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Comparative Analysis of Supervised Machine Learning Techniques in Crop Yield Prediction
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Machine learning techniques play an important role in solving real world problems. These techniques are also found to be successful in the field of Agriculture for crop yield prediction, leaf disease detection, fruit disease detection, vegetable quality assessment, etc. In this paper, the authors performed comparative analysis of various supervised machine learning techniques for crop yield prediction from soil parameters. Five supervised machine learning techniques such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Naïve Bayes (NB), Decision Tree (DT) and Random Forest (RF) have been taken for the experimental analysis. The experiments have been carried out for the prediction of the three most commonly grown crops in India: Rice, Wheat and Mustard. The performance of each technique for every crop taken in this study, has been evaluated on the basis of four metrics i.e. accuracy, recall, precision and f-score. The experimental results revealed that decision tree and random forest performed better than all the other supervised machine learning techniques taken in this study, for the prediction of each crop.
Keywords
DT, KNN, Mustard, RF, Rice, SVM, Wheat
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- Agriculture in India - Statistics & Facts. Accessed: Nov. 30, 2020. [Online]. Available: https://www.statista.com/topics/4868/agricultural-sector-in-india/
- V. Bhuyar, “Comparative analysis of classification techniques on soil data to predict fertility rate for Aurangabad district,” International Journal of Emerging Trends and Technology in Computer Science, vol. 3, no. 2, pp. 200-203, 2014.
- D. A. Bondre, and S. Mahagaonkar, “Prediction of crop yield and fertilizer recommendation using machine learning algorithms,” International Journal of Engineering Applied Sciences and Technology, vol. 4, no. 5, pp. 371-376, 2019.
- M. Champaneri, C. Chandvidkar, D. Chachpara, and M. Rathod, “Crop yield prediction using machine learning,” International Journal of Science and Research, vol. 9, no. 4, pp. 645-648, 2020.
- T. V. Klompenburg, A. Kassahun, and C. Catal, “Crop yield prediction using machine learning: A systematic literature review,” Computers and Electronics in Agriculture, vol. 177, 2020, Art. no. 105709.
- K. Lata, and B. Chaudhari, “Crop yield prediction using data mining techniques and machine learning models for decision support system,” Journal of Emerging Technologies and Innovative Research, vol. 6, no. 4, pp. 391-396, 2019.
- S. Manimekalai, and K. Nandhini, “Crop yield prediction from soil parameters through Neupper rule established algorithm,” International Journal of Engineering and Technology, vol. 7, no. 3.34, pp. 908-912, 2018.
- E. Manjula, and S. Djodiltachoumy, “A model for prediction of crop yield,” International Journal of Computational Intelligence and Informatics, vol. 6, no. 4, pp. 298-305, 2017.
- P. Priya, U. Muthaiah, and M. Balamurugan, “Predicting yield of the crop using machine learning algorithm,” International Journal of Engineering Sciences and Research Technology, vol. 7, no. 4, pp. 1-7, 2018.
- A. Shastry, H. A. Sanjay, and E. Bhanusree, “Prediction of crop yield using regression techniques,” International Journal of Soft Computing, vol. 12, no. 2, pp. 96-102, 2017.
- M. Suganya, R. Dayana, and R. Revathi, “Crop yield prediction using supervised learning techniques,” International Journal of Computer Engineering & Technology, vol. 11, no. 2, pp. 9-20, 2020.
- D. M. Supriya, “Analysis of soil behavior and prediction of crop yield using data mining approach,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 5, pp. 9648-9652, 2017.
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