Random Forest Classifier For Crop Prediction Based On Soil Data
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Abstract
Agricultural development is crucial to feed the growing population. Most farmers tend to cultivate the crops which will give the more economical benefits besides checking the suitability of the crop according to the soil conditions. Use of technology in the agricultural sector leads the sustainable improvements in the agricultural production. Machine learning approach to suggest the suitable crop based on the soil parameters can help the farmers to cultivate the crops accordingly and can produce more yield. In this paper Random Forest Classifier is used to train the Machine Learning model on soil dataset using Python. Model performance is evaluated using confusion matrix and classification report having precision, recall and F1 score. Model accuracy achieved is 99% without parameter tuning.
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