* Corresponding Author
Evaluation Trial of Drought Damage of Rice Based on RGB Aerial Image by UAV
https://doi.org/10.22146/agritech.86077 Yuti Giamerti(1*), Didi Darmadi(2), Ahmad Junaedi(3), Iskandar Lubis(4), Didie Sopandie(5), Ospa Pea Yuanita Meishanti(6), Kartika Sari(7), Chiharu Hongo(8), Koki Homma(9)
(1) Research Organization for Agriculture and Food, National Research and Innovation Agency, Cibinong Science Center, Jl. Raya Jakarta-Bogor, KM. 46, Cibinong, Bogor, West Java 16911
(2) Center for Implementation of Standardization of Agricultural Instruments of Aceh Province, Ministry of Agriculture, Jl. Pang Nyak Makam No. 27, Banda Aceh 24415, Aceh
(3) Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University (Bogor Agricultural University), Jl. Meranti, Kampus IPB Dramaga, Bogor 16680, West Java
(4) Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University (Bogor Agricultural University), Jl. Meranti, Kampus IPB Dramaga, Bogor 16680, West Java
(5) Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University (Bogor Agricultural University), Jl. Meranti, Kampus IPB Dramaga, Bogor 16680, West Java
(6) KH. A. Wahab Hasbullah University Tambakberas, Jl. Garuda No. 9, Tambak Rejo, Jombang, Jombang Regency
(7) Muhammadiyah Metro University, Jl. Ki Hajar Dewantara 166/15 34124 Metro Lampung
(8) Japan Center for Environmental Remote Sensing, Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba-shi, Chiba 263-8522
(9) Graduate School of Agricultural Science, Tohoku University, 468-1 Aramaki Aza Aoba, Aoba-ku, Sendai, Miyagi 980-8572
(*) Corresponding Author
Abstract
Unmanned Aerial Vehicle (UAV) remote sensing is recommended to evaluate damage quickly and quantitatively. Therefore, this study aimed to explore the use of RGB aerial images by UAV for evaluating drought damage of rice through canopy color and coverage. The procedures were conducted in the dry season of 2018 (August – September 2018) at the Balitkabi Experimental field, Muneng, Probolinggo, Indonesia. A split-plot experimental field design was used with 2 factors, namely drought treatments at growth stage (Vegetative/P1, Reproductive/ P2, Generative/P3, and Control/P0), and varieties (Jatiluhur/V1, IPB9G/V2, IPB 3S/V3, Hipa 19/V4, Inpari-17/ V5, Mekongga/V6, Mentik Wangi/V7, Ciherang/V8). Canopy temperature data were then obtained using FLUKE 574 Infrared Thermometer, while images were taken with an RGB camera (Zenmuse X5) attached to Drone DJI Inspire I. The images were taken twice during the treatment (4 DAT and 15 DAT), followed by analysis using QGIS 2.18 and ImageJ. The results showed that RGB aerial images by UAV could be used in agricultural insurance in Indonesia, and similar countries around the world. Although the effect on yield needed to be evaluated, quick assessment by UAV was still an effective tool. In addition, drought damage evaluation through canopy color was better than canopy coverage in terms of analysis. The conversion from RGB to Lab color space increased the determination coefficient in multiple regression of color values against temperature difference (Tc-Ta).
Keywords
Full Text:
PDFReferences
Araus, J. L., & Cairns, J. E. (2014). Field high-throughput phenotyping: the new crop breeding frontier. Trends in Plant Science, 19(1), 52–61. https://doi.org/10.1016/j.tplants.2013.09.008
Baresel, J. P., Rischbeck, P., Hu, Y., Kipp, S., Hu, Y., Barmeier, G., Mistele, B., & Schmidhalter, U. (2017). Use of a digital camera as alternative method for non-destructive detection of the leaf chlorophyll content and the nitrogen nutrition status in wheat. Computers and Electronics in Agriculture, 140, 25–33. https://doi.org/10.1016/j.compag.2017.05.032
DeJonge, K. C., Taghvaeian, S., Trout, T. J., & Comas, L. H. (2015). Comparison of canopy temperature-based water stress indices for maize. Agricultural Water Management, 156, 51–62. https://doi.org/10.1016/j.agwat.2015.03.023
Floreano, D., & Wood, R. J. (2015). Science, technology and the future of small autonomous drones. Nature, 521(7553), 460–466. https://doi.org/10.1038/nature14542
Giamerti, Y., Hongo, C., Saito, D., Caasi, O., Nur Susilawati, P., Shishido, M., Sudiarta, I. P., Sutrisna Wijaya, I. M. A., & Homma, K. (2021). Evaluating Multispectral Imaging for Assessing Bacterial Leaf Blight Damage in Indonesian Agricultural Insurance. E3S Web of Conferences, 232, 03008. https://doi.org/10.1051/e3sconf/202123203008
Hong, M., Bremer, D. J., & van der Merwe, D. (2019). Using Small Unmanned Aircraft Systems for Early Detection of Drought Stress in Turfgrass. Crop Science, 59(6), 2829–2844. https://doi.org/10.2135/cropsci2019.04.0212
Li, L., Nielsen, D. C., Yu, Q., Ma, L., and Ahuja, L. R. (2010). Evaluating the crop water stress index and its correlation with latent heat and CO2 fluxes over winter wheat and maize in the North China plain. Agric. Water Manage. 97 (8), 1146–1155. doi: 10.1016/j.agwat.2008.09.015
Pagola, M., Ortiz, R., Irigoyen, I., Bustince, H., Barrenechea, E., Aparicio-Tejo, P., Lamsfus, C., & Lasa, B. (2009). New method to assess barley nitrogen nutrition status based on image colour analysis. Computers and Electronics in Agriculture, 65(2), 213–218. https://doi.org/10.1016/j.compag.2008.10.003
Park, S., Ryu, D., Fuentes, S., Chung, H., Hernández-Montes, E., and O’Connell, M. (2017). Adaptive estimation of crop water stress in nectarine and peach orchards using high-resolution imagery from an unmanned aerial vehicle (UAV). Remote Sens. 9 (8) 828 . doi: 10.3390/rs9080828
Poblete, T., Ortega-Farias, S., and Ryu, D. (2018). Automatic coregistration algorithm to remove canopy shaded pixels in UAV-borne thermal images to improve the estimation of crop water stress index of a drip-irrigated Cabernet Sauvignon vineyard. Sensors 18 (2) 397. doi: 10.3390/s18020397
Qin, W., Wang, J., Ma, L., Wang, F., Hu, N., Yang, X., Xiao, Y., Zhang, Y., Sun, Z., Wang, Z., & Yu, K. (2022). UAV-Based Multi-Temporal Thermal Imaging to Evaluate Wheat Drought Resistance in Different Deficit Irrigation Regimes. Remote Sensing, 14(21), 5608. https://doi.org/10.3390/rs14215608
Quebrajo, L., Perez-Ruiz, M., Perez-Urrestarazu, L., Martinez, G., and Egea, G. (2018). Linking thermal imaging and soil remote sensing to enhance irrigation management of sugar beet. Biosyst. Eng. 165, 77–87. doi: 10.1016/j. biosystemseng.2017.08.013
Yang, W., Li, C., Yang, H., Yang, G., Feng, H., Han, L., et al. (2018). Monitoring of canopy temperature of maize based on UAV thermal infrared imagery and digital imagery. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering 34 (17), 68–75. doi: 10.11975/j.issn.1002-6819.2018.17.010
Zhang, C., & Kovacs, J. M. (2012). The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture, 13(6), 693–712. https://doi.org/10.1007/s11119-012-9274-5
Zhang, L., Zhang, H., Niu, Y., and Han, W. (2019). Mapping maize water stress based on UAV multispectral remote sensing. Remote Sens. 11 (6), 605. doi: 10.3390/rs11060605
Zhang, J., Virk, S., Porter, W., Kenworthy, K., Sullivan, D., & Schwartz, B. (2019). Applications of Unmanned Aerial Vehicle Based Imagery in Turfgrass Field Trials. Frontiers in Plant Science, 10. https://doi.org/10.3389/fpls.2019.00279
Zhang, W., Han, Y., & Du, H. (2007). Relationship Between Canopy Temperature at Flowering Stage and Soil Water Content, Yield Components in Rice. Rice Science, 14(1), 67–70. https://doi.org/10.1016/S1672-6308(07)60010-9
DOI: https://doi.org/10.22146/agritech.86077
Article Metrics
Abstract views : 134
| 
views : 39
Refbacks
- There are currently no refbacks.
Copyright (c) 2024 Yuti Giamerti, SP., MAgr. Sc.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
agriTECH has been Indexed by:
agriTECH (print ISSN 0216-0455; online ISSN 2527-3825) is published by Faculty of Agricultural Technology, Universitas Gadjah Mada in colaboration with Indonesian Association of Food Technologies.