Crop Yield Prediction Using Ensemble Algorithm
Keywords:Accuracy, AdaBoost regressor, Classification, Crop yield, Decision tree regressor, Ensemble, Metrics, Prediction, Churn
Machine studying is a pivotal standpoint for greedy real-global and useful use instances for yield prediction of crops. Machine studying is a supportive device for the rural area which allows us to determine which plant to develop and whilst to develop the preferred plant. This study suggests the utilization and implementation of predicting the crop kind primarily based totally on ensemble techniques. From a hard and fast of given parameters, gadget studying can forecast the final results via unsupervised or supervised studying techniques. To get the desired output parameter, we have to produce an appropriate and pleasant characteristic via the means of a few sets of variables if you want to depict the output using the given entered variables or parameters. This consists of the ensemble (combination) of or extra gadget studying algorithms which improves the crop yield prediction accuracy.
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Copyright (c) 2022 G. Abirami, R. Reni Hena Helan, K. Anandhan, G. Vishnuvardhan Reddy, S. Naveen Kumar, V. Ruban Karthick
This work is licensed under a Creative Commons Attribution 4.0 International License.