- Open Access
Dynamic monitoring of biomass of rice under different nitrogen treatments using a lightweight UAV with dual image-frame snapshot cameras
© The Author(s) 2019
- Received: 21 October 2018
- Accepted: 21 March 2019
- Published: 27 March 2019
Unmanned aerial vehicle (UAV)-based remote sensing provides a flexible, low-cost, and efficient approach to monitor crop growth status at fine spatial and temporal resolutions, and has a high potential to accelerate breeding process and improve precision field management.
In this study, we discussed the use of lightweight UAV with dual image-frame snapshot cameras to estimate aboveground biomass (AGB) and panicle biomass (PB) of rice at different growth stages with different nitrogen (N) treatments. The spatial–temporal variations in the typical vegetation indices (VIs) and AGB were first investigated, and the accuracy of crop surface model (CSM) extracted from the Red Green Blue (RGB) images at two different stages were also evaluated. Random forest (RF) model for AGB estimation as well as the PB was then developed. Furthermore, variable importance and sensitivity analysis of UAV variables were performed to study the potential of improving model robustness and prediction accuracies.
It was found that the canopy height extracted from the CSM (Hcsm) exhibited a high correlation with the ground-measured canopy height, while it was unsuitable to be independently used for biomass assessment of rice during the entire growth stages. We also observed that several VIs were highly correlated with AGB, and the modified normalized difference spectral index extracted from the multispectral image achieved the highest correlation. RF model with fusing RGB and multispectral image data substantially improved the prediction results of AGB and PB with the prediction of root mean square error (RMSEP) reduced by 8.33–16.00%. The best prediction results for AGB and PB were achieved with the coefficient of determination (r2), the RMSEP and relative RMSE (RRMSE) of 0.90, 0.21 kg/m2 and 14.05%, and 0.68, 0.10 kg/m2 and 12.11%, respectively. In addition, the result confirmed that the sensitivity analysis could simplify the prediction model without reducing the prediction accuracy.
These findings demonstrate the feasibility of applying lightweight UAV with dual image-frame snapshot cameras for rice biomass estimation, and its potential for high throughput analysis of plant growth-related traits in precision agriculture as well as the advanced breeding program.
- Unmanned aerial vehicle (UAV)
- Image-frame snapshot multispectral camera
- Data fusion
- Aboveground biomass
- Crop surface model
- Random forest regression
Rice (Oryza sativa) is one of the most important grain crops worldwide, and it serves as a food staple for more than half of the world’s population . Crop biomass defined as the averaged dry weight per unit area is an important agronomic trait linked to plant genetics, growth rate, and productivity. It is also a key ecological indicator of light use efficiency and carbon stocks in agro-ecosystems . Moreover, biomass can be applied to quantify the grain yield with the harvest index . It is also frequently used to assess crop health status and nutrient supply to support agricultural management practices . Hence, it is necessary to explore advanced and efficient technologies for dynamically monitoring crop biomass during the entire growth stages.
Traditional measurement of biomass mainly relies on the field survey with destructive sampling, which is time-consuming and labor-intensive. Many studies associated with advanced remote sensing methods utilized hand-held instruments (i.e., ASD FieldSpec Pro spectrometer) [5, 6], ground platforms (i.e., manned ground vehicle with laser scanner) [7, 8] and satellite imaging (i.e., Landsat, MODIS, SPOT5, and WorldView-2) [9, 10] for biomass estimation of different crops. However, limited spatial and temporal resolutions, and high cost of obtaining satellite image data, and image quality affected by atmospheric conditions pose great challenges to achieve an accurate estimation of biomass during the whole growth period. Although hand-held devices and ground platforms provide a better spatial resolution and can be used to conduct a field survey as frequently as needed throughout the crop growing season, they are usually confined to a small area, which is not efficient when dealing with a high-throughput analysis of biomass, and crop damage in the late growth stage could also be a concern in practical applications.
The rapid development of low-cost and relative easy to operate unmanned aerial vehicles (UAVs) provides a new means of remote sensing. They are more flexible than satellite-based remote sensing, and can overcome the survey area limitation of the ground-based platform. A UAV could fly at a low altitude and acquire an image at a high spatial resolution based on a pre-defined flight route. Different types of spectroscopic and image sensors for UAV have been developed, such as Red Green Blue (RGB) sensors, multispectral/hyperspectral imaging sensors, light detection and ranging (LiDAR) and infrared thermal imaging sensors, further extending UAV-based remote sensing to various applications. Previous studies have shown the potential of high resolution UAV-based RGB images for measuring plant height [2, 11, 12], biomass [13–15], vegetation fraction , plant density , and grain yield . Due to the availability of the near-infrared (NIR) wavelengths in multispectral/hyperspectral images, spectral images have also become an alternative for UAV sensors in evaluating the physiological- and biochemical-related parameters of plants, such as leaf area index (LAI) [19, 20], vegetation fraction , flower fraction , nitrogen (N) status [22–24], net photosynthesis  and biomass . Most of the reported studies applied a single sensor to estimate a specific trait of the crop. In recent years, with the requirement of collecting comprehensive information about plant growth status, more studies were focused on estimating plant growth-related traits by data fusion from different sensors [8, 27, 28]. Bendig et al.  utilized the canopy height extracted from the crop surface model (Hcsm) to estimate fresh aboveground biomass (FAGB) and dry aboveground biomass (DAGB) of barley with the coefficient of determination (r2) values of 0.72 and 0.68, respectively, and the result of the DAGB estimation was further improved with r2 of 0.80–0.82 by adding NIR vegetation indices (VIs) obtained from the ground-based spectral measurement. Wang et al.  proposed fusion of airborne LiDAR and hyperspectral data derived from two platforms to estimate DAGB of maize with the r2 and root mean square error (RMSE) of 0.88 and 0.32 kg/m2, respectively, and concluded that sensor fusion provided a better estimate of DAGB compared with the result obtained from LiDAR or hyperspectral data alone. More recently, Maimaitijiang et al.  used multi-sensor data collected from RGB, multispectral and thermal cameras that were mounted on different UAVs to estimate FAGB and DAGB of soybean, and reported that multispectral and thermal data fusion provided the best result for biomass estimation. The most studies as reviewed above mainly focused on estimating biomass based on the sensor data collected from different remote sensing platforms, which could add more uncertainty of the sensor data due to the variable illumination conditions and the systematic variability of the platforms during data acquisition. Furthermore, canopy coverages and structures of the crop vary at different growth stages, which would significantly affect the spectral characteristics and 3D point clouds extracted from multispectral and RGB images, respectively.
In this study, we developed a compact UAV with low-cost, lightweight dual image-frame snapshot cameras for dynamic monitoring of rice biomass at different growth stages. The specific goals were to: (1) investigate the spatial and temporal variations in UAV variables and aboveground biomass (AGB) under different N treatments and growth stages; (2) develop random forest (RF) model for AGB and panicle biomass (PB) estimations by using UAV aerial images and test variable importance for AGB and PB estimations; and (3) perform statistical analysis to evaluate the accuracy and robustness of the AGB and PB prediction models developed from the fusion of RGB and multispectral images.
The experimental site was located at the Grain-production Functional Area of Anhua Town, Zhuji City, Zhejiang Province, China (29°31′5.35″N, 120°6′6.12″E). It has an average altitude of 16 m above sea level, and the average annual temperature is 16.3 °C. Rice (Yongyou 1540) was cultivated in an experimental site of 25 subplots with 18 × 10 m2 of each, and they were treated with five levels of N fertilizers (0, 72, 120, 240 and 360 kg N/ha) with five repetitions. N fertilizers were applied in the form of urea with the rates of 40, 30 and 30% at the stages of preplanting, tillering, and booting, respectively. In addition, phosphate (P) fertilizer (120 kg/ha) and potash (K) (240 kg/ha) were applied in the form of Ca(H2PO4)2∙H2O and KCl, respectively, at the preplanting stage. Rice was transplanted in early June and harvested in middle to late October in 2017. A protected planting area, with a width of 1 m, was provided around the entire experimental site.
UAV-based image data collection
Ground measurements of canopy height and aboveground biomass
After UAV campaigns, field measurements were conducted within 1 day. The ground truth data of the plant canopy height (Hcanopy), AGB and PB was collected from five 0.2 × 0.3 m2 zones in each plot. The sampling points were randomly selected, and the sampling positions were also recorded. The calculated height was the average of the determined height area. The rice canopy height between the ground and the highest point of the plant was measured in each subplot by using a ruler in the field at initial jointing and initial heading stages. Since the height of the plant canopy remained unchangeable when rice plants entered into the heading stage, no measurement of Hcanopy was performed after the heading stage. Then, five samples in the five quadrats were manually harvested from each subplot, and 500 sample points were obtained during the entire experiment to measure the ground truth of the biomass with four growth stages. These plants were sealed in plastic bags and taken to the laboratory within 6 h after harvesting. After transportation to the laboratory, plant samples were cleaned to remove the soil and water, and the roots of the plants were cut. Theses samples were then dried for 72 h, until a consistent weight was obtained. Finally, AGB with the weight per unit area (kg/m2) was calculated . Meanwhile, PB was also measured at initial filling and late filling stages.
Crop surface models extraction
Radiometric and spectral correction
Vegetation indices calculation
Vegetation indices (VIs) derived from red green blue (RGB) and multispectral images
Visible-band difference vegetation index
VDVI = (2 * G − R − B)/(2 * G + R + B)
Normalized green–red difference index
NGRDI = (G − R)/(G + R)
Visible atmospherically resistant index
VARI = (G − R)/(G + R – B)
Green–red ratio index
GRRI = G/R
VEG = G/(Ra * B(1 − a)) a = 0.667
Modified green blue vegetation index
MGRVI = (G2 − R2)/(G2 + R2)
Normalized difference spectral index
NDSI = (Rλ1 − Rλ2)/(Rλ1 + Rλ2)
Simple ratio index
SR = Rλ1/Rλ2
Modified normalized difference spectral index
MNDSI = (Rλ1 − Rλ2)/(Rλ1 − Rλ3)
Statistical analysis and model development
The spatial heterogeneity and temporal variation in the typical VIs including the NDSI(796, 679) and VDVI, and AGB were first investigated. The NDSI(796, 679) was equivalent to the normalized difference vegetation index (NDVI), which was closely related to the canopy greenness, N content, aboveground N uptake, and N efficiency of crops . VDVI was also a good indicator of crop growth, canopy greenness and yield . Therefore, NDSI(796, 679) and VDVI values can well reflect seasonal changes in phenology of rice. Based on RGB images and multispectral reflectance images, the VDVI and NDSI(796, 679) maps were produced using the equations as shown in Table 1. The average values of VDVI and NDSI(796, 679) for each plot were calculated to represent the average growth condition. Then, the inter-correlations among all of the UAV variables, including the Hcsm, RGB-VIs, and MS-VIs, were evaluated using Pearson correlation coefficient (r). Furthermore, a regression analysis was performed to investigate the feasibility of VIs and Hcsm to estimate AGB and PB.
Spatial–temporal variations in NDSI(796, 679), VDVI and AGB
Canopy height derived from crop surface model
Estimation of biomass during rice growth stages
Correlations for UAV variables and AGB
Development of AGB estimation model
Estimation of PB at the mature phase
Comparison of RGB and multispectral cameras for biomass estimation
Estimated aboveground biomass (AGB) and panicle biomass (PB) in rice by random forest (RF) method
VDVI, NGRDI, VARI, GRRI, VEG, MGRVI, Hcsm
NDSI, SR, MNDSI
RGB + multispectral
VDVI, NGRDI, VARI, GRRI, VEG, MGRVI, NDSI, SR, MNDSI, Hcsm
In this study, we discussed a lightweight UAV equipped with dual image-frame snapshot cameras and the performance of estimating rice biomass (AGB and PB) by RGB and multispectral images under a field environment. The results have demonstrated the potential of fusing RGB and multispectral image data for biomass estimations.
Both RGB and multispectral cameras could provide spectral information in the visible spectral region, which was closely related to the vegetation greenness [16, 36]. While considering biomass estimation, RGB camera and multispectral camera possessed own advantages and disadvantages as presented in Figs. 7, 8, 9 and 10. Figure 9 revealed that the Hcsm and RGB-VIs of MVARI and VDVI possessed the higher importance for the assessment of AGB than MS-VIs. This may be due to that RGB images with a higher spatial resolution contained canopy structural information, resulting in obtaining relatively clear phenotypes of crops such as vegetation coverage and plant height, and surpassed the performance of the multispectral sensor in the spatial domain [2, 14, 16]. Moreover, RGB images can provide rich texture information, and the SfM technique with an RGB camera is able to generate denser point cloud data, and is thus suitable for restoring the intricate surface texture of plant structure . Compared to RGB sensor, the multispectral sensor with a wider wavelength range could provide the NIR spectral information that reflects physiological characteristics of crops [30, 54], especially for estimating the panicle biomass as shown in Fig. 10a. However, the saturation issue associated with using the multispectral sensor in a dense vegetation canopy could be a limitation for the biomass estimation . Hence, each sensor or data set could be both limited in accuracy and incomplete. Combining data from RGB and multispectral cameras provided a holistic view of the plant growth status, and it was also possible to increase the signal to noise ratio for the final estimation. Our results indicated that fusing RGB with multispectral image data did improve the prediction results of biomass as shown in Table 2, since both crop canopy structural features and diverse spectral characteristics with NIR wavelengths related to the crop biomass were introduced.
Agronomically, there are two growth phases of rice: vegetative and reproductive . The vegetative phase refers to the period from germination to the initiation of panicle with four stages, including emergence, seedling development, tillering and internode elongation . The first two growth stages describe the process from the emergence of the radicle to the onset of tillering, which were generally not considered in the field experiment due to the limited information of crop growth that current sensors can obtain. At tillering stage, rice plants were too small to present significant growth difference among different N applications, which was unsuitable for the prediction of biomass. In addition, matched points of the images extracted from the top canopy mixed with the lower parts of crops or soil background due to the sparse structure could also affect the plant height extraction from CSMs [11, 56]. When the tillering stage ends, the rice plant entered into the jointing stage, which has basically formed a continuous canopy that could contribute to extract height information from the CSM precisely. When rice plants entered into the initial heading stage, most of the plant nutrients were used to develop panicles, and there would be a less change in the plant height while the biomass was still accumulated. This indicated that the relationship between height and biomass varied with different growth stages, and therefore, it would be difficult to determine the biomass by only using the Hcsm when rice plants entered into mature stages, which was similar to the results shown in Figs. 8b and 10a.
Estimations of aboveground biomass (AGB) and panicle biomass (PB)
This research demonstrated that a lightweight UAV with dual image-frame snapshot cameras has the potential for estimating biomass of rice during the entire growth stages. The spatial and temporal variations were observed in typical VIs (e.g., VDVI and NDSI(796, 679)), as well as AGB under different N treatments and growth stages. The correlation analysis between Hcsm and Hcanopy was conducted to verify the accuracy of the CSMs. We also examined the capacity of various UAV variables derived from UAV-based RGB and multispectral images to estimate AGB and PB. It was found that the Hcsm extracted from RGB images exhibited a high correlation with the ground-measured Hcanopy, while it was unsuitable to be independently used for biomass assessment of rice during the entire growth stages. MS-VIs showed higher correlations with AGB and PB than RGB-VIs. Compared with individual UAV variables, the performance of RF models developed by the fusion of RGB and multispectral image data was substantially improved for estimating AGB and PB. Moreover, RF models can be further simplified by sensitivity analysis while without reducing the prediction accuracy.
For the future work, it would be useful to improve the temporal resolution for the image acquisition of the crop in order to develop a continue plant growth model. Sophisticated data fusion algorithms and advanced machine learning methods would be helpful to improve the robustness and accuracy of prediction models for crop growth-related trait estimations. The UAV-based dual-sensor remote sensing platform will be further used to collect more rice growth-related traits in different cultivars and regions to develop a remote sensing database for rice.
All authors made significant contributions to this manuscript. HC, LW, JS and YH designed the experiment. HC, LW, JZ, YL, XL, YZ, HW, WW, WY and CX performed field data collection. HC, LW, JZ, YL, XL, YZ, and HW contributed to the data analysis. HC, LW, JZ and YH wrote the manuscript, and YH, YB and LF provided suggestions on the experiment design and discussion sections. All authors read and approved the final manuscript.
We would like to thank Zhihong Ma, Haixia Xu, Jinlin Jiang, Jieni Yao and Qishuai Zheng for providing the ground measurement and sampling of the field experiment.
The authors declare that they have no competing interests.
Availability of data and materials
The remotely sensed and field sampling data used in this study is available from the corresponding author on reasonable request.
Consent for publication
All authors agreed to publish this manuscript.
Ethics approval and consent to participate
All authors read and approved the manuscript.
This work was supported by the Key Research and Development Program from the Science Technology Department of Zhejiang Province (2015C02007), and National Key R & D Program supported by Ministry of Science and Technology of the P.R. China (2016YFD0200600, 2016YFD0200603).
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Cantrell RP, Reeves TG. The rice genome—the cereal of the world’s poor takes center stage. Science. 2002;296(5565):53. https://doi.org/10.1126/science.1070721.View ArticlePubMedGoogle Scholar
- Li W, Niu Z, Chen H, Li D, Wu M, Zhao W. Remote estimation of canopy height and aboveground biomass of maize using high-resolution stereo images from a low-cost unmanned aerial vehicle system. Ecol Indic. 2016;67:637–48. https://doi.org/10.1016/j.ecolind.2016.03.036.View ArticleGoogle Scholar
- Zhang X, Chen S, Sun H, Pei D, Wang Y. Dry matter, harvest index, grain yield and water use efficiency as affected by water supply in winter wheat. Irrig Sci. 2008;27(1):1–10. https://doi.org/10.1007/s00271-008-0131-2.View ArticleGoogle Scholar
- Adamchuk VI, Ferguson RB. Soil heterogeneity and crop growth. In: Oerke EC, Gerhards R, Menz G, Sikora RA, editors. Precision crop protection—the challenge and use of heterogeneity. Dordrecht: Springer; 2010. p. 3–16. https://doi.org/10.1007/978-90-481-9277-9_1.View ArticleGoogle Scholar
- Cheng T, Song R, Li D, Zhou K, Zheng H, Yao X, Tian Y, Cao W, Zhu Y. Spectroscopic estimation of biomass in canopy components of paddy rice using dry matter and chlorophyll indices. Remote Sens. 2017;9(4):319. https://doi.org/10.3390/rs9040319.View ArticleGoogle Scholar
- Gnyp ML, Miao Y, Yuan F, Ustin SL, Yu K, Yao Y, Huang S, Bareth G. Hyperspectral canopy sensing of paddy rice aboveground biomass at different growth stages. Field Crop Res. 2014;155(155):42–55. https://doi.org/10.1016/j.fcr.2013.09.023.View ArticleGoogle Scholar
- Jimenezberni JA, Deery DM, Rozaslarraondo P, Condon AG, Rebetzke GJ, James RA, Bovill WD, Furbank RT, Sirault XRR. High throughput determination of plant height, ground cover, and above-ground biomass in wheat with LIDAR. Front Plant Sci. 2018. https://doi.org/10.3389/fpls.2018.00237.View ArticleGoogle Scholar
- Tilly N, Aasen H, Bareth G. Fusion of plant height and vegetation indices for the estimation of barley biomass. Remote Sens. 2015;7(16):11449–80. https://doi.org/10.3390/rs70911449.View ArticleGoogle Scholar
- Dong T, Liu J, Qian B, Jing Q, Croft H, Chen J, Wang J, Huffman T, Shang J, Chen P. Deriving maximum light use efficiency from crop growth model and satellite data to improve crop biomass estimation. IEEE J Sel Top Appl Earth Obs. 2016;10(1):104–17. https://doi.org/10.1109/JSTARS.2016.2605303.View ArticleGoogle Scholar
- Han J, Wei C, Chen Y, Liu W, Song P, Zhang D, Wang A, Song X, Wang X, Huang J. Mapping above-ground biomass of winter oilseed rape using high spatial resolution satellite data at parcel scale under waterlogging conditions. Remote Sens. 2017;9(3):238. https://doi.org/10.3390/rs9030238.View ArticleGoogle Scholar
- Watanabe K, Guo W, Arai K, Takanashi H, Kajiyakanegae H, Kobayashi M, Yano K, Tokunaga T, Fujiwara T, Tsutsumi N. High-throughput phenotyping of sorghum plant height using an unmanned aerial vehicle and its application to genomic prediction modeling. Front Plant Sci. 2017. https://doi.org/10.3389/fpls.2017.00421.View ArticlePubMedPubMed CentralGoogle Scholar
- Holman F, Riche A, Michalski A, Castle M, Wooster M, Hawkesford M. High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sens. 2016;8(12):1031. https://doi.org/10.3390/rs8121031.View ArticleGoogle Scholar
- Bendig J, Bolten A, Bennertz S, Broscheit J, Eichfuss S, Bareth G. Estimating biomass of barley using crop surface models (csms) derived from UAV-based RGB imaging. Remote Sens. 2014;6(11):10395–412. https://doi.org/10.3390/rs61110395.View ArticleGoogle Scholar
- Bendig J, Yu K, Aasen H, Bolten A, Bennertz S, Broscheit J, Gnyp ML, Bareth G. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int J Appl Earth Obs. 2015;39:79–87. https://doi.org/10.1016/j.jag.2015.02.012.View ArticleGoogle Scholar
- Willkomm M, Bolten A, Bareth G. Non-destructive monitoring of rice by hyperspectral in-field spectrometry and uav-based remote sensing: case study of field grown rice in north rhine-westphalia, Germany. XXIII ISPRS Congr Comm I. 2016;41(B1):1071–7. https://doi.org/10.5194/isprsarchives-XLI-B1-1071-2016.View ArticleGoogle Scholar
- Torres-Sanchez J, Pena JM, de Castro AI, Lopez-Granados F. Multi-temporal mapping of the vegetation fraction in early-season wheat fields using images from UAV. Comput Electron Agric. 2014;103:104–13. https://doi.org/10.1016/j.compag.2014.02.009.View ArticleGoogle Scholar
- Jin X, Liu S, Baret F, Hemerle M, Comar A. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sens Environ. 2017;198:105–14. https://doi.org/10.1016/j.rse.2017.06.007.View ArticleGoogle Scholar
- Du M, Noguchi N. Monitoring of wheat growth status and mapping of wheat yield’s within-field spatial variations using color images acquired from UAV-camera system. Remote Sens. 2017;9(3):14. https://doi.org/10.3390/rs9030289.View ArticleGoogle Scholar
- Duan S, Li Z, Wu H, Tang B, Ma L, Zhao E, Li C. Inversion of the PROSAIL model to estimate leaf area index of maize, potato, and sunflower fields from unmanned aerial vehicle hyperspectral data. Int J Appl Earth Obs. 2014;26:12–20. https://doi.org/10.1016/j.jag.2013.05.007.View ArticleGoogle Scholar
- Potgieter AB, George-Jaeggli B, Chapman SC, Laws K, Cadavid LAS, Wixted J, Watson J, Eldridge M, Jordan DR, Hammer GL. Multi-spectral imaging from an unmanned aerial vehicle enables the assessment of seasonal leaf area dynamics of sorghum breeding lines. Front Plant Sci. 2017. https://doi.org/10.3389/fpls.2017.01532.View ArticlePubMedPubMed CentralGoogle Scholar
- Fang S, Tang W, Peng Y, Gong Y, Dai C, Chai R, Liu K. Remote estimation of vegetation fraction and flower fraction in oilseed rape with unmanned aerial vehicle data. Remote sens. 2016;8(5):416. https://doi.org/10.3390/rs8050416.View ArticleGoogle Scholar
- Inoue Y, Sakaiya E, Zhu Y, Takahashi W. Diagnostic mapping of canopy nitrogen content in rice based on hyperspectral measurements. Remote Sens Environ. 2012;126:210–21. https://doi.org/10.1016/j.rse.2012.08.026.View ArticleGoogle Scholar
- Maresma Á, Ariza M, Martínez E, Lloveras J, Martínez-Casasnovas JA. Analysis of vegetation indices to determine nitrogen application and yield prediction in maize (Zea mays L.) from a standard UAV service. Remote Sens. 2016;8(12):973. https://doi.org/10.3390/rs8120973.View ArticleGoogle Scholar
- Caturegli L, Corniglia M, Gaetani M, Grossi N, Magni S, Migliazzi M, Angelini L, Mazzoncini M, Silvestri N, Fontanelli M, Raffaelli M, Peruzzi A, Volterrani M. Unmanned aerial vehicle to estimate nitrogen status of turfgrasses. PLoS ONE. 2016;11(6):13. https://doi.org/10.1371/journal.pone.0158268.eCollection2016.View ArticleGoogle Scholar
- Zarco-Tejada PJ, Catalina A, González MR, Martín P. Relationships between net photosynthesis and steady-state chlorophyll fluorescence retrieved from airborne hyperspectral imagery. Remote Sens Environ. 2013;136(136):247–58. https://doi.org/10.1016/j.rse.2013.05.011.View ArticleGoogle Scholar
- Honkavaara E, Saari H, Kaivosoja J, Pölönen I, Hakala T, Litkey P, Mäkynen J, Pesonen L. Processing and assessment of spectrometric, stereoscopic imagery collected using a lightweight UAV spectral camera for precision agriculture. Remote Sens. 2013;5(10):5006–39. https://doi.org/10.3390/rs5105006.View ArticleGoogle Scholar
- Elarab M, Ticlavilca AM, Torres-Rua AF, Maslova I, Mckee M. Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture. Int J Appl Earth Obs. 2015;43:32–42. https://doi.org/10.1016/j.jag.2015.03.017.View ArticleGoogle Scholar
- Wan L, Li Y, Cen H, Zhu J, Yin W, Wu W, Zhu H, Sun D, Zhou W, He Y. Combining UAV-based vegetation indices and image classification to estimate flower number in oilseed rape. Remote Sens. 2018;10(9):1484. https://doi.org/10.3390/rs10091484.View ArticleGoogle Scholar
- Wang C, Nie S, Xi X, Luo S, Sun X. Estimating the biomass of maize with hyperspectral and LiDAR data. Remote Sens. 2016;11(9):1–12. https://doi.org/10.3390/rs9010011.View ArticleGoogle Scholar
- Maimaitijiang M, Ghulam A, Sidike P, Hartling S, Maimaitiyiming M, Peterson K, Shavers E, Fishman J, Peterson J, Kadam S. Unmanned aerial system (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine. ISPRS J Photogramm. 2017;134:43–58. https://doi.org/10.1016/j.isprsjprs.2017.10.011.View ArticleGoogle Scholar
- Sona G, Pinto L, Pagliari D, Passoni D, Gini R. Experimental analysis of different software packages for orientation and digital surface modelling from UAV images. Earth Sci Inform. 2014;7(2):97–107. https://doi.org/10.1007/s12145-013-0142-2.View ArticleGoogle Scholar
- Bendig J, Bolten A, Bareth G. UAV-based imaging for multi-temporal, very high resolution crop surface models to monitor crop growth variability. Photogramm Fernerkund. 2013;6:551–62. https://doi.org/10.1127/1432-8364/2013/0200.View ArticleGoogle Scholar
- Tilly AN, Hoffmeister D, Cao Q, Huang S, Lenz-Wiedemann V, Miao Y, Bareth G. Multitemporal crop surface models: accurate plant height measurement and biomass estimation with terrestrial laser scanning in paddy rice. J Appl Remote Sens. 2014;8(1):083671. https://doi.org/10.1117/1.JRS.8.083671.View ArticleGoogle Scholar
- Tomasi C, Kanade T. Shape and motion from image streams under orthography: a factorization method. Int J Comput Vis. 1992;9(2):137–54. https://doi.org/10.1007/BF00129684.View ArticleGoogle Scholar
- Yu N, Li L, Schmitz N, Tiaz LF, Greenberg JA, Diers BW. Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform. Remote Sens Environ. 2016;187:91–101. https://doi.org/10.1016/j.rse.2016.10.005.View ArticleGoogle Scholar
- Rasmussen J, Ntakos G, Nielsen J, Svensgaard J, Poulsen RN, Christensen S. Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? Eur J Agron. 2016;74:75–92. https://doi.org/10.1016/j.eja.2015.11.026.View ArticleGoogle Scholar
- Wang X, Wang M, Wang S, Wu Y. Extraction of vegetation information from visible unmanned aerial vehicle images. Trans Chin Soc Agric Eng. 2015;31(5):152–9. https://doi.org/10.3969/j.issn.1002-6819.2015.05.022.View ArticleGoogle Scholar
- Gitelson AA, Vina A, Arkebauer TJ, Rundquist DC, Keydan G, Leavitt B. Remote estimation of leaf area index and green leaf biomass in maize canopies. Geophys Res Lett. 2003;30(30):335–43. https://doi.org/10.1029/2002gl016450.View ArticleGoogle Scholar
- Zhou X, Zheng HB, Xu XQ, He JY, Ge XK, Yao X, Cheng T, Zhu Y, Cao WX, Tian YC. Predicting grain yield in rice using multi-temporal vegetation indices from UAV-based multispectral and digital imagery. ISPRS J Photogramm. 2017;130:246–55. https://doi.org/10.1016/j.isprsjprs.2017.05.003.View ArticleGoogle Scholar
- Inoue Y, Guerif M, Baret F, Skidmore A, Gitelson A, Schlerf M, Darvishzadeh R, Olioso A. Simple and robust methods for remote sensing of canopy chlorophyll content: a comparative analysis of hyperspectral data for different types of vegetation. Plant, Cell Environ. 2016;39(12):2609–23. https://doi.org/10.1111/pce.12815.View ArticleGoogle Scholar
- Fu Y, Yang G, Wang J, Song X, Feng H. Winter wheat biomass estimation based on spectral indices, band depth analysis and partial least squares regression using hyperspectral measurements. Comput Electron Agric. 2014;100:51–9. https://doi.org/10.1016/j.compag.2013.10.010.View ArticleGoogle Scholar
- Yao X, Wang N, Liu Y, Cheng T, Tian Y, Chen Q, Zhu Y. Estimation of wheat LAI at middle to high levels using unmanned aerial vehicle narrowband multispectral imagery. Remote Sens. 2017;9(12):1304. https://doi.org/10.3390/rs9121304.View ArticleGoogle Scholar
- Gitelson AA, Kaufman YJ, Stark R, Rundquist D. Novel algorithms for remote estimation of vegetation fraction. Remote Sens Environ. 2002;80(1):76–87. https://doi.org/10.1016/s0034-4257(01)00289-9.View ArticleGoogle Scholar
- Gamon JA, Surfus JS. Assessing leaf pigment content and activity with a reflectometer. New Phytol. 1999;143(1):105–17. https://doi.org/10.1046/j.1469-8137.1999.00424.x.View ArticleGoogle Scholar
- Hague T, Tillett ND, Wheeler H. Automated crop and weed monitoring in widely spaced cereals. Precis Agric. 2006;7(1):21–32. https://doi.org/10.1007/s11119-005-6787-1.View ArticleGoogle Scholar
- Rouse JW, Haas RW, Schell JA, Deering DW, Harlan JC. Monitoring the vernal advancement and retrogradation (green wave effect) of natural vegetation. NASA Goddard Space Flight Center: Houston, TX, USA. 1974. https://ntrs.nasa.gov/search.jsp?R=19730017588. Accessed on 1 April 1973.
- Jordan CF. Derivation of leaf-area index from quality of light on the forest floor. Ecology. 1969;50(4):663–6. https://doi.org/10.2307/1936256.View ArticleGoogle Scholar
- Tian YC, Yao X, Yang J, Cao WX, Hannaway DB, Zhu Y. Assessing newly developed and published vegetation indices for estimating rice leaf nitrogen concentration with ground- and space-based hyperspectral reflectance. Field Crop Res. 2011;120(2):299–310. https://doi.org/10.1016/j.fcr.2010.11.002.View ArticleGoogle Scholar
- Duan T, Chapman SC, Guo Y, Zheng B. Dynamic monitoring of NDVI in wheat agronomy and breeding trials using an unmanned aerial vehicle. Field Crop Res. 2017;210:71–80. https://doi.org/10.1016/j.fcr.2017.05.025.View ArticleGoogle Scholar
- Liaw A, Wiener M. Classification and regression by randomForest. R News. 2002;23:23. https://doi.org/10.1023/A:101093340.View ArticleGoogle Scholar
- Sun J, Yang J, Shi S, Chen B, Du L, Gong W, Song S. Estimating rice leaf nitrogen concentration: influence of regression algorithms based on passive and active leaf reflectance. Remote Sens. 2017;9(9):951. https://doi.org/10.3390/rs9090951.View ArticleGoogle Scholar
- Cen H, Lu R, Dolan K. Optimization of inverse algorithm for estimating the optical properties of biological materials using spatially-resolved diffuse reflectance. Inverse Probl Sci Environ. 2010;18(6):853–72. https://doi.org/10.1080/17415977.2010.492516.View ArticleGoogle Scholar
- Jing R, Gong Z, Zhao W, Pu R, Deng L. Above-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform—a case study in Wild Duck Lake Wetland, Beijing, China. ISPRS J Photogramm. 2017;134:122–34. https://doi.org/10.1016/j.isprsjprs.2017.11.002.View ArticleGoogle Scholar
- Lebourgeois V, Bégué A, Labbé S, Houlès M, Martiné JF. A light-weight multi-spectral aerial imaging system for nitrogen crop monitoring. Precis Agric. 2012;13(5):525–41. https://doi.org/10.1007/s11119-012-9262-9.View ArticleGoogle Scholar
- Moldenhauer K, Slaton N. Rice growth and development. In: Slaton NA, editor. Rice production handbook misc publ 192. Coop Ext Service, University of Arkansas; Little Rock, USA: 2001, p. 7–14.Google Scholar
- Grenzdörffer GJ. Crop height determination with UAS point clouds. Int Arch Photogramm Remote Sens Spat Inf Sci. 2014;XL-1:135–40. https://doi.org/10.5194/isprsarchives-xl-1-135-2014.View ArticleGoogle Scholar