Drone-Based Vegetation Index Analysis to Estimated Nitrogen Content on The Rice Plantations
##plugins.themes.academic_pro.article.sidebar##
Published
Nov 29, 2022
Download
Statistic
Downloads
Download data is not yet available.
##plugins.themes.academic_pro.article.main##
Abstract
Nitrogen (N) is one of the essential nutrients needed for the growth of rice plants. Therefore, N fertilizing must be applied efficiently to achieve optimal results. Various methods have been used to calculate the N content in rice plants, such as tissue analysis and the use of Soil Plant Analysis Development (SPAD) technology. This technology still has lack of time efficiency. Other technologies are needed to quickly support precise agricultural analysis, such as Unmanned Aerial Vehicles (UAV). This study aimed to analyze the N content of rice crop using the UAV-based vegetation index and to compare the measurement of N content accuracy between SPAD chlorophyll and rice plant vegetation index. This study used survey methods and laboratory tests based on several approaches, namely analysis of photosynthesis physiology, leaves tissue analysis, and vegetation index using UAV. Based on the research results, it was found that the Normalized Difference Vegetation Index value had a strong correlation with N content of leaves tissue and SPAD chlorophyll. While the results of the accuracy test, the results of chlorophyll with SPAD (CI A) have better accuracy than the NDVI vegetation index. The r value between CI A – N leaves and NDVI – N leaves did not show a significant difference. In addition, the correlation results show that N content of leaves (r=0.83), CI A (r=0.88), CI B (r=81), and CI TOT (r=0.87) have a very high correlation with NDVI. This shows a unidirectional relationship between variables so that the NDVI variable can be used as a consideration to determine chlorophyll in the plants studied.
##plugins.themes.academic_pro.article.details##
How to Cite
Adzima, A. F., Neswati, R., Laban, S., Jayadi, M., Anshori, M. F., Mubarak, H., Noviyanti, E., Zaesar Muharram, N. Q., & Mallarangen, A. D. (2022). Drone-Based Vegetation Index Analysis to Estimated Nitrogen Content on The Rice Plantations. Jurnal Agritechno, 15(```2), 132–140. https://doi.org/10.20956/at.vi.928
References
-
Al-doski, J. (2013). NDVI Differencing and Post-classification to Detect Vegetation Changes in Halabja City, Iraq. IOSR Journal of Applied Geology and Geophysics, 1(2) : 01–10. https://doi.org/10.9790/0990-0120110
As’ari, A.R. (2014). Penggunaan Alat Peraga Manipulatif dalam Penanaman Konsep Matematika. Jurnal Matematika, Ilmu Pengetahuan Alam dan Pengajaran. 27(I):1-13
Asmuliani, R., Darmawan, M., Sudiarta, I. M., Megasari. R. (2021). Pertumbuhan Tanaman Padi (Oryza Sativa L.) Varietas Ponelo pada Berbagai Dosis Pupuk Nitrogen dan Jumlah Benih Per Lubang Tanam. Jurnal Pertanian Berkelanjutan. 9(1): 10-17. DOI: http://dx.doi.org/10.30605/perbal.v9i1.1559
Behmann, J., Mahlein, A., Rumpf, T., Römer, C., & Plümer, L. (2014). A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precision Agriculture, 16(3), 239-260. https://doi.org/10.1007/s11119-014-9372-7.
Darmawan, I. G. (2020). Pemanfaatan drone Untuk Pemetaan Potensi Ekowisata Di Kecamatan Panca Jaya, Mesuji. Sakai Sambayan Jurnal Pengabdian kepada Masyarakat, 4(1). https://doi.org/10.23960/jss.v4i1.200.
Endrizal, dan Bobihoe, J. (2004). Efisiensi Penggunaan Pupuk Nitrogen dengan Penggunaan Pupuk Organik pada Tanaman Padi Sawah. Pengkajian dan Pengembangan Teknologi Pertanian. 7 (2): 118-124.
Faozi, K., & Wijonarko, B. R. (2010). Serapan Nitrogen Dan Beberapa Sifat Fisiologi Tanaman Padi Sawah Dari Berbagai Umur Pemindahan Bibit (Nitrogen Uptake and Several Physiological Characters of Lowland Rice From Various Age Seedlings). Pembangunan Pedesaan, 10(2): 93-101.
Fitzgerald, G.J., Rodriguez, D. and O'Leary, G. (2010). Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index—The canopy chlorophyll content index (CCCI). Field Crops Research, 116 (3). pp. 318-324.
Ghozali, I. (2016). Aplikasi Analisis Multivariate Dengan Program SPSS. Semarang: Badan Penerbit Universitas Diponegoro.
Goswami, S., Choudhary, S. S., Chatterjee, C., Mailapalli, D. R., Mishra, A., & Raghuwanshi, N. S. (2021). Estimation of nitrogen status and yield of rice crop using unmanned aerial vehicle equipped with multispectral camera. Journal of Applied Remote Sensing, 15(4), 042407.
Guan, S., Fukami, K., Matsunaka, H., Okami, M., Tanaka, R., Nakano, H., Sakai, T., et al. (2019). Assessing Correlation of High-Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops using Small UAVs. Remote Sensing, 11(2), 112. MDPI AG. Retrieved from http://dx.doi.org/10.3390/rs11020112
J. B. A. Dumas. (1831). “Procedes de I’analyse Organique,” Ann. Chim. Phys 47: 198–205
Latuamury, B., Gunawan, T., & Suprayogi, S. (2016). Pengaruh Kerapatan Vegetasi Penutup Lahan terhadap Karakteristik Resesi Hidrograf pada Beberapa Subdas di Propinsi Jawa Tengah Dan Propinsi DIY. Majalah Geografi Indonesia, 26(2), 98-118. doi:https://doi.org/10.22146/mgi.13418
Liu, Y., Cheng, T., Zhu, Y., Tian, Y., Cao, W., Yao, X., & Wang, N. (2016). Comparative analysis of vegetation indices, non-parametric and physical retrieval methods for monitoring nitrogen in wheat using UAV-based multispectral imagery. 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). https://doi.org/10.1109/igarss.2016.7730920.
Mukhlisin, A., & Soemarno, S. (2020). Estimasi Kandungan Klorofil Tanaman Kopi Robusta (Coffea canephora Var. Robusta) Menggunakan normalized difference vegetation index (NDVI) di Bangelan, Wonosari, Malang. Jurnal Tanah dan Sumberdaya Lahan, 7(2), 329-339. https://doi.org/10.21776/ub.jtsl.2020.007.2.18.,
Muñoz-Huerta, R., Guevara-Gonzalez, R., Contreras-Medina, L., Torres-Pacheco, I., Prado-Olivarez, J., & Ocampo-Velazquez, R. (2013). A review of methods for sensing the nitrogen status in plants: Advantages, disadvantages and recent advances. Sensors, 13(8), 10823-10843. https://doi.org/10.3390/s130810823.
Norasma, C. Y. N., Fadzilah, M. A., Roslin, N. A., Zanariah, Z. W. N., Tarmidi, Z., & Candra, F. S. (2019). Unmanned Aerial Vehicle Applications In Agriculture. IOP Conference Series: Materials Science and Engineering, 506, 012063. https://doi.org/10.1088/1757-899x/506/1/012063.
Patti, P. S., Kaya, E., & Silahooy, C. (2018). Analisis Status Nitrogen Tanah Dalam Kaitannya Dengan Serapan N Oleh Tanaman Padi Sawah Di Desa Waimital, Kecamatan Kairatu, Kabupaten Seram Bagian Barat. Agrologia, 2 (1). https://doi.org/10.30598/a.v2i1.278.
Purevdorj, T.S., Tateishi, R., Ishiyama, T., Honda, Y. (1998). Relationships between Percent Vegetation Cover and Vegetation Indices. International Journal of Remote Sensing. 19(18), 3519-3535, https://doi.org/10.1080/014311698213795
Putri, R. E., Yahya, A., Adam, N. M., and Aziz, S. A. (2019). Rice yield prediction model with respect to crop healthiness and soil fertility. Food Res., 3 (2) : 171 - 176, https://doi.org/10.26656/fr.2017.3(2).117.
Rouse, W.J. Haas, H.R. Schell, A.J. Deering, W.D. Monitoring Vegetation Systems in the Great Plains with ERTS. Third ERTS Symp. 1973, 1: 309–317.
Walsh, O. S., Shafian, S., Marshall, J. M., Jackson, C., McClintick-Chess, J. R., Blanscet, S. M., Swoboda, K., Thompson, C., Belmont, K. M., & Walsh, W. L. (2018). Assessment of UAV based vegetation indices for nitrogen concentration estimation in spring wheat. Advances in Remote Sensing, 07(02), 71-90. https://doi.org/10.4236/ars.2018.72006.
Wan, L., Li, Y., Cen, H., Zhu, J., Yin, W., Wu, W., Zhu, H., Sun, D., Zhou, W., & He, Y. (2018). Combining UAV-based vegetation indices and image classification to estimate flower number in oilseed rape. Remote Sensing, 10(9), 1484. https://doi.org/10.3390/rs10091484
Widiyawati. I., Sugiyanta, Junaedi, A., Widyastuti, R. (2014). Peran Bakteri Penambat Nitrogen untuk Mengurangi Dosis Pupuk Nitrogen Anorganik pada Padi Sawah. J. Agron. Indonesia. 42 (2) : 96 – 102.
Xue, L. H., Luo, W. H., Cao, W. X., & Tian, Y. C. (2003). Research progress on the water and nitrogen detection using spectral reflectance. Journal of Remote Sensing-Beijing, 7(1), 73-80.
Yang, H., Yang, J., Lv, Y., & He, J. (2014). SPAD values and nitrogen nutrition index for the evaluation of rice nitrogen status. Plant Production Science, 17(1), 81-92. https://doi.org/10.1626/pps.17.81.
Yang, H., Yang, X., Heskel, M., Sun, S., & Tang, J. (2017). Seasonal variations of leaf and canopy properties tracked by ground-based NDVI imagery in a temperate forest. Scientific Reports, 7(1). https://doi.org/10.1038/s41598-017-01260-y
Yeom, J., Jung, J., Chang, A., Ashapure, A., Maeda, M., Maeda, A., & Landivar, J. (2019). Comparison of vegetation indices derived from UAV data for differentiation of tillage effects in agriculture. Remote Sensing, 11(13), 1548. https://doi.org/10.3390/rs11131548
Zakiyah, M., Manurung, F., and Wulandari, R.S. (2018). Kandungan Klorofil Daun Pada Empat Jenis Pohon Di Arboretum Sylva Indonesia Pc. Universitas Tanjungpura. J. Hutan Lestari, vol. 6, no. 1, pp. 48– 55, 2018.
Zhang, D., Zhou, X., Zhang, J., Lan, Y., Xu, C., & Liang, D. (2018). Detection of rice sheath blight using an unmanned aerial system with high-resolution color and multispectral imaging. PLOS ONE, 13(5). https://doi.org/10.1371/journal.pone.0187470
Zhang, K., Liu, X., Tahir Ata-Ul-Karim, S., Lu, J., Krienke, B., Li, S., Cao, Q., Zhu, Y., Cao, W., & Tian, Y. (2019). Development of chlorophyll-meter-Index-Based dynamic models for evaluation of high-yield japonica rice production in Yangtze River reaches. Agronomy, 9(2). https://doi.org/10.3390/agronomy9020106