ANALISIS GAMBAR DIGITAL UNTUK SERANGAN PENYAKIT LAYU FUSARIUM DI PISANG MENGGUNAKAN IMAGEJ

Ahmad Zaelani, Wulan S. Kurniajati, Herlina Herlina, Diyah Martanti, Fajarudin Ahmad
10.14203/beritabiologi.v19i3A.3945
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Abstract

Fusarium wilt disease caused by Fusarium oxysporum f. sp. cubense is the most dangerous disease in banana. Recently, development of new banana varieties has been the most effective ways to prevent this disease. To develop the resistant banana, Fusarium severity analysis is the important part in the process of Fusarium disease assessment to quantify the disease severity.. The objective of this study was to develop digital image analysis method for Fusarium severity analysis by using software ImageJ. Pisang Ambon and Pisang Cavendish were used as plant material due to its susceptibility of the disease. Fusarium severity analysis performed as follows (i) Photographing of Fusarium-infected rhizom (ii) Digital image analysis by using ImageJ of the taken image. The analysis result was percentage area of Fusarium-infected rhizom, represented by necrosis and discoloration. The percentage of rhizome infected by Fusarium- of Pisang Ambon#1 was 50.10%, while Pisang Ambon#2 was 22.23%. In addition, the percentage of Pisang Cavendish#1 and Pisang Cavendish#2 was 28.52% and 39.5%,
respectively. Digital image analysis of the sample showed consistent result and more objective. Development of the digital image analysis is not only useful for Fusarium severity analysis in Banana, but also for other crops.

 

 

 

Keywords

Banana, Digital Image Analysis, Fusarium Wilt Disease, ImageJ

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References

Barbedo, Jayme, G.A., Tibola, C.S. and Fernandes, J.M.C., 2015. Detecting Fusarium head blight in wheat kernels using hyperspectral imaging. Biosystems Engineering, 131, pp. 65–76.

Barbedo, Jayme, Garcia Arnal, Koenigkan, L.V. and Santos, T.T., 2016. Identifying multiple plant diseases using digital image processing. Biosystems Engineering, 147, pp. 104–116.

Dhingra, G., Kumar, V. and Joshi, H.D., 2018. Study of digital image processing techniques for leaf disease detection and classification. Multimedia Tools and Applications, 77(15), pp. 19951–20000.

FAO, 2019. Food Outlook, Biannual Report on Global Food Markets. November 2019. Rome.

García-Bastidas, F.A., Van der Veen, A.J.T., Nakasato-Tagami, G., Meijer, H.J.G., Arango-Isaza, R. E. and Kema, G. H. J., 2019. An Improved Phenotyping Protocol for Panama Disease in Banana. Frontiers in Plant Science, 10(August), pp. 1–12.

Khirade, S.D. and Patil, A.B., 2015. Plant disease detection using image processing. Proceedings - 1st International Conference on Computing, Communication, Control and Automation, ICCUBEA 2015, pp. 768–771.

Maloney, P.V., Petersen, S., Navarro, R.A., Marshall, D., McKendry, A.L., Costa, J.M. and Murphy, J.P., 2014. Digital image analysis method for estimation of Fusarium-damaged kernels in wheat. Crop Science, 54(5), pp. 2077–2083.

Maryani, N., Lombard, L., Poerba, Y.S., Subandiyah, S., Crous, P.W. and Kema. G.J.H., 2019. Phylogeny and genetic diversity of the banana Fusarium wilt pathogen Fusarium Oxysporum f. sp. cubense in the Indonesian centre of origin. Studies in Mycology, 92, pp. 155–194.

Meena Prakash, R., Saraswathy, G.P., Ramalakshmi, G., Mangaleswari, K.H. and Kaviya, T., 2018. Leaf Disease Detection, Quantification and Classification Using Digital Image Processing. Proceedings of 2017 International Conference on Innovations in

Information, Embedded and Communication Systems, ICIIECS 2017, 2018-Janua(July), pp. 1–4.

O’Donnell, K., Kistlerr, H.C., Cigelnik, E. and Ploetz, R.C., 1998. Multiple evolutionary origins of the fungus causing panama disease of banana: Concordant evidence from nuclear and mitochondrial gene genealogies. Proceedings of the National Academy of Sciences of the United States of America, 95(5), pp. 2044–2049.

Orjeda, G., 1998. Evaluation of Musa germplasm for resistance to Sigatoka diseases and Fusarium wilt. INIBAP Technical Guidelines 3. International Plant Genetic Resources Institute, Rome, Italy; International Network for the Improvement of Banana and Plantain, Montpellier, France; ACP-EU Technical Centre for Agricultural and and Rural Cooperation, Wageningen, The Netherlands. INIBAP ISBN: 2-910810-24-0

Orr, R., Pattison, A., East, D., Warman, N., O’Neill, W., Czislowski, E. and Nelson, P.N., 2019. Image-based quantification of Fusarium wilt severity in banana. Australasian Plant Disease Notes, 14(1), pp. 2–5.

Ploetz, R. C., 2008. Panama Disease: A Classic and Destructive Disease of Banana Panama Disease: A Classic and Destructive Disease of Banana Panama Disease: A Classic and Destructive Disease of Banana. Agricultural Sciences, 1876(November 2000), pp. 1–7.

Shin, S., Kim, K.H., Kang, C.S., Cho, K.M., Park, C.S., Okagaki, R. and Park, J.C., 2014. A simple method for the assessment of fusarium head blight resistance in Korean wheat seedlings inoculated with Fusarium graminearum. Plant Pathology Journal, 30(1), pp. 25–32.

Smith, L.J., Smith, M.K., Tree, D., O’Keefe, D. and Galea, V.J., 2008. Development of a small-plant bioassay to assess banana grown from tissue culture for consistent infection by Fusarium oxysporum f. sp. cubense. Australasian Plant Pathology, 37(2), pp. 171–179.

Viljoen, A., Mahuku, G., Massawe, C., Ssali, R.T., Kimunye, J., Mostert, G., Ndayihanzamaso, P. and Coyne, D. L., 2016. Banana Pests and Diseases Field Guide for Disease Diagnostics and Data Collection Great Lakes Region of Africa. International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria.

DOI: https://doi.org/10.14203/beritabiologi.v19i3A.3945

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