Detection of Disease and Pest of Kenaf Plant Based on Image Recognition with VGGNet19

Diny Melsye Nurul Fajri, Wayan Firdaus Mahmudy, Titiek Yulianti


One of the advantages of Kenaf fiber as an environmental management product that is currently in the center of attention is the use of Kenaf fiber for luxury car interiors with environmentally friendly plastic materials. The opportunity to export Kenaf fiber raw material will provide significant benefits, especially in the agricultural sector in Indonesia. However, there are problems in several areas of Kenaf's garden, namely plants that are attacked by diseases and pests, which cause reduced yields and even death. This problem is caused by the lack of expertise and working hours of extension workers as well as farmers' knowledge about Kenaf plants which have a terrible effect on Kenaf plants. The development of information technology can be overcome by imparting knowledge into machines known as artificial intelligence. In this study, the Convolutional Neural Network method was applied, which aims to identify symptoms and provide information about disease symptoms in Kenaf plants based on images so that early control of plant diseases can be carried out. Data processing trained directly from kenaf plantations obtained an accuracy of 57.56% for the first two classes of introduction to the VGGNet19 architecture and 25.37% for the four classes of the second introduction to the VGGNet19 architecture. The 5×5 block matrix input feature has been added in training to get maximum results.

Full Text:



Miyagawa and Tranggono, “Kebutuhan Serat Kenaf Sebagai Bahan Baku Industri PT TBINA,” in Seminar Nasional Serat Alam Serat Alam Inovasi Teknologi Serat Alam Mendukung Agroindustri yang Berkelanjutan, 2015, pp. 54–59

E. Alpaydin, Introduction to Machine Learning. London: The MIT Press, 2004.

T. H. Saragih, W. F. Mahmudy, A. L. Abadi, D. M. N. Fajri, and Y. P. Anggodo, “Jatropha Curcas Disease Identification With Extreme Learning Machine,” Indones. J. Electr. Eng. Comput. Sci., vol. 12, no. 2, 2018, doi: 10.11591/ijeecs.v12.i2.pp883-888.

M. Hassaballah and A. I. Awad, Deep Learning in Computer Vision, 1st edition. CRC Press, 2020.

J. Akbar, M. Shahzad, M. I. Malik, A. Ul-hasan, and F. Shafait, “Runway Detection and Localization in Aerial Images Using Deep Learning,” 2019 Digit. Image Comput. Tech. Appl., pp. 1–8, 2019.

F. Ertam, “Deep learning based text classification with Web Scraping methods,” in 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 2018, pp. 1–4, doi: 10.1109/IDAP.2018.8620790.

S. T. Kebir and S. Mekaoui, “An Efficient Methodology of Brain Abnormalities Detection using CNN Deep Learning Network,” in 2018 International Conference on Applied Smart Systems (ICASS), 2018, pp. 1–5, doi: 10.1109/ICASS.2018.8652054.

M. D. Radu, I. M. Costea, and V. A. Stan, “Automatic Traffic Sign Recognition Artificial Inteligence - Deep Learning Algorithm,” 2020, doi: 10.1109/ECAI50035.2020.9223186.

P. Nepal, “VGGNet Architecture Explained,” 2020.

D. C. Khrisne and I. M. A. Suyadnya, “Indonesian Herbs and Spices Recognition using Smaller VGGNet-like Network,” in 2018 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS), Oct. 2018, pp. 221–224, doi: 10.1109/ICSGTEIS.2018.8709135.

S. V Militante and B. D. Gerardo, “Detecting Sugarcane Diseases through Adaptive Deep Learning Models of Convolutional Neural Network,” 2019 IEEE 6th Int. Conf. Eng. Technol. Appl. Sci., pp. 1–5, 2019, doi: 10.1109/ICETAS48360.2019.9117332.

D. M. N. Fajri, W. F. Mahmudy, and T. Yulianti, “Detection of Disease and Pest of Kenaf Plant using Convolutional Neural Network,” J. Inf. Technol. Comput. Sci., vol. 6, no. 1, p. 18, Apr. 2021, doi: 10.25126/jitecs.202161195.

T. Yulianti and Supriyono, “Penyakit Tanaman Kenaf dan Pengendaliannya,” in Monograf Balittas: Kenaf (Hibiscus cannabinus L.), 2009, p. 107.

P. Ranali, “A survey of Hemp Pest and Disease,” in Advances in Hemp Research, P. Ranalli, Ed. Boca Ratoon, London, New York: CRC Press Taylor & Francis Group, 1999, pp. 109–122.

J. K. Gill, “Automatic Log Analysis using Deep Learning and AI,” 2018.

Z. Monge, “Does Deep Learning Really Require ‘Big Data’? — No!,” Medium Towards Data Science, 2018.

Y. Le Cun et al., Handwritten Digit Recognition with a Back-propagation Network. San Francisco: Morgan Kaufmann Publishers Inc., 1990.

R. H. R. Hannloser, R. Sarpeshkar, M. A. Mahowald, R. Douglas, and H. S. Seung, “Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit,” Nature, vol. 405, pp. 947–951, 2000, doi: 10.1038/35016072.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Network from Overfitting,” J. Mach. Learn. Res. 15, pp. 1929–1958, 2014.

D. Stathakis, “How many hidden layers and nodes?,” Int. J. Remote Sens., vol. 30, no. 8, pp. 2133–2147, 2009, doi: 10.1080/01431160802549278.



  • There are currently no refbacks.

Copyright (c) 2021 Knowledge Engineering and Data Science

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Flag Counter

Creative Commons License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

View My Stats