• Anushka Aman
• Gaurav V Salgaonkar
• Jyotsna A Patel
• Kunal Tapse
• Dr. T N Anitha

Predicting crop yield is critical in agriculture as it helps farmers in making informed decisions related to planting, harvesting, and marketing their crops. With the advent of machine learning techniques, crop yield prediction has become more effective and accurate by utilizing various factors such as weather conditions, soil properties, and agricultural practices. In this paper, our focus is on the random forest regressor algorithm for crop yield prediction. The algorithm is popular for its ability to handle complex and non-linear relationships between input features and the target variable. It is also robust to overfitting and can manage high-dimensional datasets. Our objective is to examine the effectiveness of the random forest algorithm for crop yield prediction and compare its performance with other machine learning algorithms. We also aim to determine the factors influencing the prediction accuracy and provide insights into the model's interpretation. Our study can aid agricultural experts and farmers in making informed decisions about crop management practices, ensuring food security, and promoting sustainable agriculture practices. Overall, this study provides valuable insights into the application of machine learning for crop yield prediction and highlights the potential benefits of using Random Forest Regressor in agriculture.

Crop yield prediction, Crop prediction, Random forest regressor algorithm, food security

"Crop Yield Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.b435-b439, May-2023, Available :http://www.jetir.org/papers/JETIR2305157.pdf

"Crop Yield Prediction using Machine Learning", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppb435-b439, May-2023, Available at : http://www.jetir.org/papers/JETIR2305157.pdf