction becomes crucial for maximizing yield. We utilized MATLAB for clustering using the K-means algorithm and WEKA for classification using Modified KNN. The results demonstrate that our method outperforms traditional data mining approaches. This approach allows us to anticipate crop demand more effectively, thereby enabling farmers and stakeholders to optimize production and meet the growing demands of the population

: Random forest regression, K-means, Machine learninig

"Advanced Analytical Framework for Crop Yield Prediction Leveraging Diverse Feature Selection Methods and Machine Learning Classifiers in Varied Agricultural Environments", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 7, page no.g158-g163, July-2024, Available :http://www.jetir.org/papers/JETIR2407619.pdf

"Advanced Analytical Framework for Crop Yield Prediction Leveraging Diverse Feature Selection Methods and Machine Learning Classifiers in Varied Agricultural Environments", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 7, page no. ppg158-g163, July-2024, Available at : http://www.jetir.org/papers/JETIR2407619.pdf