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A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform

Published on May 1, 2019in Plant Science3.79
· DOI :10.1016/j.plantsci.2018.10.022
Muhammad Adeel Hassan2
Estimated H-index: 2
(CAAS: Civil Aviation Authority of Singapore),
Mengjiao Yang2
Estimated H-index: 2
(Xinjiang Agricultural University)
+ 5 AuthorsZhonghu He48
Estimated H-index: 48
(CIMMYT: International Maize and Wheat Improvement Center)
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Abstract
Abstract Wheat improvement programs require rapid assessment of large numbers of individual plots across multiple environments. Vegetation indices (VIs) that are mainly associated with yield and yield-related physiological traits, and rapid evaluation of canopy normalized difference vegetation index (NDVI) can assist in-season selection. Multi-spectral imagery using unmanned aerial vehicles (UAV) can readily assess the VIs traits at various crop growth stages. Thirty-two wheat cultivars and breeding lines grown in limited irrigation and full irrigation treatments were investigated to monitor NDVI across the growth cycle using a Sequoia sensor mounted on a UAV. Significant correlations ranging from R 2  = 0.38 to 0.90 were observed between NDVI detected from UAV and Greenseeker (GS) during stem elongation (SE) to late grain gilling (LGF) across the treatments. UAV-NDVI also had high heritabilities at SE ( h 2  = 0.91), flowering (F)( h 2  = 0.95), EGF ( h 2  = 0.79) and mid grain filling (MGF) ( h 2  = 0.71) under the full irrigation treatment, and at booting (B) ( h 2  = 0.89), EGF ( h 2  = 0.75) in the limited irrigation treatment. UAV-NDVI explained significant variation in grain yield (GY) at EGF ( R 2  = 0.86), MGF ( R 2  = 0.83) and LGF ( R 2  = 0.89) stages, and results were consistent with GS-NDVI. Higher correlations between UAV-NDVI and GY were observed under full irrigation at three different grain-filling stages ( R 2  = 0.40, 0.49 and 0.45) than the limited irrigation treatment ( R 2  = 0.08, 0.12 and 0.14) and GY was calculated to be 24.4% lower under limited irrigation conditions. Pearson correlations between UAV-NDVI and GY were also low ranging from r  = 0.29 to 0.37 during grain-filling under limited irrigation but higher than GS-NDVI data. A similar pattern was observed for normalized difference red-edge (NDRE) and normalized green red difference index (NGRDI) when correlated with GY. Fresh biomass estimated at late flowering stage had significant correlations of r  = 0.30 to 0.51 with UAV-NDVI at EGF. Some genotypes Nongda 211, Nongda 5181, Zhongmai 175 and Zhongmai 12 were identified as high yielding genotypes using NDVI during grain-filling. In conclusion, a multispectral sensor mounted on a UAV is a reliable high-throughput platform for NDVI measurement to predict biomass and GY and grain-filling stage seems the best period for selection.
  • References (45)
  • Citations (4)
Cite
References45
Newest
Published on May 23, 2018in Remote Sensing4.12
Muhammad Adeel Hassan2
Estimated H-index: 2
,
Mengjiao Yang2
Estimated H-index: 2
+ 4 AuthorsZhonghu He48
Estimated H-index: 48
Detection of senescence’s dynamics in crop breeding is time consuming and needs considerable details regarding its rate of progression and intensity. Normalized difference red-edge index (NDREI) along with four other spectral vegetative indices (SVIs) derived from unmanned aerial vehicle (UAV) based spatial imagery, were evaluated for rapid and accurate prediction of senescence. For this, 32 selected winter wheat genotypes were planted under full and limited irrigation treatments. Significant va...
Published on Dec 1, 2017in Plant Methods3.17
Osval A. Montesinos-López9
Estimated H-index: 9
(CIMMYT: International Maize and Wheat Improvement Center),
Abelardo Montesinos-López8
Estimated H-index: 8
(CIMAT: Centro de Investigación en Matemáticas)
+ 6 AuthorsJuan Burgueño21
Estimated H-index: 21
(CIMMYT: International Maize and Wheat Improvement Center)
Background Modern agriculture uses hyperspectral cameras to obtain hundreds of reflectance data measured at discrete narrow bands to cover the whole visible light spectrum and part of the infrared and ultraviolet light spectra, depending on the camera. This information is used to construct vegetation indices (VI) (e.g., green normalized difference vegetation index or GNDVI, simple ratio or SRa, etc.) which are used for the prediction of primary traits (e.g., biomass). However, these indices only...
Published on Sep 8, 2017in Frontiers in Plant Science4.11
Andries Potgieter15
Estimated H-index: 15
(UQ: University of Queensland),
Barbara George-Jaeggli5
Estimated H-index: 5
(Warw.: University of Warwick)
+ 7 AuthorsGraeme L. Hammer62
Estimated H-index: 62
(CSIRO: Commonwealth Scientific and Industrial Research Organisation)
Genetic improvement in sorghum breeding programs requires the assessment of adaptation traits in small-plot breeding trials across multiple environments. Many of these phenotypic assessments are made by manual measurement or visual scoring, both of which are time consuming and expensive. This limits trial size and the potential for genetic gain. In addition, these methods are typically restricted to point estimates of particular traits such as leaf senescence or flowering and do not capture the ...
Published on Aug 1, 2017in Field Crops Research3.87
T. Duan1
Estimated H-index: 1
(CAU: China Agricultural University),
Scott C. Chapman48
Estimated H-index: 48
(UQ: University of Queensland)
+ 1 AuthorsBangyou Zheng13
Estimated H-index: 13
(CSIRO: Commonwealth Scientific and Industrial Research Organisation)
Abstract While new technologies can capture high-resolution normalized difference vegetation index (NDVI), a surrogate for biomass and leaf greenness, it is a challenge to efficiently apply this technology in a large breeding program. Here we validate a high-throughput phenotyping platform to dynamically monitor NDVI during the growing season for the contrasting wheat cultivars and managements. The images were rapidly captured (approximately 1 ha in 10 min) by an unmanned aerial vehicle (UAV) ca...
Published on Aug 1, 2017in Precision Agriculture3.36
L. G. T. Crusiol1
Estimated H-index: 1
(Empresa Brasileira de Pesquisa Agropecuária),
Josirley de Fátima Corrêa Carvalho7
Estimated H-index: 7
(Empresa Brasileira de Pesquisa Agropecuária)
+ 8 AuthorsN. Neumaier13
Estimated H-index: 13
(Empresa Brasileira de Pesquisa Agropecuária)
Although the information on the Normalized Difference Vegetation Index (NDVI) in plants under water deficit is often obtained from sensors attached to satellites, the increasing data acquisition with portable sensors has wide applicability in agricultural production because it is a fast, nondestructive method, and is less prone to interference problems. Thus, we carried out a set of experiments to investigate the influence of time, spatial plant arrangements, sampling size, height of the sensor ...
Published on Jun 30, 2017in Frontiers in Plant Science4.11
Guijun Yang16
Estimated H-index: 16
(CIT: Center for Information Technology),
Jiangang Liu2
Estimated H-index: 2
(CIT: Center for Information Technology)
+ 13 AuthorsXiaoyan Zhang1
Estimated H-index: 1
(NAU: Nanjing Agricultural University)
Phenotyping plays an important role in crop science research; the accurate and rapid acquisition of phenotypic information of plants or cells in different environments is helpful for exploring the inheritance and expression patterns of the genome to determine the association of genomic and phenotypic information to increase the crop yield. Traditional methods for acquiring crop traits, such as plant height, leaf color, leaf area index (LAI), chlorophyll content, biomass and yield, rely on manual...
Published on May 16, 2017in Frontiers in Plant Science4.11
Shouyang Liu3
Estimated H-index: 3
(INRA: Institut national de la recherche agronomique),
Fred Baret3
Estimated H-index: 3
(INRA: Institut national de la recherche agronomique)
+ 2 AuthorsMatthieu Hemmerlé3
Estimated H-index: 3
Crop density is a key agronomical trait used to manage wheat crops and estimate yield. Visual counting of plants in the field is currently the most common method used. However, it is tedious and time consuming. The main objective of this work is to develop a machine vision based method to automate the density survey of wheat at early stages. RGB images taken with a high resolution RGB camera are classified to identify the green pixels corresponding to the plants. Crop rows are extracted and the ...
Published on Apr 1, 2017in Precision Agriculture3.36
Anserd J. Foster3
Estimated H-index: 3
,
Vijaya Gopal Kakani26
Estimated H-index: 26
(OSU: Oklahoma State University–Stillwater),
Jagadeesh Mosali3
Estimated H-index: 3
The objective of this study was to compare performance of partial least square regression (PLSR) and best narrowband normalize nitrogen vegetation index (NNVI) linear regression models for predicting N concentration and best narrowband normalize different vegetation index (NDVI) for end of season biomass yield in bioenergy crop production systems. Canopy hyperspectral data was collected using an ASD FieldSpec FR spectroradiometer (350–2500 nm) at monthly intervals in 2012 and 2013. The cropping ...
Published on Mar 28, 2017in Frontiers in Plant Science4.11
Kakeru Watanabe1
Estimated H-index: 1
(UTokyo: University of Tokyo),
Wei Guo1
Estimated H-index: 1
(UTokyo: University of Tokyo)
+ 8 AuthorsNobuhiro Tsutsumi40
Estimated H-index: 40
(UTokyo: University of Tokyo)
Genomics-assisted breeding methods have been rapidly developed with novel technologies such as next-generation sequencing, genomic selection and genome-wide association study. However, phenotyping is still time consuming and is a serious bottleneck in genomics-assisted breeding. In this study, we established a high-throughput phenotyping system for sorghum plant height and its response to nitrogen availability; this system relies on the use of unmanned-aerial-vehicle (UAV) remote sensing with ei...
Published on Jan 1, 2017in Functional Plant Biology2.33
Tao Duan3
Estimated H-index: 3
(CAU: China Agricultural University),
Bangyou Zheng13
Estimated H-index: 13
(CSIRO: Commonwealth Scientific and Industrial Research Organisation)
+ 3 AuthorsScott C. Chapman48
Estimated H-index: 48
(UQ: University of Queensland)
Ground cover is an important physiological trait affecting crop radiation capture, water-use efficiency and grain yield. It is challenging to efficiently measure ground cover with reasonable precision for large numbers of plots, especially in tall crop species. Here we combined two image-based methods to estimate plot-level ground cover for three species, from either an ortho-mosaic or undistorted (i.e. corrected for lens and camera effects) images captured by cameras using a low-altitude unmann...
Cited By4
Newest
Published on Apr 15, 2019in Plant Methods3.17
Muhammad Adeel Hassan2
Estimated H-index: 2
(CAAS: Civil Aviation Authority of Singapore),
Mengjiao Yang2
Estimated H-index: 2
(Xinjiang Agricultural University)
+ 5 AuthorsZhonghu He48
Estimated H-index: 48
(CIMMYT: International Maize and Wheat Improvement Center)
Background Plant height is an important selection target since it is associated with yield potential, stability and particularly with lodging resistance in various environments. Rapid and cost-effective estimation of plant height from airborne devices using a digital surface model can be integrated with academic research and practical wheat breeding programs. A bi-parental wheat population consisting of 198 doubled haploid lines was used for time-series assessments of progress in reaching final ...
Published on Jul 11, 2019in Remote Sensing4.12
Fenner Howard Holman1
Estimated H-index: 1
,
Andrew B. Riche22
Estimated H-index: 22
+ 2 AuthorsMalcolm J. Hawkesford50
Estimated H-index: 50
Vegetation indices, such as the Normalised Difference Vegetation Index (NDVI), are common metrics used for measuring traits of interest in crop phenotyping. However, traditional measurements of these indices are often influenced by multiple confounding factors such as canopy cover and reflectance of underlying soil, visible in canopy gaps. Digital cameras mounted to Unmanned Aerial Vehicles offer the spatial resolution to investigate these confounding factors, however incomplete methods for radi...
Published on May 1, 2019in Plant Science3.79
Matthew P. Reynolds61
Estimated H-index: 61
,
Ulrich Schurr38
Estimated H-index: 38
Published on Apr 30, 2019in Sensors3.03
Juan José Quirós Vargas , Chongyuan Zhang1
Estimated H-index: 1
+ 2 AuthorsSindhuja Sankaran18
Estimated H-index: 18
Field pea cultivars are constantly improved through breeding programs to enhance biotic and abiotic stress tolerance and increase seed yield potential. In pea breeding, the Above Ground Biomass (AGBM) is assessed due to its influence on seed yield, canopy closure, and weed suppression. It is also the primary yield component for peas used as a cover crop and/or grazing. Measuring AGBM is destructive and labor-intensive process. Sensor-based phenotyping of such traits can greatly enhance crop bree...
Published on Jan 1, 2019in Agronomy Journal1.80
Dan Olson (NDSU: North Dakota State University), Amitava Chatterjee15
Estimated H-index: 15
(NDSU: North Dakota State University),
David W. Franzen14
Estimated H-index: 14
(NDSU: North Dakota State University)
Published on Jan 1, 2018in bioRxiv
Abbas Haghshenas1
Estimated H-index: 1
(Shiraz University),
Y. Emam14
Estimated H-index: 14
(Shiraz University)
Light extinction is the most fundamental aspect of green canopies. The exponential form of light gradient is extensively evaluated or utilized by conventional approaches mainly in contribution to the vital concept of leaf area index (LAI), which reasonably characterizes canopies based on their theoretical capability for light attenuation i.e. having greater leaf surfaces. We analyzed the image archive of heterogeneous wheat canopies (cultivar mixtures), captured from experimental plots of a two-...