Three Dimensional Seed Shape and Size Measurement with Orthogonal Cameras

Shahin, Mohamed A.; Symons, Stephen J.; Schepdael, L. Van; Tahir, Ahsen

doi:10.13031/2013.20883

Cited by 4 publications

(12 citation statements)

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“…Individual seed‐based comparisons of IA and micrometer measurements showed an R 2 > 0.92. These results were in agreement with earlier findings (Shahin et al ., ) demonstrating that a side‐mounted camera can be used to measure lentil seed size characteristics. Applications requiring greater accuracy for T on individual seed basis would require correction for error due to seed orientation as pointed out by Shahin et al .…”

Section: Resultsmentioning

confidence: 97%

“…The degree of E roundness was determined by the angle of tangents drawn from the seed edges as explained in Shahin et al . (). As shown in Figure , the edge angle was measured by ‘slicing’ the blob representing a seed into 16 equally spaced sections along the major axis and measuring angles of the two ‘triangular’ extremes.…”

Section: Methodsmentioning

confidence: 97%

“…A dual camera system described in Shahin et al . () was used to capture seed images. The camera system consisted of two orthogonal cameras to capture top and side view images of grain seeds (Figure ).…”

Section: Methodsmentioning

confidence: 99%

“…While these systems measured lentil seed D highly accurately, measurement of seed T from images captured with a scanner was not possible as the natural seed orientation would only expose seed D. A side view image of lentil seed would be required for measuring its T. Shahin et al . () developed a dual camera system to measure three‐dimensional size and shape characteristics of individual lentil seeds. They observed that a side‐mounted camera could accurately measure D, T and shape of edges.…”

Section: Introductionmentioning

confidence: 99%

“…This study extended the earlier work by Shahin et al . () with specific focus on understanding the effect of physical seed characteristics on dehulling quality of lentils. Specific objectives of this research were: (a) to measure lentil seed D, T, and roundness of edges (E) using a side‐mounted camera system; (b) to investigate the effect of seed size (D, T) and shape [plumpness (P), E] characteristics on dehulling efficiency (DE) of red lentils; and (c) to develop statistical models to predict (DE) based on seed size and shape characteristics.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Predicting dehulling efficiency of lentils based on seed size and shape characteristics measured with image analysis

Shahin¹,

Symons²,

Wang³

2011

Quality Assurance and Safety of Crops & Foods

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Introduction Seed size and shape are important factors influencing trade in pulse grains. Lentil plumpness (determined by shape and size characteristics such as seed diameter, thickness, edge curvature, etc) is an important seed characteristic commonly believed to affect dehulling quality of lentils. Physical measurements of lentil shape and size characteristics are monotonous and time consuming. Objectives The focus of this research was to develop an imaging method to measure seed size and shape characteristics for predicting dehulling efficiency of red lentils. Methods A side‐mounted camera system was used to image individual lentil seeds to determine seed size and shape characteristics. Results Regression models based on image analysis measurements of seed diameter, thickness, plumpness and degree of edge roundness predicted lentil dehulling efficiency highly accurately with an R2 approaching 0.90 and root‐mean‐squared‐error <2%. Conclusion Image analysis can be used to measure lentil seed size and shape characteristics, which in turn can predict dehulling efficiency of red lentils.

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Section: Resultsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 97%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Predicting dehulling efficiency of lentils based on seed size and shape characteristics measured with image analysis

Shahin¹,

Symons²,

Wang³

2011

Quality Assurance and Safety of Crops & Foods

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The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples

Halcro

McNabb

Lockinger

et al. 2019

Preprint

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AbstractBackgroundSeed analysis is currently a bottleneck in phenotypic analysis of seeds. Measurements are slow and imprecise with potential for bias to be introduced when gathered manually. New acquisition tools were requested to improve phenotyping efficacy with an emphasis on obtaining colour information.ResultsA portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis. Lentil (Lens culinaris L.) phenotyping was used as the primary test case. Seeds were loaded into the system and all seeds in a sample were automatically and individually imaged to acquire top and side views as they passed through an imaging chamber. A Python analysis script applied a colour calibration and extracted quantifiable traits of seed colour, size and shape. Extraction of lentil seed coat patterning was implemented to further describe the seed coat. The use of this device was forecasted to eliminate operator biases, increase the rate of acquisition of traits, and capture qualitative information about traits that have been historically analyzed by eye.ConclusionsIncreased precision and higher rates of data acquisition compared to traditional techniques will help breeders to develop more productive cultivars. The system presented is available as an open-source project for academic and non-commercial use.

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The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples

et al. 2020

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Background: Quantitative and qualitative assessment of visual and morphological traits of seed is slow and imprecise with potential for bias to be introduced when gathered with handheld tools. Colour, size and shape traits can be acquired from properly calibrated seed images. New automated tools were requested to improve data acquisition efficacy with an emphasis on developing research workflows.Results: A portable imaging system (BELT) supported by image acquisition and analysis software (phenoSEED) was created for small-seed optical analysis. Lentil (Lens culinaris L.) phenotyping was used as the primary test case. Seeds were loaded into the system and all seeds in a sample were automatically individually imaged to acquire top and side views as they passed through an imaging chamber. A Python analysis script applied a colour calibration and extracted quantifiable traits of seed colour, size and shape. Extraction of lentil seed coat patterning was implemented to further describe the seed coat. The use of this device was forecasted to eliminate operator biases, increase the rate of acquisition of traits, and capture qualitative information about traits that have been historically analyzed by eye. Conclusions:Increased precision and higher rates of data acquisition compared to traditional techniques will help to extract larger datasets and explore more research questions. The system presented is available as an open-source project for academic and non-commercial use.

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Three Dimensional Seed Shape and Size Measurement with Orthogonal Cameras

Cited by 4 publications

References 0 publications

Predicting dehulling efficiency of lentils based on seed size and shape characteristics measured with image analysis

Predicting dehulling efficiency of lentils based on seed size and shape characteristics measured with image analysis

The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples

The BELT and phenoSEED platforms: shape and colour phenotyping of seed samples

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