Robust Crop Prediction Framework Using Soil Classification and Ensemble Methods
DOI:
https://doi.org/10.32628/CSEIT2511646Keywords:
Agriculture Data, Soil Series, Machine Learning, Crop Selection, Pre-process, Crop Yield Prediction, FeaturesAbstract
Because the population of the globe is always growing, the agricultural sector is very significant in terms of meeting the world's food requirements. However, traditional agricultural techniques do not always make the best use of crops in a manner that is advantageous to the environment and does not squander resources. Maximizing crop output is of vital importance for ensuring food security and economic stability, especially in countries in which agriculture plays a significant role in the economy. diverse kinds of soil have diverse characteristics that are suitable for growing a variety of crops. In order to increase the number of crops grown in this area, a variety of different strategies and models are used these days. This system makes use of machine learning methodologies in order to recommend crops that are appropriate for the kind or sequence of soil. The model will simply inform you of the kind of soil you have, and then, depending on the results, it will be able to provide suggestions for crops that would thrive in that soil. Utilizing a variety of different classifiers, the model is able to provide a recommendation for the most suitable crop.
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