Estimation of Blast Disease Attack Intensity on Rice Plant through NDVI (Normalized Difference Vegetation Index) Image Approach

Authors

  • I Made Prasetia Candra Andika
  • I Made Anom S. Wijaya
  • Ida Bagus Putu Gunadnya

DOI:

https://doi.org/10.24843/JBETA.2019.v07.i24

Keywords:

attack intensity, blast disease, estimated, NDVI

Abstract

Blast is one of disease that is dangerous for rice plants. This disease can attack in every phase of growth. Calculation of the intensity of blast disease attacks is still done manually. Technology development is needed in estimating the intensity of blast disease attacks through NDVI imagery. This study purpose (1) to get the best NDVI aerial photo altitude, (2) to get the age of rice plants with the highest attack intensity of blast disease, (3) to get a relationship between the intensity of blast disease and the NDVI value of rice plants. This study use Drone DJI Phantom 4 with lens NDVI. Processing data using Web Drone Deploying and Arc Gis 10.3 software. Based on the analysis results, the best detail of 200% zooming results obtained altitude of the NDVI image acquisition that is 20 m with pixel density of 1,4732 cm/pixel. The highest intensity of blast disease attacks occurs at the age of 98 days after planting. The relationship between the intensity of blast disease and NDVI value has a determination coefficient of 0.986. The regression equation obtained in this study is y = -23345x3+ 21191x2-6416,8x + 665,07 with an estimated accuracy of 91,74%

References

Direktorat Perlindungan Tanaman Pangan. 2007. Pedoman Pengamatan dan Pelaporan Perlindungan Tanaman Pangan. Departemen Pertanian. Jakarta

Hakim, A.R. 2011. Perencanaan Sistem Informasi Pengukuran Konduktivitas Hidraulik Tidak Jenuh Tanah dengan Sensor Tensiometer dan Higrometer Digital. SkripsiJurusan Teknik Elektro Fakultas Teknik Universitas Jember.

Uktoro,A.I. 2017. Analisis Citra Drone untuk MonitoringKesehatan Tanaman Kelapa Sawit.Fakultas Teknologi Pertanian. InstitutPertanian Stiper.Yogyakarta.

Lillesand, T.M. dan R.W. Kiefer. 1997. Penginderaan Jauh dan Interprestasi (Terjemahan). Gadjah Mada University Press ,Yogyakarta.

Ou SH.1985. Rice Diseases Second Edition. C.A.B. International, Farnham House. Farnham Royal.Slough

Parsa, I.M. 2014. Pemanfaatan Data Penginderaan Jauh Resolusi Menengah/Tinggi untuk Estimasi Luas Panen Tanaman Padidi Sentra Produksi Padi.LAPAN. Jakarta.

Santika, I.W.A.2016. Pendugaan Hasil Panen Padi Melalui Foto Udara. Jurnal BETA(Biosistem dan Teknik Pertanian).Universitas Udayana. Jimbaran.

Santika,A dan Sunaryo. 2008. Teknik Pengujian Galur Padi Gogo terhadap Penyakit Blas (Pyricularia grisea). Buletin Teknik Pertanian 13(1):1-8.

Sudarmo, S. 1990. Pengendalian Serangan HamaPenyakit dan Gulma Padi. Konisius, Yogyakarta.

Virma, C.A. 2013. Analisis Perubahan Kerapatan Vegetasi Kota Semarang Menggunakan Bantuan Teknologi Penginderaan Jauh (Skripsi). Universitas Negeri Semarang. Semarang.

Yulianto dan Subiharta. 2009. Ketahanan Padi Varietas Unggul Baru Terhadap Penyakit Blas (Magnaporthegricea (T.T Hebert) M.E. Barr) Di Lahan Sawah Tadah Hujan Kabupaten Pemalang. Prosiding Seminar Ilmiah Nasional. BBP2TP dan UPN.

Published

2019-06-26

How to Cite

Andika, I. M. P. C., Wijaya, I. M. A. S., & Gunadnya, I. B. P. (2019). Estimation of Blast Disease Attack Intensity on Rice Plant through NDVI (Normalized Difference Vegetation Index) Image Approach. Jurnal BETA (Biosistem Dan Teknik Pertanian), 7(2), 1–10. https://doi.org/10.24843/JBETA.2019.v07.i24

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