Coffee Leaf Disease Recognition Using Gist Feature
Автор: Md. Burhan Uddin Chowdhury
Журнал: International Journal of Information Engineering and Electronic Business @ijieeb
Статья в выпуске: 2 vol.13, 2021 года.
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Coffee leaf disease recognition is important as its quality can be affected by the disease like –rust. This paper presents a coffee leaf disease recognition system with the help of gist feature. This research can help coffee producers in diagnosis of coffee plants in initial stage. Rocole coffee leaf dataset is considered in this study. Input image is pre-processed first. Resize and filtering is used as pre-processing work. Gist feature is extracted from pre-processed image. Extracted features are trained with machine learning algorithm. In testing phase, features are extracted and tested with trained ML model. Simulation is done with 10 fold cross validation. Different ML models are used and selected the best among them based on performance. SVM achieved overall 99.8% accuracy in recognizing coffee leaf disease.
Leaf disease, gist feature, support vector machine
Короткий адрес: https://sciup.org/15017439
IDR: 15017439 | DOI: 10.5815/ijieeb.2021.02.05
Список литературы Coffee Leaf Disease Recognition Using Gist Feature
- Pereira, Cassiano Spaziani, Rubens José Guimarães, Edson Ampélio Pozza, and Adriano Alves da Silva. (2008). “Controle de Cercosporiose e da Ferrugem do Cafeeiro com Extrato Etanólico de Própolis” Ceres 55(5): 369-376.
- Kushalappa, A.C., M.Akutsu, S.H. Oseguera, G.M.Chaves et al. (1984). “Equations for Predicting the Rate of Coffee Rust Development Based on Net Survival Ratio for Monocyclic Process of Hemileia Vastatrix [Coffea Arabica].” Fitopatologia Brasileira (Brazil)
- Fuentes, Alvaro, Sook Yoon, Sang Cheol Kim, Dong Sun Park. (2017). “A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition”. Sensors, 17(9), 1-21.
- Suhartono, Derwin, Wahyu Aditya, Miranty Lestari, and Muhamad Yasin. (2013). “Expert System in Detecting Coffee Plant Diseases.” International Journal of Electrical Energy 1 (3): 156–162..
- S. D. A, G. A. G, C. P. A, and K. P. L, “ Intelligent autonomous farming robot with plant disease detection using image processing,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no. 4, pp. 1012–1016, 2016
- J. D. Pujari, R. Yakkundimath, and A. S. Byadgi, “Detection and classification of fungal disease with radon transform and support vector machine affected on cereals,” Int. J. of Computational Vision and Robotics, vol. 4, pp. 261–280, 2014.
- Gutte, Vitthal S., Maharudra A.Gitte. (2018). “A Survey on Recognition of Plant Disease with Help of Algorithm”. International Journal of Engineering Science and Computing, 6(6), 7100-7102.
- Selvaraj, Arivazhagan & Shebiah, Newlin & Ananthi, S. & Varthini, S.. (2013) , “Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features”, Agricultural Engineering International: CIGR Journal. 15. 211-217
- Dheeb Albashish & Malik Braik & Sulieman Bani-Ahmad (2011). A framework for detection and classification of plant leaf and stem diseases. 113 - 118. 10.1109/ICSIP.2010.5697452.
- V. Premalatha, S. Valarmathy and M. G. Sumithra, “Disease Identification in Cotton Plants Using Spatial FCM & PNN Classifier”, vol. 3, no. 4, (2015).
- H. Wang, G. Li, Z. Ma and X. Li, “Image Recognition of Plant Diseases Based on Back propagation Networks”, IEEE, (2015)
- S. Phadikar, J. Sil and A. K. Das, “Classification of Rice Leaf Diseases Based on morphological Changes”, Breeding Science, (2008), pp. 93-96.
- A. Oliva and A. Torralba, “Modeling the shape of the scene: A holistic representation of the spatial envelope,” Int. J. Comput. Vision, vol. 42, pp. 145–175, May 2001.
- Douze, M., Jegou, H., Sandhawalia, H., Amsaleg, L., and Schmid, C. (2009). Evaluation of gist descriptors for web-scale image search. In Proceedings of the ACM International Conference on Image and Video Retrieval, CIVR ’09, pages 19:1–19:8, New York, NY, USA. ACM
- Parraga-Alava, Jorge; Cusme, Kevin; Loor, Angélica; Santander, Esneider (2019), “RoCoLe: A robusta coffee leaf images dataset ”, Mendeley Data, V2, doi: 10.17632/c5yvn32dzg.2
- A. P. Marcos, N. L. Silva Rodovalho and A. R. Backes, "Coffee Leaf Rust Detection Using Convolutional Neural Network," 2019 XV Workshop de Visão Computacional (WVC), São Bernardo do Campo, Brazil, 2019, pp. 38-42, doi: 10.1109/WVC.2019.8876931.
- Megha P Arakeri, Malavika Arun, Padmini R K,"Analysis of Late Blight Disease in Tomato Leaf Using Image Processing Techniques", International Journal of Engineering and Manufacturing(IJEM), Vol.5, No.4, pp.12-22, 2015.DOI: 10.5815/ijem.2015.04.02
- Kohei Arai, Indra Nugraha Abdullah, Hiroshi Okumura"Image Identification Based on Shape and Color Descriptors and Its Application to Ornamental Leaf", IJIGSP, vol.5, no.10, pp.1-8, 2013.DOI: 10.5815/ijigsp.2013.10.01
- Heba F. Eid, Ashraf Darwish, "Variant-Order Statistics based Model for Real-Time Plant Species Recognition", International Journal of Information Technology and Computer Science (IJITCS), Vol.9, No.9, pp. 77-84, 2017. DOI: 10.5815/ijitcs.2017.09.08
