High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR

Berni, J.A.J.; Deery, David M.; Larraondo, Pablo Rozas; Condon, Anthony G.; Rebetzke, G. J.; James, Richard A.; Bovill, William D.; Furbank, Robert T.; Sirault, Xavier

doi:10.3389/fpls.2018.00237

Cited by 253 publications

(264 citation statements)

References 71 publications

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“…Morphological parameters, such as plant height, stem diameter, leaf area or leaf area index (LAI), leaf angle, stalk length, and in-plant space [1], can be determined with LiDAR (light detection and ranging). Research on phenotyping using LiDAR often focusses on one specific crop, for example, wheat [2,3] or cotton [4].…”

Section: Introductionmentioning

confidence: 99%

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Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

2019

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Phenotyping of crops is important due to increasing pressure on food production. Therefore, an accurate estimation of biomass during the growing season can be important to optimize the yield. The potential of data acquisition by UAV-LiDAR to estimate fresh biomass and crop height was investigated for three different crops (potato, sugar beet, and winter wheat) grown in Wageningen (The Netherlands) from June to August 2018. Biomass was estimated using the 3DPI algorithm, while crop height was estimated using the mean height of a variable number of highest points for each m 2 . The 3DPI algorithm proved to estimate biomass well for sugar beet (R 2 = 0.68, RMSE = 17.47 g/m 2 ) and winter wheat (R 2 = 0.82, RMSE = 13.94 g/m 2 ). Also, the height estimates worked well for sugar beet (R 2 = 0.70, RMSE = 7.4 cm) and wheat (R 2 = 0.78, RMSE = 3.4 cm). However, for potato both plant height (R 2 = 0.50, RMSE = 12 cm) and biomass estimation (R 2 = 0.24, RMSE = 22.09 g/m 2 ), it proved to be less reliable due to the complex canopy structure and the ridges on which potatoes are grown. In general, for accurate biomass and crop height estimates using those algorithms, the flight conditions (altitude, speed, location of flight lines) should be comparable to the settings for which the models are calibrated since changing conditions do influence the estimated biomass and crop height strongly. good correlation with in situ field measurements of plant height. Sun et al. [4] published an R 2 of 0.98 for cotton plants, [2] published an R 2 of 0.99 for wheat, and [7] showed an R 2 of 0.90 for wheat. These studies show the capability of LiDAR to measure basic phenotypes such as plant height. However, LiDAR systems mounted on tractors can be unsuitable for labour-intensive crops such as rice or in orchards [8], for example, due to compaction of the soil [9]. A UAV equipped with a LiDAR system can overcome those limitations.Earlier research on the relation between plant height and biomass was based on varying approaches for plant height measurements. Madec et al. [7] found an R 2 of 0.88 for the correlation between plant height and field-measured biomass, using a tractor based LiDAR system. Bendig et al.[10] used a structure from a motion (SfM) technique on UAV acquired imagery to derive plant height and found an R 2 of 0.81 between field-measured height and SfM derived height.In the last few years, LiDAR systems have been miniaturized, resulting in lower weights and reduced dimensions, and as a result, can be operated from UAVs. This development opens the way towards high throughput derived, more complex products like biomass and yield, thus, improving the speed and frequency at which these plant traits can be acquired in the field in an undisturbed way.LiDAR-based biomass estimates of agricultural crops can be derived in different ways. Based on the Lambert-Beer LAI model of [11], the authors of [2] developed a biomass prediction model called the 3-Dimensional Profile Index (3DPI). Where the LIDAR 3D point cloud is divided into layers...

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Section: Introductionmentioning

confidence: 99%

“…good correlation with in situ field measurements of plant height. Sun et al [4] published an R 2 of 0.98 for cotton plants, [2] published an R 2 of 0.99 for wheat, and [7] showed an R 2 of 0.90 for wheat. These studies show the capability of LiDAR to measure basic phenotypes such as plant height.…”

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confidence: 99%

Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

2019

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“…Measurement of plant height using a ruler has long been the traditional approach [12,16,17]. Assessment of plant height from images is a far more complex process as it necessitates the estimation of depth in physical units; in discipline terms, a so-called depth map is reconstructed from multiple images of a canopy taken from slightly different viewpoints.…”

Section: Introductionmentioning

confidence: 99%

“…Relevant work in this area has shown that accurate estimates are possible and indeed preferable given their objectivity and accuracy, compared with manual measurements, which can be subjective, as well as incomplete [5]. An alternative method, light detection and ranging (LiDAR), uses an active laser sensor to non-destructively measure canopy height with high accuracy [17].…”

Section: Introductionmentioning

confidence: 99%

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

et al. 2018

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Abstract:This study evaluates an aerial and ground imaging platform for assessment of canopy development in a wheat field. The dependence of two canopy traits, height and vigour, on fertilizer treatment was observed in a field trial comprised of ten varieties of spring wheat. A custom-built mobile ground platform (MGP) and an unmanned aerial vehicle (UAV) were deployed at the experimental site for standard red, green and blue (RGB) image collection on five occasions. Meanwhile, reference field measurements of canopy height and vigour were manually recorded during the growing season. Canopy level estimates of height and vigour for each variety and treatment were computed by image analysis. The agreement between estimates from each platform and reference measurements was statistically analysed. Estimates of canopy height derived from MGP imagery were more accurate (RMSE = 3.95 cm, R 2 = 0.94) than estimates derived from UAV imagery (RMSE = 6.64 cm, R 2 = 0.85). In contrast, vigour was better estimated using the UAV imagery (RMSE = 0.057, R 2 = 0.57), compared to MGP imagery (RMSE = 0.063, R 2 = 0.42), albeit with a significant fixed and proportional bias. The ability of the platforms to capture differential development of traits as a function of fertilizer treatment was also investigated. Both imaging methodologies observed a higher median canopy height of treated plots compared with untreated plots throughout the season, and a greater median vigour of treated plots compared with untreated plots exhibited in the early growth stages. While the UAV imaging provides a high-throughput method for canopy-level trait determination, the MGP imaging captures subtle canopy structures, potentially useful for fine-grained analyses of plants.

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“…By scanning across a physical area with multiple sensors or by grid-serpentine sampling with a single sensor, a three-dimensional point cloud can be obtained. Lidar has demonstrated accurate predictions (R 2 > 0.80) for crop density in wheat (Triticum aestivum L.; Saeys et al, 2009;Hosoi and Omasa, 2009) Zhang and Grift, 2012), rice (Oryza sativa L.; Tilly et al, 2014), and maize (Zea mays L.; Luo et al, 2016); canopy volume in tree species (Rosell et al, 2009); and biomass in maize (Luo et al, 2016), alfalfa (Medicago sativa L.; Noland et al, 2018), and wheat (Jimenez-Berni et al, 2018). Numerous other forage crops have exhibited moderate to high correlations between lidar and biomass (Freeman et al, 2007;Schaefer and Lamb, 2016;Ghamkhar et al, 2018).…”

Section: Hairy Vetch (Vicia Villosamentioning

confidence: 99%

Lidar and RGB Image Analysis to Predict Hairy Vetch Biomass in Breeding Nurseries

Wiering

Ehlke

2019

The Plant Phenome Journal

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Core Ideas Early‐season biomass is conventionally phenotyped by subjective visual estimates. RGB image data are highly predictive of biomass in hairy vetch breeding plots. Lidar and RGB image data can be combined to accurately predict sward biomass. Remote sensing could increase genetic gain potential for biomass in cover crops. Hairy vetch (Vicia villosa Roth) is an annual legume grown as a forage and cover crop. To improve cover crop function, traits such as biomass production are of high interest for cover crop breeders. However, direct phenotypic methods for biomass production are destructive. Breeders have thus relied on subjective, visual scoring of biomass, which is generally correlative but not quantitative or absolute. We evaluated two low‐cost remote sensing tools, lidar and red–green–blue (RGB) image analysis, for their potential to predict biomass in vivo. We evaluated these tools in two common forage breeding scenarios, spaced‐plant and sward‐plot nurseries, at three Minnesota locations following the winter of 2016–2017. Ground cover from RGB image binarization had a significant and linear relationship with aboveground biomass in spaced plants (R2 = 0.93) and sward plots (R2 = 0.89). However, once the image area from sward plots became saturated with vegetative pixels, a near‐exponential relationship would occur. Because of the prostrate growth habit of hairy vetch, RGB image analysis was more appropriate at lower plant densities, such as spaced‐plant nurseries. Conversely, the dimensionality of lidar sensing gave it greater predictive ability at higher plant densities where RGB analysis could not detect vertical increases in biomass. Lidar measures of sward‐plot height were also linearly and strongly related to dry‐matter biomass in sward plots (R2 = 0.80). When we combined RGB and lidar data to predict sward‐plot biomass in a multiple mixed‐effect regression model, we were able to explain more biomass variation than with the use of either phenotypic tool as a single predictor (R2 = 0.94).

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High Throughput Determination of Plant Height, Ground Cover, and Above-Ground Biomass in Wheat with LiDAR

Cited by 253 publications

References 71 publications

Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar

Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery

Lidar and RGB Image Analysis to Predict Hairy Vetch Biomass in Breeding Nurseries

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