Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests
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Keywords:
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Classification, Corn Plant Diseases and Pests, Decision Tree, Fuzzy Membership Function
Abstract
Corn is an essential agricultural commodity since it is used in animal feed, biofuel, industrial processing, and the manufacture of non-food industrial commodities such as starch, acid, and alcohol. Early detection of diseases and pests of corn aims to reduce the possibility of crop failure and maintain the quality and quantity of crop yields. A decision tree is a nonparametric classification model in statistical machine learning that predicts target variables using tree-structured decisions. The performance of this model can increase significantly if the continuous predictor variables are discretized into valid categories. However, in some cases, the result does not provide satisfactory performance. The possible cause is the ambiguity in discretizing predictor variables. The incorporation of fuzzy membership functions into the model to resolve discretization ambiguity issues. This work aims to classify diseases and pests of corn plants using the decision tree model and improve the model’s performance by implementing fuzzy membership functions. The main contribution of this work is that we have shown a significant improvement in the decision tree model performance by implementing fuzzy membership functions; S-growth, triangle, and S-shrinkage curves. The proposed fuzzy model is better than the decision tree model, with an average performance increase from the largest to the smallest; kappa (12.16%), recall (11.8%), F-score (9.71%), precision (5.08%), accuracy (3.23%), specificity (1.94%), and AUC (0.49%). The combination of bias and variance generated by the proposed model is quite small, indicating that the model is able to capture data trends well.
References
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Kresnawati, E. S., Y. Resti, B. Suprihatin, M. R. Kurniawan, and W. A. Amanda (2021). Coronary Artery Disease Prediction using Decision Trees and Multinomial Naïve Bayes with k-Fold Cross Validation. Inomatika, 3(2); 172–187
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Panigrahi, K. P., H. Das, A. K. Sahoo, and S. C. Moharana (2020). Maize Leaf Disease Detection and Classification using Machine Learning Algorithms. In Progress in Computing, Analytics and Networking. Springer
Quinlan, J. R. (1996). Improved use of Continuous Attributes in C4. 5. Journal of Articial Intelligence Research, 4; 77 90
Rajesh, B., M. V. S. Vardhan, and L. Sujihelen (2020). Leaf Disease Detection and Classification by Decision Tree. In International Conference on Trends in Electronics and Informatics; 705–708
Ramasubramanian, K. and A. Singh (2017). Machine Learning using R: with Time Series and Industry-based use Cases in R, 2a. Apress, New Delhi
Resti, Y., F. Burlian, I. Yani, and I. M. Sari (2020). Improved the Cans Waste Classification Rate of Naïve Bayes using Fuzzy Approach. Science and Technology Indonesia, 5(3); 75– 78
Resti, Y., C. Irsan, M. T. Putri, I. Yani, A. Ansyori, and B. Suprihatin (2022). Identification of Corn Plant Diseases and Pests Based on Digital Images using Multinomial Naïve Bayes and K-Nearest Neighbor. Science and Technology Indonesia, 7(1); 29–35
Resti, Y., E. S. Kresnawati, N. R. Dewi, N. Eliyati (2021). Diagnosis of Diabetes Mellitus in Women of Reproductive Age using the Prediction Methods of Naive Bayes, Discriminant Analysis, and Logistic Regression. Science and Technology Indonesia, 6(2); 96–104
Rodriguez, J. D., A. Perez, and J. A. Lozano (2009). Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation. Institute of Electrical and Electronics Engineers Transactions on Pattern Analysis and Machine Intelligence, 32(3); 569–575
Sahith, R., P. Reddy, and S. Nimmala (2019). Decision Tree-based Machine Learning Algorithms to Classify Rice Plant Diseases. International Journal of Innovative Technology and Exploring Engineering, 9(1); 5365–5368
SAS Institute Inc (1999). SAS/GRAPH Colors. In Sas/Graph
Semra, E. and Ö. Ersoy (2010). Comparison of ID3, Fuzzy ID3 and Probabilistic ID3 Algorithms in The Evaluation of Learning Achievements. Journal of Computing, 2(12); 20–25
Singh, A. K., B. Chourasia, N. Raghuwanshi, and K. Raju (2021). BPSO based Feature Selection for Rice Plant Leaf Disease Detection with Random Forest Classier. International Journal of Engineering Trends and Technology, 69(4); 34–43
Sokolova, M. and G. Lapalme (2009). A Systematic Analysis of Performance Measures for Classication Tasks. Information Processing & Management, 45(4); 427–437
Sutha, P., A. Nandhu Kishore, V. Jayanthi, A. Periyanan, and P. Vahima (2021). Plant Disease Detection using Fuzzy Classification. Annals of The Romanian Society for Cell Biology, 25(4); 9430–9441
Syarief, M. and W. Setiawan (2020). Convolutional Neural Network for Maize Leaf Disease Image Classification. Telkomnika, 18(3); 1376–1381
Witten, I., E. Frank, and M. Hall (2011). Data Mining: Practical Machine Learning Tools and Techniques (Google eBook). Complementary Literature None
Xian, T. S. and R. Ngadiran (2021). Plant Diseases Classification using Machine Learning. In Journal of Physics: Conference Series, 1962; 012024
Yunus, M. (2018). Optimasi Penentuan Nilai Parameter Himpunan Fuzzy dengan Teknik Tuning System. MATRIK: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, 18(1); 21–28 (in Indonesia)
Bengio, Y. and Y. Grandvalet (2003). No Unbiased Estimator of The Variance of k-Fold Cross-Validation. Advances in Neural Information Processing Systems, 16; 1089–1105
Bitar, S., C. Campos, and C. Freitas (2016). Applying Fuzzy Logic to Estimate The Parameters of The Length-Weight Relationship. Brazilian Journal of Biology, 76; 611–618
Dhanalakshimi, R., C. Geetha, and T. Sethukarasi (2019). Monitoring and Detecting Disease in Human Adults using Fuzzy Decision Tree and Random Forest Algorithm. International Journal of Recent Technology and Engineering, 7(5); 93–99
Dougherty, J., R. Kohavi, and M. Sahami (1995). Supervised and Unsupervised Discretization of Continuous Features. In Machine Learning Proceedings. Elsevier
Dubois, D. and H. Prade (2016). Practical Methods for Constructing Possibility Distributions. International Journal of Intelligent Systems, 31(3); 215–239
Ferreira, J. A., E. Soares, L. S. Machado, and R. M. Moraes (2015). Assessment of Fuzzy Gaussian Naive Bayes for Classification Tasks. In Proceedings of The Seventh International Conferences on Pervasive Patterns and Applications (Patterns 2015). Nice, França, 1; 64–69
García, S., J. Luengo, and F. Herrera (2015). Data Preprocessing in Data Mining, Volume 72. Springer
Han, J., J. Pei, and M. Kamber (2011). Data Mining: Concepts and Techniques. Elsevier
Hastie, T., R. Tibshirani, J. H. Friedman, and J. H. Friedman (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Volume 2. Springer
Hussein, A. M., H. Q. Gheni, W. K. Oleiwi, and Z. Y. Hasan (2020). Prediction of Credit Card Payment Next Month through Tree Net Data Mining Techniques. International Journal for Computing, 19(1); 97–105
James, G., W. Daniela, H. Trevor, and T. Robert (2013). An Introduction to Statistical Learning: with Applications in R. Spinger
Kasinathan, T., D. Singaraju, and S. R. Uyyala (2021). Insect Classification and Detection in Field Crops using Modern Machine Learning Techniques. Information Processing in Agriculture, 8(3); 446–457
Kranth, G. P. R., M. H. Lalitha, L. Basava, and A. Mathur (2018). Plant Disease Prediction using Machine Learning Algorithms. International Journal of Computer Applications, 18(2); 0975 – 8887
Kresnawati, E. S., Y. Resti, B. Suprihatin, M. R. Kurniawan, and W. A. Amanda (2021). Coronary Artery Disease Prediction using Decision Trees and Multinomial Naïve Bayes with k-Fold Cross Validation. Inomatika, 3(2); 172–187
Kusumo, B. S., A. Heryana, O. Mahendra, and H. F. Pardede (2018). Machine Learning-based for Automatic Detection of Corn-Plant Diseases using Image Processing. In International Conference on Computer, Control, Informatics and its Applications; 93–97
Mantas, C. J. and J. Abellan (2014). Credal-C4. 5: Decision Tree based on Imprecise Probabilities to Classify Noisy Data. Expert Systems with Applications, 41(10); 4625–4637
Mishra, S., O. A. Vanli, F. W. Huer, and S. Jung (2016). Regularized Discriminant Analysis for Multi-Sensor Decision Fusion and Damage Detection with Lamb Waves. In Sensors and Smart Structures Technologies for Civil, Mechanical, and Aerospace Systems, Volume 9803. International Society for Optics and Photonics
Ngugi, L. C., M. Abelwahab, and M. Abo-Zahhad (2021). Recent Advances in Image Processing Techniques for Automated Leaf Pest and Disease Recognition–a Review. Information Processing in Agriculture, 8(1); 27–51
Panigrahi, K. P., H. Das, A. K. Sahoo, and S. C. Moharana (2020). Maize Leaf Disease Detection and Classification using Machine Learning Algorithms. In Progress in Computing, Analytics and Networking. Springer
Quinlan, J. R. (1996). Improved use of Continuous Attributes in C4. 5. Journal of Articial Intelligence Research, 4; 77 90
Rajesh, B., M. V. S. Vardhan, and L. Sujihelen (2020). Leaf Disease Detection and Classification by Decision Tree. In International Conference on Trends in Electronics and Informatics; 705–708
Ramasubramanian, K. and A. Singh (2017). Machine Learning using R: with Time Series and Industry-based use Cases in R, 2a. Apress, New Delhi
Resti, Y., F. Burlian, I. Yani, and I. M. Sari (2020). Improved the Cans Waste Classification Rate of Naïve Bayes using Fuzzy Approach. Science and Technology Indonesia, 5(3); 75– 78
Resti, Y., C. Irsan, M. T. Putri, I. Yani, A. Ansyori, and B. Suprihatin (2022). Identification of Corn Plant Diseases and Pests Based on Digital Images using Multinomial Naïve Bayes and K-Nearest Neighbor. Science and Technology Indonesia, 7(1); 29–35
Resti, Y., E. S. Kresnawati, N. R. Dewi, N. Eliyati (2021). Diagnosis of Diabetes Mellitus in Women of Reproductive Age using the Prediction Methods of Naive Bayes, Discriminant Analysis, and Logistic Regression. Science and Technology Indonesia, 6(2); 96–104
Rodriguez, J. D., A. Perez, and J. A. Lozano (2009). Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation. Institute of Electrical and Electronics Engineers Transactions on Pattern Analysis and Machine Intelligence, 32(3); 569–575
Sahith, R., P. Reddy, and S. Nimmala (2019). Decision Tree-based Machine Learning Algorithms to Classify Rice Plant Diseases. International Journal of Innovative Technology and Exploring Engineering, 9(1); 5365–5368
SAS Institute Inc (1999). SAS/GRAPH Colors. In Sas/Graph
Semra, E. and Ö. Ersoy (2010). Comparison of ID3, Fuzzy ID3 and Probabilistic ID3 Algorithms in The Evaluation of Learning Achievements. Journal of Computing, 2(12); 20–25
Singh, A. K., B. Chourasia, N. Raghuwanshi, and K. Raju (2021). BPSO based Feature Selection for Rice Plant Leaf Disease Detection with Random Forest Classier. International Journal of Engineering Trends and Technology, 69(4); 34–43
Sokolova, M. and G. Lapalme (2009). A Systematic Analysis of Performance Measures for Classication Tasks. Information Processing & Management, 45(4); 427–437
Sutha, P., A. Nandhu Kishore, V. Jayanthi, A. Periyanan, and P. Vahima (2021). Plant Disease Detection using Fuzzy Classification. Annals of The Romanian Society for Cell Biology, 25(4); 9430–9441
Syarief, M. and W. Setiawan (2020). Convolutional Neural Network for Maize Leaf Disease Image Classification. Telkomnika, 18(3); 1376–1381
Witten, I., E. Frank, and M. Hall (2011). Data Mining: Practical Machine Learning Tools and Techniques (Google eBook). Complementary Literature None
Xian, T. S. and R. Ngadiran (2021). Plant Diseases Classification using Machine Learning. In Journal of Physics: Conference Series, 1962; 012024
Yunus, M. (2018). Optimasi Penentuan Nilai Parameter Himpunan Fuzzy dengan Teknik Tuning System. MATRIK: Jurnal Manajemen, Teknik Informatika dan Rekayasa Komputer, 18(1); 21–28 (in Indonesia)
Authors
Resti, Y., Irsan, C. ., Amini, M. ., Yani, I., Passarella, R., & Zayantii, D. A. (2022). Performance Improvement of Decision Tree Model using Fuzzy Membership Function for Classification of Corn Plant Diseases and Pests. Science and Technology Indonesia, 7(3), 284–290. https://doi.org/10.26554/sti.2022.7.3.284-290
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