Classification of Tomato Plant Diseases through Leaf using Gray-Level Co-Occurrence Matrix and Color Moment with Convolutional Neural Network Methods

Classification of Tomato Plant Diseases through Leaf using Gray-Level Co-Occurrence Matrix and Color Moment with Convolutional Neural Network Methods

• By Anton; Rustad,Supriadi, Shidik, Guruh Fajar; Syukur, Abdul;
• Seminar Internasional Bereputasi
• 294
• 291-299

Tomato is a fruit vegetable source of vitamins and minerals, in addition to being consumed as fresh fruit it can also be processed into food industry raw materials such as fruit juices and sauces. However, due to various causes such as diseases, pest attacks, and unstable weather conditions cause a decrease in the quality and quantity of production. In order to contribute to maintaining the productivity of tomato plants, the use of technology can be an alternative to be applied to the cultivation of tomato plants. This study applies image processing techniques to detect the texture of affected leaf using Gray-level Cooccurence Matrix (GLCM) extraction and Color Moment using Convolutional Neural Network (CNN) method. Among the diseases that often occur in tomato leaf are late blight, Septoria spot, bacterial spot, Target Spot, Early blight, leaf curl, Spider mites Two spotted spider mites, and Leaf Mold. In this study a combination of GLCM-Color Moment and CNN method was chosen because of its reliability in identifying and classifying plant diseases compared to only using CNN. In this study, using a data set from Plant Village totaling 16.012 images.

• Katakunci Tomato Plant diseases, Color Moment, GLCM, CNN, deep learning
• URL https://www.springer.com/gp/book/9789811552236#aboutBook
• Penerbit ,

REFERENSI

[1] C. Wasonowati, “Meningkatkan Pertumbuhan Tanaman Tomat (Lycopersicon esculentum) Ddengan Sistem Budidaya Hidroponik,” Agrovigor, vol. 4, no. 1, pp. 21–28, 2011. [2] D. Homepage, “Tingkatkan konsumsi sayur dan buah nusantara menuju masyarakat hidup sehat,” last accessed 2019/12/10., 2017. [3] F. Alviansyah, I. Ruslianto, and M. Diponegoro, “Identifikasi Penyakit Pada Tanaman Tomat Berdasarkan Warna Dan Bentuk Daun Dengan Metode Naive Bayes Classifier Berbasis Web,” J. Coding Sist. Komput. Untan, vol. 05, no. 1, pp. 23–32, 2017. [4] H. Sabrol and K. Satish, “Tomato Plant Disease Classification in Digital Images using Classification Tree,” pp. 1242–1246, 2016. [5] A. Camargo and J. S. Smith, “Image pattern classification for the identification of disease causing agents in plants,” Comput. Electron. Agric., vol. 66, no. 2, pp. 121–125, 2009. [6] M. Shabanzade, M. Zahedi, and S. Amin Aghvami, “Combination of Local Descriptors and Global Features for Leaf Recognition,” Signal Image Process.  An Int. J., vol. 2, no. 3, pp. 23–31, 2011. [7] J. R. Aduwo, E. Mwebaze, and J. A. Quinn, “Automated Vision-Based Diagnosis of Cassava Mosaic Disease,” Proc. ICDM Work. Data Min. Agric., pp. 114–122, 2010. [8] J. K. Patil, “Color Feature Extraction of Tomato Leaf Diseases,” Int. J. Eng. Trends Technol., vol. 2, no. 2, pp. 72–74, 2011. [9] M. M. Santoni, D. I. Sensuse, A. M. Arymurthy, and M. I. Fanany, “Cattle Race Classification Using Gray Level Co-occurrence Matrix Convolutional Neural Networks,” Procedia Computer Science, vol. 59. ICCSCI 2015, pp. 493–502, 2015. [10] H. Yalcin and S. Razavi, “Plant Classification using Convolutional Neural Networks Hulya,” Adv. Intell. Syst. Comput., vol. 898, pp. 357–365, 2019. [11] R. A. Priyadharshini, “Maize leaf disease classification using deep convolutional neural networks,” Neural Comput. Appl., vol. 3, 2019. [12] X. Zhang, Y. Qiao, F. Meng, C. Fan, and M. Zhang, “Identification of maize leaf diseases using improved deep convolutional neural networks,” IEEE Access, vol. 6, pp. 30370–30377, 2018. [13] J. C. Comstock and R. S. Lentini, “Sugarcane Mosaic Virus Disease,” Univ. Florida, pp. 1–3, 2005. [14] R. G. De Luna, E. P. Dadios, and A. A. Bandala, “Automated Image Capturing System for Deep Learning-based Tomato Plant Leaf Disease Detection and Recognition,” IEEE Reg. 10 Annu. Int. Conf. Proceedings/TENCON, vol. 2018-Octob, no. October, pp. 1414–1419, 2018. [15] W. Zaka-ud-din, Muhammad Aziz, Sumair Rashid, Junaid Ismail, “Classification of Disease in Tomato Plants ’ Leaf Using Image Segmentation and SVM,” Tech. Journal, Univ. Eng. Technol., vol. 23, no. 2, pp. 81–88, 2018. [16] M. A. N. Patil and M. V. Pawar, “Detection and Classification of Plant Leaf Disease,” Iarjset, vol. 4, no. 4, pp. 72–75, 2017. [17] Y. Sharath Kumar, “Leaf recognition for plant classification using GLCM and PCA methods,” Orient. J. Comput. Sci. Technol., vol. 3, no. 1, pp. 31–36, 2010. [18] D.N.D.Harini and Dr.D.Lalitha Bhaskari, “Identification of Leaf Diseases in TomatoPlant Based on Wavelets and PCA,” 2011 World Congr. Inf. Commun. Technol., pp. 1398–1403, 2011. [19] M. Sibiya and M. Sumbwanyambe, “A Computational Procedure for the Recognition and Classification of Maize Leaf Diseases Out of Healthy Leaves Using Convolutional Neural Networks,” AgriEngineering, vol. 1, no. 1, pp. 119–131, 2019. [20] C. E. Honeycutt and R. Plotnick, “Image analysis techniques and gray-level co-occurrence matrices (GLCM) for calculating bioturbation indices and characterizing biogenic sedimentary structures,” Comput. Geosci., vol. 34, no. 11, pp. 1461–1472, 2008.