Validation of an image-analysis-based method of measurement of the overall dimensions of seeds

Gierz, Łukasz; Markowski, Piotr; Chmielewski, Bartosz

doi:10.1088/1742-6596/1736/1/012007

Cited by 4 publications

(2 citation statements)

References 25 publications

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“…This software was designed to perform measurements for direct ear or grain morphometry. Different studies were conducted to compare the reference results for morphometric measurements with the results obtained from image analysis (Gierz et al 2021;Cirit et al, 2022). Again, a remarkable number of studies have been conducted about the comparison of morphometry results obtained from different image processing software (Makanza et al 2018;Loddo et al, 2022).…”

Section: Görüntü Analizi Kullanılarak Mısırda Koçan Ağırlığı Tane Ağı...mentioning

confidence: 99%

Prediction of Ear Weight, Kernel Weight and Viability in Maize Using Image Analysis

NESRİN,

KAHRIMAN

2023

ÇOMÜ Ziraat Fakültesi Dergisi

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In maize breeding studies, it is becoming common to determine the ear and kernel characteristics by image analysis. While current methods focus on measurements that can be obtained directly by image analysis, it has not been adequately addressed whether different parameters such as weight and viability can be estimated using these measurements. This study aimed to determine whether it is possible to estimate the ear weight (g), kernel weight (g), single kernel weight (g) and viability (1/0) status of maize with the help of features (area, perimeter, width, length) extracted from images of the ear and kernel samples. In this study, 233 ear and 1242 grain samples belonging to 13 maize genotypes were used as material. Digital images of the ear samples were taken with a 5 MP camera and from the kernel samples with a desktop scanner. The ear weight reference data (DV1) and the kernel weight reference data (DV2) were obtained by weighing each sample on a precision balance. Single kernel reference data (DV3) was obtained with the measurements of single kernel weights. Kernel samples underwent paper germination test and reference data (DV4) related to viability was created. Regression models were developed by using the features obtained from image analysis (area, perimeter, width, height) for each reference data set as the predictor variable. As a result of the study, it was seen that the ear weight and kernel weight can be estimated with the help of the parameters extracted from the image analysis. While moderate success was achieved in the determination of single seed weight, it was difficult to determine the viability status based on the morphometric measurements of a single kernel in maize.

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Section: Görüntü Analizi Kullanılarak Mısırda Koçan Ağırlığı Tane Ağı...mentioning

confidence: 99%

Prediction of Ear Weight, Kernel Weight and Viability in Maize Using Image Analysis

NESRİN,

KAHRIMAN

2023

ÇOMÜ Ziraat Fakültesi Dergisi

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“…A model of the corresponding geometric figure is built and used to calculate the seed parameters [10][11][12][13]. The second group of methods relies on digital images and image-processing software to elicit information about the geometric properties of seeds [14][15][16][17][18][19][20]. An image provides two-dimensional information about the examined particle.…”

Section: Introductionmentioning

confidence: 99%

Determination of Seed Volume Based on Selected Seed Dimensions

2022

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The volume coefficient, which denotes a simple relationship between selected seed dimensions and seed volume, can be used to facilitate volume calculations in individual seeds, in particular in species with a complex seed shape. For this reason, seed thickness, width, and length were measured in nine species of forest trees and shrubs. The volume of seeds belonging to each plant species was determined by pycnometry, and the results were used to calculate 10 volume coefficients based on different combinations of basic seed dimensions. The calculated coefficients had different values, and they were lowest when volume was determined based on the cube of seed length and highest when volume was determined based on the cube of seed thickness. In a formula based on all three basic dimensions, the calculated volume coefficient ranged from 0.376 to 0.537, and Cornus macrophylla, Picea abies, and Cornus sanguinea seeds most closely resembled an ellipsoid. When seed volume was determined with the use of two basic dimensions, formulas based on the square of the smaller dimension produced somewhat smaller errors in individual seeds. In turn, seed thickness should be used in formulas that rely on a single dimension. Seed volume coefficients were most strongly correlated with the sphericity index, which indicates that this parameter can be used to estimate their values. The sphericity index was most strongly correlated with volume coefficients; the strongest correlations were observed for volume coefficients calculated based on the square of the seed length and seed width, and the cube of the seed length.

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Non-Destructive Quality-Detection Techniques for Cereal Grains: A Systematic Review

et al. 2022

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Grain quality involves the appearance, nutritional, and safety attributes of grains. With the improvement of people’s living standards, problems pertaining to the quality of grains have received greater attention. Modern quality detection techniques feature unique advantages including rapidness, non-destructiveness, accuracy, and efficiency in detecting grain quality. This review summarizes research progress of these techniques in detection of quality indices of grains. Particularly, the review focuses on detection techniques based on physical properties including acoustic, optical, thermal, electrical, and mechanical properties, and those simulating sensory analysis such as electronic noses, electronic tongues, and electronic eyes. According to the current technological development and application, the challenges and prospects of these techniques are demonstrated.

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Validation of an image-analysis-based method of measurement of the overall dimensions of seeds

Cited by 4 publications

References 25 publications

Prediction of Ear Weight, Kernel Weight and Viability in Maize Using Image Analysis

Prediction of Ear Weight, Kernel Weight and Viability in Maize Using Image Analysis

Determination of Seed Volume Based on Selected Seed Dimensions

Non-Destructive Quality-Detection Techniques for Cereal Grains: A Systematic Review

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