High-Throughput Plant Phenotyping Platforms

Pratap, Aditya; Tomar, Rakhi; Kumar, Jitendra; Pandey, Vankat Raman; Mehandi, Suhel; Katiyar, P. K.

doi:10.1007/978-81-322-2226-2_19

Cited by 14 publications

(18 citation statements)

References 44 publications

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“…Assume that the tractor was moving at a constant speed during the interval (200 ms) of two adjacent GPS points. The distance of the two adjacent frames within two adjacent GPS points was computed using Equation (14). Therefore, the position of each LiDAR scanned frame was able to be obtained using Equation (15).…”

Section: Data Processing Algorithm and Performance Evalutaionmentioning

confidence: 99%

“…Several platforms based on remote sensing technologies have been developed for automatic crop phenotyping in greenhouses over the past three decades [13,14], but such controlled conditions are significantly different in many important ways from the outdoor agricultural environment in which the vast majority of crops are produced. Admittedly, there are tremendous challenges to developing field-based high-throughput phenotyping (HTP) systems in the highly variable natural environment, including variable natural lighting, high temperature, uncontrolled rainfall, and occlusion of plant features between adjacent plants within a plot or between plots, to name a activities since it has limited capability to provide high resolution information for crops which are much smaller than trees [29].…”

Section: Introductionmentioning

confidence: 99%

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In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR

Sun

Paterson

2017

Remote Sensing

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A LiDAR-based high-throughput phenotyping (HTP) system was developed for cotton plant phenotyping in the field. The HTP system consists of a 2D LiDAR and an RTK-GPS mounted on a high clearance tractor. The LiDAR scanned three rows of cotton plots simultaneously from the top and the RTK-GPS was used to provide the spatial coordinates of the point cloud during data collection. Configuration parameters of the system were optimized to ensure the best data quality. A height profile for each plot was extracted from the dense three dimensional point clouds; then the maximum height and height distribution of each plot were derived. In lab tests, single plants were scanned by LiDAR using 0.5 • angular resolution and results showed an R 2 value of 1.00 (RMSE = 3.46 mm) in comparison to manual measurements. In field tests using the same angular resolution; the LiDAR-based HTP system achieved average R 2 values of 0.98 (RMSE = 65 mm) for cotton plot height estimation; compared to manual measurements. This HTP system is particularly useful for large field application because it provides highly accurate measurements; and the efficiency is greatly improved compared to similar studies using the side view scan.

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Section: Data Processing Algorithm and Performance Evalutaionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR

Sun

Paterson

2017

Remote Sensing

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“…Visual observations and manual recording of data further increase the chance of errors and therefore increase the probability of identifying false positives. Greenhouse conditions are also not ideal as they do not adequately imitate the natural field conditions [29] and offer experimental bias due to biased effects of light and temperature gradients [30]. Moisture extraction from the soil in the field is slower than in pot culture due to the limited container volume, leading to faster depletion of soil moisture in pots [31].…”

Section: Phenotyping Bottlenecksmentioning

confidence: 99%

Using Plant Phenomics to Exploit the Gains of Genomics

et al. 2019

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Agricultural scientists face the dual challenge of breeding input-responsive, widely adoptable and climate-resilient varieties of crop plants and developing such varieties at a faster pace. Integrating the gains of genomics with modern-day phenomics will lead to increased breeding efficiency which in turn offers great promise to develop such varieties rapidly. Plant phenotyping techniques have impressively evolved during the last two decades. The low-cost, automated and semi-automated methods for data acquisition, storage and analysis are now available which allow precise quantitative analysis of plant structure and function; and genetic dissection of complex traits. Appropriate plant types can now be quickly developed that respond favorably to low input and resource-limited environments and address the challenges of subsistence agriculture. The present review focuses on the need of systematic, rapid, minimal invasive and low-cost plant phenotyping. It also discusses its evolution to modern day high throughput phenotyping (HTP), traits amenable to HTP, integration of HTP with genomics and the scope of utilizing these tools for crop improvement.

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“…Currently, there are many high-throughput phenotyping platforms to measure plant growth and morphology [4]. These platforms can be divided into three categories depending upon the measurement environment: platforms for laboratory-, greenhouse-, or field-based measurements [5]. The Australian Plant Phenotypic Group Facility (APPF) combines digital image processing technology, large-scale computing technology and robotics, which has been successfully applied to high-throughput precision modeling and prediction of grain biomass of greenhouse plants [6].…”

Section: Introductionmentioning

confidence: 99%

Image-Based On-Panicle Rice [Oryza sativa L.] Grain Counting with a Prior Edge Wavelet Correction Model

et al. 2018

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Abstract:The number of rice grains on a panicle is an important index for variety screening during high-quality rice [Oryza Sativa L.] breeding. For an in-vivo image-based measurement, the occlusion and overlapping among grains are the major challenges in non-destructive precise phenotyping of the on-panicle grains. In order to tackle these challenges, this paper describes a correction-model-referred on-panicle grain counting method based on the area of the rice panicle and its edge contour wavelet analysis. First, we assume that a deterministic correlation exists between the number of grains of the panicle and the traits of its edge contour morphology, which reflects the extent to which the grains are occluded. Second, a method for coarsely estimating grain number per panicle is proposed based on the projective area of the panicle in the image and the average area of a rice grain. Finally, a correction model which is built with the average wavelet frequency of the edge contour of the panicle is employed to correct the estimated value of the grain number. Two randomly selected cases are investigated in detail, showing that computation accuracy with a correction model is increased by 26% and 23% respectively when compared to that of the naive area-based computation.In conclusion, we reveal and validate the relationship between the number of grains of the panicle and the fluctuation frequency of its edge contours. Further, experiments show that errors caused by overlapping and occlusion scenarios can be alleviated with the estimation and correction hybrid models, achieving an average accuracy of 94% compared to the results of manual counting.

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High-Throughput Plant Phenotyping Platforms

Cited by 14 publications

References 44 publications

In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR

In-Field High-Throughput Phenotyping of Cotton Plant Height Using LiDAR

Using Plant Phenomics to Exploit the Gains of Genomics

Image-Based On-Panicle Rice [Oryza sativa L.] Grain Counting with a Prior Edge Wavelet Correction Model

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