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Crop classification and prediction based on soil nutrition using machine learning methods

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Abstract

In India, farmers generally grow a traditional crop or the crop that is in demand resulting in poor yield. In the former case, the nutritional value of the soil gets deteriorated due to the non-rotation of crops, while in the latter the soil nutrient is not analyzed for the suitability of the new crop. As a result, people may suffer from stress and depression due to low income. Taking these into account, for classifying and predicting the suitable crop based on the soil nutrition levels a model is proposed using machine learning models such as Decision Tree, Support Vector Machine, Naive Bayes, K-Nearest Neighbours, Extreme Gradient Boosting and Random Forest. The dataset for this research is collected from the Kaggle website consisting of 6 different crop types with 11 nutrients. The models are trained and tested with 80% and 20% of the dataset respectively. The results prove Extreme Gradient Boosting followed by Naive Bayes to perform better with an AUC score of 0.994 and 0.993 respectively when compared to other models.

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Data availability

The data that support the findings of this study are available in Kaggle in the name “Crop Recommender Dataset with Soil Nutrients” at “https://doi.org/10.34740/KAGGLE/DSV/2397200”. These data were derived from the resources available in the public domain.

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Acknowledgements

The authors would like to acknowledge the support of network Laboratory for performing this work.

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  1. Electrical and Electronics Engineering Department, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India

    T. Swathi & S. Sudha

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  1. T. Swathi
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Swathi, T., Sudha, S. Crop classification and prediction based on soil nutrition using machine learning methods. Int. j. inf. tecnol. 15, 2951–2960 (2023). https://doi.org/10.1007/s41870-023-01345-0

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