A Bootstrap-Aggregating in Random Forest Model for Classification of Corn Plant Diseases and Pests
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Keywords:
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Bootstrap-Aggregating, Classification, Corn Plant Disease and Pest, Decision Tree, Random Forest
Abstract
Control of diseases and pests of maize plants is a significant challenge to ensure global food security, self-sufficiency, and sustainable agriculture. Classification or early detection of diseases and pests of corn plants is intended to assist the control process. Random forest is a classification model in tree-based statistical learning in making decisions. This approach is an ensemble method that generates many decision trees and makes classification decisions based on the majority of trees selecting the same class. However, tree-based methods are often unstable when small changes or disturbances exist in the learning data. Such instability can produce significant variances and affect model performance. This study classifies diseases and pests of the corn plant using a random forest method based on bootstrap-aggregating. It fits multiple models of a single random forest, then combines the predictions from all models and determines the final result using majority voting. The results showed that the bootstrap aggregating could improve the classification of diseases and pests of maize using a random forest if the number of trees is optimal.
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Singh, A., B. Chourasia, N. Raghuwanshi, and K. Raju (2021). BPSO Based Feature Selection for Rice Plant Leaf Disease Detection with Random Forest Classifier. International Journal of Engineering Trends and Technology, 69(4); 34–43
Sokolova, M. and G. Lapalme (2009). A Systematic Analysis of Performance Measures for Classification Tasks. Information Processing & Management, 45(4); 427–437
Syarief, M. and W. Setiawan (2020). Convolutional Neural Network for Maize Leaf Disease Image Classification. Telkomnika (Telecommunication Computing Electronics and Control), 18(3); 1376–1381
Witten, I. H. and E. Frank (2002). Data Mining: Practical Machine Learning Tools and Techniques With Java Implementations. Acm Sigmod Record, 31(1); 76–77
Xian, T. S. and R. Ngadiran (2021). Plant Diseases Classification Using Machine Learning. Conference Series, 1962(1); 012024
Zhu, J., S. Huang, Y. Shi, K. Wu, and Y. Wang (2021). A Method of Random Forest Classification based on Fuzzy Comprehensive Evaluation. International Conference on Dependable System and Their Applications, 4(5); 178–183
Aronoff, S. (1985). The Minimum Accuracy Value as An Index Of Classification Accuracy. Photogrammetric Engineering and Remote Sensing, 51(1); 99–111
Conn, D., T. Ngun, G. Li, and C. M. Ramirez (2019). Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-dimensional Data. Journal of Statistical Software, 91; 1–25
Dewantara, A. W., D. Ratna, and S. J. Santosa (2020). Kajian Macam Pupuk Hayati Terhadap Intensitas Kerusakan Hama Belalang Pada Tanaman Jagung Hitam. Innofarm: Jurnal Inovasi Pertanian, 22(1); 29–35 (In Indonesia)
Dinesh, S. and T. Dash (2016). Reliable Evaluation of Neural Network for Multiclass Classification of Real-world Data. arXiv preprint arXiv:1612.00671, 11(2); 30–35
Gareth, J., W. Daniela, H. Trevor, and T. Robert (2013). An Introduction to Statistical Learning: with Applications In R. In Paper History of Documents, 3(5); 34–38
Girsang, W., J. Purba, and S. Daulay (2020). Uji Aplikasi Agens Hayati Tribac Mengendalikan Pathogen Hawar Daun (Helminthosporium SP.) Tanaman Jagung (Zea Mays L.). Jurnal Ilmiah Pertanian, 17(1); 51–59 (In Indonesia)
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
Jafarzadeh, H., M. Mahdianpari, E. Gill, F. Mohammadimanesh, and S. Homayouni (2021). Bagging and Boosting Ensemble Classifiers for Classification of Multispectral, Hyperspectral and Polsar Data: A Comparative Evaluation. Remote Sensing, 13(21); 4405
Jin, Z., J. Shang, Q. Zhu, C. Ling, W. Xie, and B. Qiang (2020). RFRSF: Employee Turnover Prediction Based on Random Forests and Survival Analysis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3(4); 503–515
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
Kuhn, M. and K. Johnson (2013). Applied predictive modeling, volume 26. Springer
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. International Conference on Computer, Control, Informatics and Its Applications: Recent Challenges in Machine Learning for Computing Applications, IC3INA 2018, 4(5); 93–97
Liu, J. and X. Wang (2021). Plant Diseases and Pests Detection Based On Deep Learning: A Review. Plant Methods, 17; 1–18
Lubis, A. A. N., R. Anwar, B. P. Soekarno, B. Istiaji, S. Dewi, and D. Herawati (2020). Serangan Ulat Grayak Jagung (Spodoptera frugiperda) pada Tanaman Jagung Di Desa Petir, Kecamatan Daramaga, Kabupatem Bogor dan Potensi Pengendaliannya Menggunakan Metarizhium Rileyi. Jurnal Pusat Inovasi Masyarakat (PIM), 2(6); 931–939 (In Indonesia)
Megasari, R. and M. Nuriyadi (2019). The Inventory of Pests and Diseases of Corn Plants (Zea mays L.) and its Control Inventarisasi Hama dan Penyakit Tanaman Jagung (Zea mays L.). Musamus Journal of Agrotechnology Research, 2(1); 1–12
Mengistu, M. S. G. . M. D., A. D. (2018). An Automatic Coffee Plant Diseases Identification Using Hybrid Approaches of Image Processing and Decision Tree. Indonesian Journal of Electrical Engineering and Computer Science, 9(3); 806–811
Mirsam, H., S. Suriani, N. Djaenuddin, A. T. Makkulawu, and F. Abdullah (2021). Evaluasi Ketahanan Genotipe Jagung Hibrida terhadap Penyakit Hawar Daun Maydis dan Karat Daun. Seminar Nasional Lahan Suboptimal, 9(2021); 305–313 (In Indonesia)
Mishra, S., O. A. Vanli, F. W. Huffer, and S. Jung (2016). Regularized Discriminant Analysis for Multi-sensor Decision Fusion and Damage Detection With Lamb Waves. SPIE, 9803; 728–741
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. Proceedings of ICCAN, 4(5); 659–669
Prasojo, B. and E. Haryatmi (2021). Analisa Prediksi Kelayakan Pemberian Kredit Pinjaman dengan Metode Random Forest. Jurnal Nasional Teknologi dan Sistem Informasi, 7(2); 79–89 (In Indonesia)
Purwanto, D. S., H. Nirwanto, and S. Wiyatiningsih (2017). Model Epidemi Penyakit Tanaman: Hubungan Faktor Lingkungan Terhadap Laju Infeksi dan Pola Sebaran Penyakit Bulai (peronosclerospora maydis) pada Tanaman Jagung Di Kabupaten Jombang. Berkala Ilmiah Agroteknologi-PLUMULA, 5(2); 138–152 (In Indonesia)
Ramasubramanian, K. and A. Singh (2016). Machine Learning Using R: With Time Series and Industry-Based Use Cases in R. Apress, 2(321); 42–48
Resti, Y., C. Irsan, M. Amini, I. Yani, R. Passarella, and D. Zayanti (2022a). 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
Resti, Y., C. Irsan, M. T. Putri, I. Yani, A. Ansyori, and B. Suprihatin (2022b). 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
Sahith, R., P. V. P. Reddy, and S. Nimmala (2021). Decision Tree-based Machine Learning Algorithms to Classify Rice Plant Diseases: A Recent Study. Advanced Aspects of Engineering Research, 16; 52–59
Saifudin, A., U. Nabillah, and T. Desyani (2020). Bagging Technique to Reduce Misclassification in Coronary Heart Disease Prediction Based on Random Forest. Journal of Physics: Conference Series, 1477(3); 032009
Salman, R., A. Alzaatreh, H. Sulieman, and S. Faisal (2021). A Bootstrap Framework for Aggregating Within and Between Feature Selection Methods. Entropy, 23(2); 200
Sari, D. M. (2018). Bakteri Antagonis Dari Sumber Air Panas Dan Uji Kemampuan Antagonisnya Terhadap Helminthosporium Turcicum (Pass.) Penyebab Hawar Daun Pada Tanaman Jagung (Zea Mays L.). In Paper History of Documents, 4(5); 54–60 (In Indonesia)
Singh, A., B. Chourasia, N. Raghuwanshi, and K. Raju (2021). BPSO Based Feature Selection for Rice Plant Leaf Disease Detection with Random Forest Classifier. International Journal of Engineering Trends and Technology, 69(4); 34–43
Sokolova, M. and G. Lapalme (2009). A Systematic Analysis of Performance Measures for Classification Tasks. Information Processing & Management, 45(4); 427–437
Syarief, M. and W. Setiawan (2020). Convolutional Neural Network for Maize Leaf Disease Image Classification. Telkomnika (Telecommunication Computing Electronics and Control), 18(3); 1376–1381
Witten, I. H. and E. Frank (2002). Data Mining: Practical Machine Learning Tools and Techniques With Java Implementations. Acm Sigmod Record, 31(1); 76–77
Xian, T. S. and R. Ngadiran (2021). Plant Diseases Classification Using Machine Learning. Conference Series, 1962(1); 012024
Zhu, J., S. Huang, Y. Shi, K. Wu, and Y. Wang (2021). A Method of Random Forest Classification based on Fuzzy Comprehensive Evaluation. International Conference on Dependable System and Their Applications, 4(5); 178–183
Authors
Resti, Y., Irsan, C., Latif, J. F., Yani, I., & Dewi, N. R. (2023). A Bootstrap-Aggregating in Random Forest Model for Classification of Corn Plant Diseases and Pests. Science and Technology Indonesia, 8(2), 288–297. https://doi.org/10.26554/sti.2023.8.2.288-297
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