PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits

Aasen, Helge; Kirchgeßner, Norbert; Walter, Achim; Liebisch, Frank

doi:10.3389/fpls.2020.00593

Cited by 34 publications

(25 citation statements)

References 96 publications

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“…The results of the subsequently conducted Dunnett test confirmed that the determination of VCOV traits was possible through the use of drone-mounted RGB cameras and that the accuracy of the results was sufficient for further quantitative genetic analysis. However, the results presented here were severely limited by the number of observations at the time of the trait increase, because to extract growth dynamics from imagery data, temporally high-resolution data collection is critical [20]. The weekly trait monitoring of the environments resulted in only 2-3 relevant observations that could be used for growth modelling until saturation was reached, which was not sufficient to detect the small trait variations within the population.…”

Section: Vegetation Covermentioning

confidence: 99%

“…Breeding selection at this early stage of the breeding cycle is based on a few rapidly collected traits and thus offers the most potential for improvement through a better phenotype database provided by UAV. The high temporal resolution enables the observation of dynamic growth processes and the detection of small phenotypic differences and thus the extraction of new traits regarding plant architecture and physiology [20,21]. In order to make optimal use of time series of measurements and to characterize dynamic growth, it is essential to carry out functional analyses in which mathematical functions are able to simulate the plant growth pattern [22,23].…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding

et al. 2021

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With advances in plant genomics, plant phenotyping has become a new bottleneck in plant breeding and the need for reliable high-throughput plant phenotyping techniques has emerged. In the face of future climatic challenges, it does not seem appropriate to continue to solely select for grain yield and a few agronomically important traits. Therefore, new sensor-based high-throughput phenotyping has been increasingly used in plant breeding research, with the potential to provide non-destructive, objective and continuous plant characterization that reveals the formation of the final grain yield and provides insights into the physiology of the plant during the growth phase. In this context, we present the comparison of two sensor systems, Red-Green-Blue (RGB) and multispectral cameras, attached to unmanned aerial vehicles (UAV), and investigate their suitability for yield prediction using different modelling approaches in a segregating barley introgression population at three environments with weekly data collection during the entire vegetation period. In addition to vegetation indices, morphological traits such as canopy height, vegetation cover and growth dynamics traits were used for yield prediction. Repeatability analyses and genotype association studies of sensor-based traits were compared with reference values from ground-based phenotyping to test the use of conventional and new traits for barley breeding. The relative height estimation of the canopy by UAV achieved high precision (up to r = 0.93) and repeatability (up to R2 = 0.98). In addition, we found a great overlap of detected significant genotypes between the reference heights and sensor-based heights. The yield prediction accuracy of both sensor systems was at the same level and reached a maximum prediction accuracy of r2 = 0.82 with a continuous increase in precision throughout the entire vegetation period. Due to the lower costs and the consumer-friendly handling of image acquisition and processing, the RGB imagery seems to be more suitable for yield prediction in this study.

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Section: Vegetation Covermentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding

et al. 2021

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“…Optimum planting geometry can be employed for the reduction of harmful effects on phenology due to climate change. Plant phenology is also affected by planting geometry 130 , 249 . Crop microclimate is changed by varying planting geometry like planting density, row spacing, seed rate etc.…”

Section: Adaptation Strategies In Response To Climate Changementioning

confidence: 99%

The fingerprints of climate warming on cereal crops phenology and adaptation options

Fatima

Ahmed

Hussain

et al. 2020

Sci Rep

204

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Growth and development of cereal crops are linked to weather, day length and growing degree-days (GDDs) which make them responsive to the specific environments in specific seasons. Global temperature is rising due to human activities such as burning of fossil fuels and clearance of woodlands for building construction. The rise in temperature disrupts crop growth and development. Disturbance mainly causes a shift in phenological development of crops and affects their economic yield. Scientists and farmers adapt to these phenological shifts, in part, by changing sowing time and cultivar shifts which may increase or decrease crop growth duration. Nonetheless, climate warming is a global phenomenon and cannot be avoided. In this scenario, food security can be ensured by improving cereal production through agronomic management, breeding of climate-adapted genotypes and increasing genetic biodiversity. In this review, climate warming, its impact and consequences are discussed with reference to their influences on phenological shifts. Furthermore, how different cereal crops adapt to climate warming by regulating their phenological development is elaborated. Based on the above mentioned discussion, different management strategies to cope with climate warming are suggested.

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“…For example, GCC has been found to be in good agreement with start-of-season and end-of-season indicators for honey mesquite, but not black grama; for both species it correlated well with canopy greenness [22]. However, [5] and [23] have found there is not a reliable direct correlation between LAI and GCC.…”

Section: Data Acquisition For Images and Gccmentioning

confidence: 94%

“…In a 2020 study, Aasen et al [23] determined there was not a clear correlation between GCC and LAI however, they were able to track LAI development using the PhenoCams. While we agree there is no universal GCC and LAI relationship, our methods adapted to each site and species do allow for LAI to be determined using PhenoCams.…”

Section: Jersand Nm: Creosote Bushmentioning

confidence: 99%

Use of PhenoCam Measurements and Image Analysis to Inform the ALMANAC Process-based Simulation Model

Jacot¹,

Kiniry

Williams

et al. 2021

JEAI

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Near-surface remote sensing has been used to document seasonal growth patterns (i.e. phenology) for plant communities in diverse habitats. Phenology from this source may only apply to the area within the images. Meanwhile ecosystem models can accommodate variable weather and landscape differences to plant growth, but accuracy is improved by adding ground-truthed inputs. The objective of this study was to use PhenoCam data, image analysis, and Beer’s law with established extinction coefficients to compare leaf area index (LAI) development in the ALMANAC model for diverse plant types and environments. Results indicate that PhenoCam time series imagery can be used to improve leaf area development in ALMANAC by adjusting parameter values to better match LAI derived values in new diverse environments. Soybeans, mesquite, and maize produced the most successful match between the model simulations and PhenoCam data out of the eight species simulated. This study represents, to our knowledge, the first independent evaluation of the ALMANAC process-based plant growth model with imagery in agroecosystems available from the PhenoCam network. The results show how PhenoCam data can make a valuable contribution to validate process-based models, making these models much more realistic and allows for expansion of PhenoCam influence.

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PhenoCams for Field Phenotyping: Using Very High Temporal Resolution Digital Repeated Photography to Investigate Interactions of Growth, Phenology, and Harvest Traits

Cited by 34 publications

References 96 publications

Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding

Evaluation of RGB and Multispectral Unmanned Aerial Vehicle (UAV) Imagery for High-Throughput Phenotyping and Yield Prediction in Barley Breeding

The fingerprints of climate warming on cereal crops phenology and adaptation options

Use of PhenoCam Measurements and Image Analysis to Inform the ALMANAC Process-based Simulation Model

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