Above-ground vegetation indices and yield attributes of rice crop using unmanned aerial vehicle combined with ground truth measurements


  • Piyanan PIPATSITEE National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Paholyothin Rd., Khlong Nueng, Khlong Luang, Pathum Thani 12120 (TH)
  • Apisit EIUMNOH National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Paholyothin Rd., Khlong Nueng, Khlong Luang, Pathum Thani 12120 (TH)
  • Rujira TISARUM National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Paholyothin Rd., Khlong Nueng, Khlong Luang, Pathum Thani 12120 (TH)
  • Kanyarat TAOTA National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Paholyothin Rd., Khlong Nueng, Khlong Luang, Pathum Thani 12120 (TH)
  • Sumaid KONGPUGDEE National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Paholyothin Rd., Khlong Nueng, Khlong Luang, Pathum Thani 12120 (TH)
  • Kampol SAKULLEERUNGROJ National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Paholyothin Rd., Khlong Nueng, Khlong Luang, Pathum Thani 12120 (TH)
  • Suriyan CHA-UM National Center for Genetic Engineering and Biotechnology (BIOTEC), National Science and Technology Development Agency (NSTDA), 113 Paholyothin Rd., Khlong Nueng, Khlong Luang, Pathum Thani 12120 (TH)




non-destructive measurement; RedEdge; RGB imagery; total chlorophyll; unmanned aerial vehicle; yield attribute


Rice is an important economic and staple crop in several developing countries. Indica rice cultivars, ‘KDML105’ and ‘RD6’ are clear favourites, popular throughout world for their cooking quality, aroma, flavour, long grain, and soft texture, thus consequently dominate major plantation area in Northeastern region of Thailand. The objective of present study was to validate UAV (unmanned aerial vehicle)-derived information of rice crop traits with ground truthing non-destructive measurements in these rice varieties throughout whole life span under field environment. Plant height of cv. ‘KDML105’ was more than cv. ‘RD6’ for each respective stage. Whereas, number of tillers per clump in ‘KDML105’ exhibited stability at each developmental stage, which was in contrast to ‘RD6’ (increased continuously). Moreover, 1,000 grain weight, total grain weight and aboveground biomass were higher in ‘KDML105’ than in ‘RD6’ by 1.20, 1.82 and 3.82 folds. Four vegetative indices, ExG, EVI2, NDVI and NDRE derived from UAV platform proved out to be excellent parameters to compare KDML105 and RD6, especially in the late vegetative and reproductive developmental stages. Positive relationships between NDVI and NDRE, NDRE and total yield traits, as well as NDVI and aboveground biomass were demonstrated. In contrast, total chlorophyll pigment in cv. ‘RD6’ was higher than in cv. ‘KDML105’ leading to negative correlation with NDVI. ‘KDML105’ reflected rapid adaptation to Northeastern environments, leading to maintenance of plant height and yield components. Vegetation indices derived from UAV platform and ground truth non-destructive data exhibited high correlation. ‘KDML105’ was rapidly adapted to NE environments when compared with ‘RD6’, leading to maintenance of physiological parameters (detecting by UAV), the overall growth performances and yield traits (measuring by ground truth method). This study advocates harnessing and adopting the approach of UAV platform along with ground truthing non-destructive measurements of assessing a species/cultivars performance at broad land-use scale.


Arunrat N, Pumijumnong N, Hatano R (2017). Practices sustaining soil organic matter and rice yield in a tropical monsoon region. Soil Science and Plant Nutrition 63:274‒287. https://www.doi.org/10.1080/00380768.2017.1323546

Aung Nan MS, Janto J, Sribunrueang A, Monkham T, Sanitchon J, Chankaew S (2019). Field evaluation of RD6 introgression lines for yield performance, blast, bacterial blight resistance, and cooking and eating qualities. Agronomy 9:825. https://www.doi.org/10.3390/agronomy9120825

Barnes EM, Clarke TR, Richards SE, Colaizzi PD, Haberland J, Kostrzewski M, … Thompson T (2000). Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In: Robert PC, Rust RH, Larson WE (eds.), Proceedings of the 5th International Conference on Precision Agriculture, Bloomington, MN, USA.

Barrero O, Perdomo SA (2018). RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields. Precision Agriculture 19:809‒822. https://www.doi.org/10.1007/s11119-017-9558-x

Cárdenas DAG, Valencia JAR, Velásquez DFA, Gonzalez JRP (2018). Dynamics of the indices NDVI and GNDVI in a rice growing in its reproduction phase from multi-spectral aerial images taken by drones. In: International Conference of ICT for Adapting Agriculture to Climate Change. Springer, Cham. pp 106‒119. https://www.doi.org/10.1007/978-3-030-04447-3_7

Cooper M, Rajatasereekul S, Immark S, Fukai S, Basnayake J (1999). Rainfed lowland rice breeding strategies for Northeast Thailand.: I. Genotypic variation and genotype× environment interactions for grain yield. Field Crops Research 64:131‒151. https://www.doi.org/10.1016/S0378-4290(99)00056-8

Cortazar B, Koydemir HC, Tseng D, Feng S, Ozan A (2015). Quantification of plant chlorophyll content using Google glass. Lab on a Chip 15(7):1708‒1716. https://www.doi.org/10.1039/c4lc01279h

Devia CA, Rojas J, Petro E, Mondragon I, Patino D, Rebolledo C, Colorado J (2019a). Aerial monitoring of rice crop variables using an UAV robotic system. In: ICINCO 2019 - International Conference on Informatics in Control, Automation and Robotics. 29-31 July. Prague, Czech Republic, pp 1‒7. https://www.doi.org/10.5220/0007909900970103

Devia CA, Rojas JP, Petro E, Martinez C, Mondragon IF, Patiño D, Rebolledo MC, Colorado J (2019b). High-throughput biomass estimation in rice crops using UAV multispectral imagery. Journal of Intelligent and Robotic Systems 96:573‒589. https://www.doi.org/10.1007/s10846-019-01001-5

Duan B, Fang S, Zhu R, Wu X, Wang S, Gong Y, Peng Y (2019a). Remote estimation of rice yield with unmanned aerial vehicle (UAV) data and spectral mixture analysis. Frontiers in Plant Science 10:204. https://www.doi.org/10.3389/fpls.2019.00204

Duan B, Liu Y, Gong Y, Peng Y, Wu X, Zhu R, Fang S (2019b). Remote estimation of rice LAI based on Fourier spectrum texture from UAV image. Plant Methods 15:124. https://www.doi.org/10.1186/s13007-019-0507-8

Gitelson AA, Merzlyak MN (1996). Signature analysis of leaf reflectance spectra: algorithm development for remote sensing of chlorophyll. Journal of Plant Physiology 148:494‒500. https://www.doi.org/10.1016/S0176-1617(96)80284-7

Gitelson AA, Gritz Y, Merzlyak MN (2003). Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. Journal of Plant Physiology 160:271‒282. https://www.doi.org/10.1078/0176-1617-00887

Hayashi S, Kamoshita A, Yamagishi J, Kotchasatit A, Jongdee B (2010). High-yielding crop management by enhancing growth in reproductive stage of direct-seeded rainfed lowland rice (Oryza sativa L.) in Northeast Thailand. Plant Production Science 13:104‒115. https://www.doi.org/10.1626/pps.13.104

Huang J, Gómez-Dans JL, Huang H, Ma H, Wu Q, Lewis PE, … Zhao F (2019). Assimilation of remote sensing into crop growth models: Current status and perspectives. Agricultural and Forest Meteorology 276:107609. https://www.doi.org/10.1016/j.agrformet.2019.06.008

Hungsaprug K, Kojonna T, Samleepan M, Punchkhon C, Ut-Khao W, Kositsup B, … Chadchawan S (2020). Chlorophyll fluorescence, leaf gas exchange, and genomic analysis of chromosome segment substitution rice lines exposed to drought stress. Photosynthetica 58:214‒227. https://www.doi.org/10.32615/ps.2019.144

Hussain F, Bronson KF, Peng S (2000). Use of chlorophyll meter sufficiency indices for nitrogen management of irrigated rice in Asia. Agronomy Journal 92:875‒879. https://www.doi.org/10.2134/agronj2000.925875x

Hassan MA, Yang M, Rasheed A, Yang G, Reynolds M, Xia X, … He Z (2019). A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant Science 282:95-103. https://www.doi.org/10.1016/j.plantsci.2018.10.022

IRRI (2002). Standard evaluation system for rice. 4th edition. International Rice Research Institute, P.O. Box 933, Manila, Philippines.

Jiang Z, Huete AR, Didan K, Miura T (2008). Development of a two-band enhanced vegetation index without a blue band. Remote Sensing of Environment 112(10):3833‒3845. https://www.doi.org/10.1016/j.rse.2008.06.006

Jongdee B, Pantuwan G, Fukai S, Fischer K (2006). Improving drought tolerance in rainfed lowland rice: an example from Thailand. Agricultural Water Management 80:225‒240. https://www.doi.org/10.1016/j.agwat.2005.07.015

Kasampalis DA, Alexandridis TK, Deva C, Challinor A, Moshou D, Zalidis G (2018). Contribution of remote sensing on crop models: a review. Journal of Imaging 4:52. https://www.doi.org/10.3390/jimaging4040052

Kawamura K, Asai H, Yasuda T, Khanthavong P, Soisouvanh P, Phongchanmixay S (2020). Field phenotyping of plant height in an upland rice field in Laos using low-cost small unmanned aerial vehicles (UAVs). Plant Production Science https://www.doi.org/10.1080/1343943X.2020.1766362

Khush GS (2005). What it will take to feed 5.0 billion rice consumers in 2030. Plant Molecular Biology 59:1‒6. https://www.doi.org/10.1007/s11103-005-2159-5

Lee MK, Golzarian MR, Kim I (2020). A new color index for vegetation segmentation and classification. Precision Agriculture https://www.doi.org/10.1007/s11119-020-09735-1

Li S, Yuan F, Ata-UI-Karim ST, Zheng H, Cheng T, Liu X, … Cao Q (2019). Combining color indices and textures of UAV-based digital imagery for rice LAI estimation. Remote Sensing 11:1763. https://www.doi.org/10.3390/rs11151763

Maes WH, Steppe K (2019). Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends in Plant Science 24:152‒164. https://www.doi.org/10.1016/j.tplants.2018.11.007

Mosleh MK, Hassan QK, Chowdhury EH (2015). Application of remote sensors in mapping rice area and forecasting its production: A review. Sensors 15(1):769‒791. https://www.doi.org/10.3390/s150100769

Mukherjee A, Misra S, Raghuwanshi NS (2019). A survey of unmanned aerial sensing solutions in precision agriculture. Journal of Network and Computer Applications 148:102461. https://www.doi.org/10.1016/j.jnca.2019.102461

Nishimura T, Cha-um S, Takagaki M, Ohyama K, Kirdmanee C (2011). Survival percentage, photosynthetic abilities and growth characters of two indica rice (Oryza sativa L. spp. indica) cultivars in response to iso-osmotic stress. Spanish Journal of Agricultural Research 9:262‒270.

OAE Office of Agricultural Economics (2018). Report on survey of rice growing areas in Thailand crop year 2017/2018. Center for Agriculture Information, Office of Agricultural Economics, Ministry of Agriculture and Cooperation, Bangkok, Thailand.

Poley LG, McDermid GJ (2020). A systematic review of the factors influencing the estimation of vegetation aboveground biomass using unmanned aerial systems. Remote Sensing 12:1052. https://www.doi.org/10.3390/rs12071052

Qiu B, Li W, Tang Z, Chen C, Qi W (2015). Mapping paddy rice areas based on vegetation phenology and surface moisture conditions. Ecological Indicator 56:79‒86. https://www.doi.org/10.1016/j.ecolind.2015.03.039

Qiu Z, Xiang H, Ma F, Du C (2020). Qualifications of rice growth indicators optimized at different growth stages using unmanned aerial vehicle digital imagery. Remote Sensing 12:3228. https://www.doi.org/10.3390/rs12193228

Rice Research Institute (2004). The recommendation of chemical fertilizer application based on soil analysis, DOA, Bangkok.

Schuster C, Forster M, Kleinschmit B (2012). Testing the red edge channel for improving land-use classifications based on high-resolution multi-spectral satellite data. International Journal of Remote Sensing 33(17):5583‒5599. https://www.doi.org/10.1080/01431161.2012.666812

Shiu YS, Chuang YC (2019). Yield estimation of paddy rice based on satellite imagery: comparison of global and local regression models. Remote Sensing 11:111. https://www.doi.org/10.3390/rs11020111

Sugianto S, Adinda R, Rusdi M, Basri H, Iqbal M (2020). Rice crop phenology analysis during rending season using remote sensing data: an EVI-2 ratio approach of Aceh Besar regency rice field. IOP Conference Series: Earth and Environmental Science 425:012017. https://www.doi.org/10.1088/1755-1315/425/1/012017

Sujariya S, Jongdee B, Monkham T, Jongrungklang N (2019) Adaptation of rice genotypes to diverse rainfed lowland paddy conditions. SABRAO Journal of Breeding Genetics 51:340‒355.

Tsubo M, Fukai S, Basnayake J, Tuong TP, Bouman B, Harnpichitvitaya D (2007). Effects of soil clay content on water balance and productivity in rainfed lowland rice ecosystem in Northeast Thailand. Plant Production Science 10(2):232‒241. https://www.doi.org/10.1626/pps.10.232

Tucker CJ (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment 8(2):127‒150. https://www.doi.org/10.1016/0034-4257(79)90013-0

Vanavichit A, Kamolsukyeunyong W, Siangliw M, Siangliw JL, Traprab S, Ruengphayak S, … Tragoonrung S (2018). Thai Hom Mali Rice: origin and breeding for subsistence rainfed lowland rice system. Rice 11:20. https://www.doi.org/10.1186/s12284-018-0212-7

Wang Y, Zhang K, Tang C, Cao Q, Tian Y, Zhu Y, Cao W, Liu X (2019). Estimation of rice growth parameters based on linear mixed-effect model using multispectral images from fixed-wing unmanned aerial vehicles. Remote Sensing 11:1371. https://www.doi.org/10.3390/rs11111371

Yang W, Wang S, Zhao X, Zhang J, Feng J (2015). Greenness identification based on HSV decision tree. Information Processing in Agriculture 2:149‒160. https://www.doi.org/10.1016/j.inpa.2015.07.003

Yoo SC, Cho SH, Zhang H, Paik HC, Lee CH, Li J, Yoo JH, Lee BW, Koh HJ, Seo HS, Paek NC (2007). Quantitative trait loci associated with functional stay-green SNU-SG1 in rice. Molecules and Cells 24:83‒94.

Yu F, Xu T, Cao Y, Yang G, Du W, Wang S (2016). Models for estimating the leaf NDVI of japonica rice on a canopy scale by combining canopy NDVI and multisource environmental data in Northeast China. International Journal of Agricultural and Biological Engineering 9:132‒142.

Zhang Y, Su Z, Shen W, Jia R, Luan J (2016). Remote monitoring of heading rice growing and nitrogen content based on UAV images. International Journal of Smart Home 10:103‒114. https://www.doi.org/10.14257/ijsh.2016.10.7.11

Zhao Y, Qiang C, Wang X, Chen Y, Deng J, Jiang C, … Li J (2019). New alleles for chlorophyll content and stay-green traits revealed by a genome wide association study in rice (Oryza sativa). Scientific Reports 9:1‒11. https://www.doi.org/10.1038/s41598-019-39280-5

Zheng H, Cheng T, Yao X, Deng X, Tian Y, Cao W, Zhu Y (2016). Detection of rice phenology through time series analysis of ground-based spectral index data. Field Crops Research 198:131‒139. https://www.doi.org/10.1016/j.fcr.2016.08.027

Zheng H, Cheng T, Zhou M, Li D, Yao X, Tian Y, … Zhu Y (2019). Improved estimation of rice aboveground biomass combining textural and spectral analysis of UAV imagery. Precision Agriculture 20:611‒629. https://www.doi.org/10.1007/s11119-018-9600-7

Zheng H, Ma J, Zhou M, Li D, Yao X, Cao W, … Cheng T (2020). Enhancing the nitrogen signals of rice canopies across critical growth stages through the integration of textural and spectral information from unmanned aerial vehicle (UAV) multispectral imagery. Remote Sensing 12:957. https://www.doi.org/10.3390/rs12060957

Zhou X, Zheng HB, Xu XQ, He JY, Ge XK, Yao X, … Tian YC (2017). Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS Journal of Photogrammetry and Remote Sensing 130:246-255. https://www.doi.org/10.1016/j.isprsjprs.2017.05.003


Additional Files



How to Cite

PIPATSITEE, P. ., EIUMNOH, A. ., TISARUM, R. ., TAOTA, K., KONGPUGDEE, S., SAKULLEERUNGROJ, K., & CHA-UM, S. (2020). Above-ground vegetation indices and yield attributes of rice crop using unmanned aerial vehicle combined with ground truth measurements. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 48(4), 2385–2398. https://doi.org/10.15835/nbha48412134



Research Articles
DOI: 10.15835/nbha48412134

Most read articles by the same author(s)