A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum

Thapa, Suresh; Zhu, Feiyu; Walia, Harkamal; Yu, Hongfeng; Ge, Yufeng

doi:10.3390/s18041187

Cited by 102 publications

(75 citation statements)

References 37 publications

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“…Phenotyping of individual plants can be done in very high detail with LiDAR, analyzing complex phenotypical properties, such as leaf area, leaf width, and leaf angle [5,6]. For this, plants were put on a slowly turning platform while a fixed LiDAR instrument was used.…”

Section: Introductionmentioning

confidence: 99%

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%

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

2019

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“…Other than the parameters above, tree volume and species can be estimated [25][26][27]. The detailed and accurate information from lidar provides a useful tool, for example, in the field of plant phenotyping [28]. Guo et al (2018) developed a high-throughput crop phenotyping platform with lidar that can retrieve various crop structural and physiological relevant parameters efficiently [29].…”

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confidence: 99%

Estimation of Leaf Inclination Angle in Three-Dimensional Plant Images Obtained from Lidar

Itakura

Hosoi

2019

Remote Sensing

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The leaf inclination angle is a fundamental variable for determining the plant profile. In this study, the leaf inclination angle was estimated automatically from voxel-based three-dimensional (3D) images obtained from lidar (light detection and ranging). The distribution of the leaf inclination angle within a tree was then calculated. The 3D images were first converted into voxel coordinates. Then, a plane was fitted to some voxels surrounding the point (voxel) of interest. The inclination angle and azimuth angle were obtained from the normal. The measured leaf inclination angle and its actual value were correlated and indicated a high correlation (R 2 = 0.95). The absolute error of the leaf inclination angle estimation was 2.5 • . Furthermore, the leaf inclination angle can be estimated even when the distance between the lidar and leaves is about 20 m. This suggests that the inclination angle estimation of leaves in a top part is reliable. Then, the leaf inclination angle distribution within a tree was calculated. The difference in the leaf inclination angle distribution between different parts within a tree was observed, and a detailed tree structural analysis was conducted. We found that this method enables accurate and efficient leaf inclination angle distribution.A 3D scanner called lidar (light detection and ranging) provides highly accurate and dense 3D point measurements [15,16]. The lidar is very useful for the retrieval of plant structural parameters. Trunk diameter and plant height can be estimated accurately [17][18][19]. Leaf area and parameters related to leaf area can be calculated from the 3D images of plants obtained by the lidar [20][21][22][23][24]. Other than the parameters above, tree volume and species can be estimated [25][26][27]. The detailed and accurate information from lidar provides a useful tool, for example, in the field of plant phenotyping [28]. Guo et al (2018) developed a high-throughput crop phenotyping platform with lidar that can retrieve various crop structural and physiological relevant parameters efficiently [29]. For more detailed information of the lidar application for plant structural parameters, please refer to the comprehensive reviews in [30,31].Some previous studies on leaf inclination angle estimation with 3D images are available [32][33][34][35][36][37]. In these studies, the points that composed each leaf were fitted to a plane by a least-squares method, and normals to the planes were calculated. Although this method is quite effective for leaf inclination angle estimation, each leaf should be selected manually in the 3D images one by one. Thus, exploring the distribution of the leaf inclination angle is very tedious and time-consuming. Further, owing to the manual operation, the number of leaves that can be examined is limited.To overcome this problem, a previous study [38] converted 3D point cloud images obtained with structure from a motion method into a voxel-based 3D image. In voxel-based 3D models, a geographical space is systematically decomposed into a ...

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“…Under artificial settings, Thapa et al . () used LiDAR 3D imaging and point cloud algorithms to successfully phenotype leaf area, and to a slightly lesser degree of accuracy leaf inclination angle. Another important interest to plant scientists is the study of below‐ground biomass traits, which until recently have been challenging to measure non‐destructively even in controlled conditions.…”

Section: Advances To Identify and Utilize Genetic Variation For Sorghmentioning

confidence: 99%

Genetic and genomic resources of sorghum to connect genotype with phenotype in contrasting environments

2018

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Summary With the recent development of genomic resources and high‐throughput phenotyping platforms, the 21st century is primed for major breakthroughs in the discovery, understanding and utilization of plant genetic variation. Significant advances in agriculture remain at the forefront to increase crop production and quality to satisfy the global food demand in a changing climate all while reducing the environmental impacts of the world's food production. Sorghum, a resilient C4 grain and grass important for food and energy production, is being extensively dissected genetically and phenomically to help connect the relationship between genetic and phenotypic variation. Unlike genetically modified crops such as corn or soybean, sorghum improvement has relied heavily on public research; thus, many of the genetic resources serve a dual purpose for both academic and commercial pursuits. Genetic and genomic resources not only provide the foundation to identify and understand the genes underlying variation, but also serve as novel sources of genetic and phenotypic diversity in plant breeding programs. To better disseminate the collective information of this community, we discuss: (i) the genomic resources of sorghum that are at the disposal of the research community; (ii) the suite of sorghum traits as potential targets for increasing productivity in contrasting environments; and (iii) the prospective approaches and technologies that will help to dissect the genotype–phenotype relationship as well as those that will apply foundational knowledge for sorghum improvement.

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A Novel LiDAR-Based Instrument for High-Throughput, 3D Measurement of Morphological Traits in Maize and Sorghum

Cited by 102 publications

References 37 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

Estimation of Leaf Inclination Angle in Three-Dimensional Plant Images Obtained from Lidar

Genetic and genomic resources of sorghum to connect genotype with phenotype in contrasting environments

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