Detection of foliar diseases using image processing techniques
Palavras-chave:
affected area, black Sigatoka, Phytophthora infestans, yellow SigatokaResumo
This paper presents the development of a methodology to detect the percentage of affected area of Phytophthora infestans disease in tomato plants, using digital image processing techniques to extract the regions of interest with color analysis, where the YIQ and TSL models for the detection of the disease. The method consists of solving one of the most common problems in images that is segmentation, in this case the background and the disease with the Plant Village database, which was captured under uncontrolled lighting conditions. In the experiments conducted, it is observed that our method achieved a performance of 98.60% for the detection of healthy pixels and 98.17% in detection of sick pixels. This process was subjected to comparison against other alternatives of the state of the art like K-means with HSV and LAB, showing a referred error regarding the leaf size of 4.32 ± 5.44% in the detection of the disease and a computational time of 0.03 ± 0.01 [s] in comparison with the other procedures, in addition, this methodology was implemented to detect the foliar diseases black Sigatoka and yellow Sigatoka in banana leaves obtaining satisfactory results.
Referências
Al-Hiary H, Bani-Ahmad S, Reyalat M, Braik M & ALRahamneh Z (2011) Fast and accurate detection and classification of plant diseases. International Journal of Computer Applications, 17:31- 38.
Arakeri MP, Arun M & Padmini RK (2015) Analysis of late blight disease in tomato leaf using image processing techniques. International Journal of Engineering and Manufacturing, 4:12-22.
Baghel J & Jain P (2016) Disease detection in soya bean using k-means clustering segmentation technique. International journal of computer applications, 145:15-18.
Camargo A & Smith JS (2009) An image-processing based algorithm to automatically identify plant disease visual symptoms. Biosystems engineering, 102:09-21.
Caracol Radio (2018) Colombia es capaz de ser despensa del mundo y potencia agropecuaria: Santos. Available at: <http://caracol.com.co/emisora/2018/02/09/tunja/ 1518213178_343387.html>. Accessed on: June 28th, 2018.
Chaudhary P, Chaudhari AK, Cheeran AN & Godara S (2012) Color transform-based approach for disease spot detection on plant leaf. International Journal of Computer Science and Telecommunications, 3:65-70.
Chen TW, Chen YL & Chien SY (2008) Fast image segmentation based on k-means clustering with histograms in HSV color space. In: 10th Workshop on Multimedia Signal Processing, Cairns. Proceedings, IEEE. p. 322-325.
Escalante M & Farrera R (2004) Epidemiología del tizón tardío (Phytophthora infestans mont de Bary) de la papa en zonas productoras del estado Táchira Venezuela. Bioagro, 16:47-54.
FAO - Food and Agriculture Organization of the United Nations (2014) FAO Statistical Yearbook - Asia food and Agriculture.
Accra, FAO. 112p. Finagro (2014) Perspectiva del sector agropecuario colombiano. Bogotá, Finagro. 28p.
Gonzalez RC, Woods RE & Eddins SL (2009) Digital Image Processing Using MATLAB. 2nd ed. Upper Saddle River, Prentice Hall. 609p.
Guzmán O, Zapata J & Villegas-Estrada B (2009) Diagnóstico de enfermedades de plantas de origen biótico. Agronomía, 17:07- 24.
Hlaing CS & Zaw SMM (2017) Model-based statistical features for mobile phone image of tomato plant disease classification. In: 18th International Conference on Parallel and Distributed Computing, Applications and Technologies, Taipei. Proceedings, IEEE. p. 223-229.
Huang K (2007) Application of artificial neural network for detecting Phalaenopsis seedling diseases using color and texture features. Computers and Electronics in Agriculture, 57:03-11.
Hughes DP & Salathe M (2016) An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv:1511.08060.
Intagri (2015) Enfermedades foliares en Tomate. Available at: https://www.intagri.com/articulos/fitosanidad/enfermedades- foliares-en-tomate. Accessed on: June, 28th, 2018.
Juárez-Becerra GP, Sosa-Morales ME & López-Malo A (2010) Hongos fitopatógenos de alta importancia económica: descripción y métodos de control. Temas Selectos de Ingeniería de Alimentos, 4:14-23.
Lasprilla DM (2011) Estado actual de fruticultura colombiana y perspectivas para su desarrollo. Revista Brasileira de Fruticultura, 33:199-205.
Meunkaewjinda A, Kumsawat P, Attakitmongcol K & Srikaew A (2008) Grape leaf disease detection from color imagery using hybrid intelligent system. 5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology, 1:513-516.
Mohanty SP (2016) Dataset of diseased plant leaf images and corresponding labels. Available at: <https://github.com/ spMohanty/PlantVillage-Dataset>. Accessed on: June 28th, 2018.
Mohanty SP, Hughes D & Salathé M (2016) Using deep learning for image-based plant disease detection. Frontiers in plant science, 7:1419.
Patel SP & Dewangan AK (2017) Automatic detection of plant leaf disease using k-means clustering and segmentation. International Journal of Scientific Research Engineering & Technology, 6:774-779.
Patil SB & Bodhe SK (2011) Leaf disease severity measurement using image processing. International Journal of Engineering and Technology, 3:297-301.
Pérez L (2012) A holistic integrated management approach to control black sigatoka disease of banana caused by Mycosphaerella fijiensis. Available at: <http://www.fao.org/3/a-as177e.pdf>. Accessed on: June 18th, 2018.
Sankaran S, Mishra A, Ehsani R & Davis C (2010) A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture, 72:01-13.
Sharma R, Singh A, Dutta MK, Riha K & Kriz P (2017) Image processing based automated identification of late blight disease from leaf images of potato crops. In: 40th Telecommunications and Signal Processing, Barcelona. Anais, IEEE. p. 758-762.
Singh V & Misra A (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, 4:41-49.
Wiwart M, Fordoñski G, ̄uk-Go3aszewska K & Suchowilska E (2009) Early diagnostics of macronutrient deficiencies in three legume species by color image analysis. Computers and Electronics in Agriculture, 65:125-132.
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