Seed Image Analysis and Its Application in Seed Science Research

Vn, Kapadia

doi:10.19080/aibm.2017.07.555709

Cited by 13 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Image analysis has many uses in agriculture (Doğan et al, 2018). The main areas for use of image analysis in kernel measurements are; characterization and identification, classification and grading, physiological tests, detection of mechanical or disease damage, determination of color and morphological features (Kiratiratanapruk and Sinthupinyo, 2011;Kapadia et al, 2017;Yafie et al, 2020;Beyaz and Gerdan, 2021). Among these purposes, the use of image analysis in morphometric measurements has become quite widespread.…”

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

View full text Add to dashboard Cite

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.

show abstract

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

View full text Add to dashboard Cite

show abstract

“…The advancement of digital imaging technology has been extensively used for the precise and efficient evaluation of phenotypic traits in plants (Omari et al, 2020). Analyzing images captured under controlled conditions can greatly assist in accurate characterization, including the characterization of seed traits (Kapadia et al, 2017;Hemender et al, 2018). The utilization of digital imaging in seed characterization has been documented for various crops, including tomato (Borges et al, 2019), soybean (Franca-Silva et al, 2023), rice (Santos et al, 2019), and melon (Medeiros et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Digital phenotyping for robust seeds variability assessment in Setaria italica (L.) P. Beauv.

Jannah,

Ratnawati,

Suwarno

et al. 2024

J. Seed Sci.

View full text Add to dashboard Cite

Foxtail millet (Setaria italica L.) is a cereal crop with potential as a functional food due to its high nutritional value and its wide adaptability to unfavorable environmental conditions. Seed-related traits of foxtail millet are difficult to observe due to its small size. Therefore, the development of an efficient and accurate method for characterizing foxtail millet seeds using digital imaging technology is essential. This study aimed to characterize the seed morphology for variability assessment of Indonesian local foxtail millet genotypes and to develop a model to estimate the 100-seed weight using ImageJ. A total of 28 Indonesian local foxtail millet genotypes were used in this study for seed morphology characterization and the development of the 100-seed weight estimation model. Foxtail millet genotypes from different regions in Indonesia exhibited diverse seed morphologies. The 100-seed weight estimation model (y = 0.123x - 0.0821 (R² = 0.9223)) demonstrated a highly significant positive correlation (r = 0.96, p < 0.01) between the predicted and actual 100-seed weights. The correlation coefficient from model validation was 0.8731 (p < 0.01), indicating that the obtained model could estimate the 100-seed weight of foxtail millet seeds in future studies.

show abstract

“…It has also been indicated for reducing costs and labor, with accurate results (Sousa et al, 2015). Additionally, evaluating seedling growth by image analysis can be interesting because it is accurate, faster, and less expensive, as is an automated procedure (Kapadia et al, 2017;Medeiros et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Germination under stress simulation and image analysis as tools for water deficit phenotyping of maize

Pinho,

Souza,

Vasconcellos

et al. 2024

J. Seed Sci.

View full text Add to dashboard Cite

Faster phenotyping tools are crucial for field progeny selection. We conducted research comparing two water deficit simulation methods on contrasting cultivars for water deficit tolerance. In a first step, we conducted two experiments: one for seed germination in sand at 10% and 70% water retention capacity, being analyzed seedling emergence and growth; other for seed germination in stress simulation by moistening the germination paper with PEG solution at -0.6 MPa, being analyzed seed germination. These experiments were used to distinguish characteristics of two maize lineages with different response to drought, being 57 - sensitive and 91 - tolerant. After that, we produced hybrid progenies from these lines at generations F1, F2, and F2:3 and applied the stress simulation by moistening the germination paper with -0.6 MPa of PEG solution. The seedling size was analyzed trough image analysis by the GroundEye® system. We could distinguish both lines and its hybrids through the stress simulation and image analysis. The results indicate that maize cultivars can be phenotyped for water deficit tolerance either 5 days postsowing through stress simulation or via image analysis of root length from seedlings germinated under -0.6 MPa. This method provides faster, more accurate, and more cost-effective methods for assessing water deficit tolerance in maize cultivars.

show abstract

Seed Image Analysis and Its Application in Seed Science Research

Cited by 13 publications

References 8 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

Digital phenotyping for robust seeds variability assessment in Setaria italica (L.) P. Beauv.

Germination under stress simulation and image analysis as tools for water deficit phenotyping of maize

Contact Info

Product

Resources

About