Image Analysis: A Modern Approach to Seed Quality Testing

Hemender,; Sharma, Sushma; Mor, V. S.; Jitender,; Bhuker, Axay

doi:10.9734/cjast/2018/40945

Cited by 13 publications

(4 citation statements)

References 36 publications

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“…An increased and fast germination provides high number of seedlings that cover the soil rapidly, not only promoting the formation of homogeneous stand but also increasing the seedling potential for using more efficiently radiation and nutrients in relation to weeds (Atkinson et al, 2019 ). Additionally, vigorous seedlings present an improved capacity to endure stressing conditions (Hemender et al, 2018 ). Nevertheless, seed quality assessment is currently centered on germination and vigor tests that are destructive, time consuming, labor intensive, and requiring experienced seed analysts.…”

Section: Introductionmentioning

confidence: 99%

Integrating Optical Imaging Tools for Rapid and Non-invasive Characterization of Seed Quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as Study Cases

et al. 2020

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Light-based methods are being further developed to meet the growing demands for food in the agricultural industry. Optical imaging is a rapid, non-destructive, and accurate technology that can produce consistent measurements of product quality compared to conventional techniques. In this research, a novel approach for seed quality prediction is presented. In the proposed approach two advanced optical imaging techniques based on chlorophyll fluorescence and chemometric-based multispectral imaging were employed. The chemometrics encompassed principal component analysis (PCA) and quadratic discrimination analysis (QDA). Among plants that are relevant as both crops and scientific models, tomato, and carrot were selected for the experiment. We compared the optical imaging techniques to the traditional analytical methods used for quality characterization of commercial seedlots. Results showed that chlorophyll fluorescence-based technology is feasible to discriminate cultivars and to identify seedlots with lower physiological potential. The exploratory analysis of multispectral imaging data using a non-supervised approach (two-component PCA) allowed the characterization of differences between carrot cultivars, but not for tomato cultivars. A Random Forest (RF) classifier based on Gini importance was applied to multispectral data and it revealed the most meaningful bandwidths from 19 wavelengths for seed quality characterization. In order to validate the RF model, we selected the five most important wavelengths to be applied in a QDA-based model, and the model reached high accuracy to classify lots with high-and low-vigor seeds, with a correct classification from 86 to 95% in tomato and from 88 to 97% in carrot for validation set. Further analysis showed that low quality seeds resulted in seedlings with altered photosynthetic capacity and chlorophyll content. In conclusion, both chlorophyll fluorescence and chemometrics-based multispectral imaging can be applied as reliable proxies of the physiological potential in tomato and carrot seeds. From the practical point of view, such techniques/methodologies can be potentially used for screening low quality seeds in food and agricultural industries.

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Section: Introductionmentioning

confidence: 99%

Integrating Optical Imaging Tools for Rapid and Non-invasive Characterization of Seed Quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as Study Cases

et al. 2020

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“…The positive aspects of image analysis over more traditional approaches include quick assessments, cost-effectiveness, automated nature, and a user-friendly working environment. As it is faster, more accurate, and offers close examination of seeds and sprouting seedlings, image analysis systems have been demonstrated to be extremely useful methods for seed-related studies [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Analysis and prediction of seed quality using machine learning

Srinivasaiah,

Meenakshi,

Chennegowda

et al. 2023

IJECE

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<span lang="EN-US">The mainstay of the economy has always been agriculture, and the majority of tasks are still carried out without the use of modern technology. Currently, the ability of human intelligence to forecast seed quality is used. Because it lacks a validation method, the existing seed prediction analysis is ineffective. Here, we have tried to create a prediction model that uses machine learning algorithms to forecast seed quality, leading to high crop yield and high-quality harvests. For precise seed categorization, this model was created using convolutional neural networks and trained using the seed dataset. Using data that can be used to forecast the future, this model is used to learn about whether the seeds are of premium quality, standard quality, or regular quality. While testing data are employed in the algorithm’s predictive analytics, training data and validation data are used for categorization reasons. Thus, by examining the training accuracy of the convolution neural network (CNN) model and the prediction accuracy of the algorithm, the project’s primary goal is to develop the best method for the more accurate prediction of seed quality.</span>

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“…Its seeds are widely consumed in several regions of Brazil, especially in the North and Northeast (WANDERLEY JÚNIOR; NASCIMENTO, 2008;SOUZA et al, 2011). Seed maturation is one of the physiological quality parameters, being a crucial point for the health and vigor of plants, which can be determined by seed coat color (HEMENDER et al, 2018;JUSTINO et al, 2015). According to Embrapa Pantanal (VAZ, 2007), coriander seeds are ready to be harvested when about 50 -60% fruits are golden yellow or light brown, depending on the variety.…”

Section: Introductionmentioning

confidence: 99%

“…CROP PRODUCTION separation analysis methods are extremely exhausting and highly susceptible to failures, they have been replaced by image analysis technologies, especially in the post-harvest process (SANTANNA et al, 2014;HEMENDER et al, 2018). Image processing makes use of algorithms to extract objects, which can be developed with the help of several tools, including Matlab Mathematical ® (ARAÚJO et al, 2015;MONTEIRO et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Separation of coriander seeds by Red, Green and Blue image processing

et al. 2022

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Coriander seeds have high socio-economic value in several regions of Brazil, especially in the North and Northeast. Seed maturation determined by color influences the seed quality. With this, digital image processing has become an important tool for separating seeds by color since this classification is usually performed by humans and is highly susceptible to error. The study established parameters for separating coriander seeds by red green and blue (RGB) image analysis, seeking a better selection of coriander seeds according to their color, and evaluating the physiological quality by the germination test. Separation was carried out from two coriander seed lots to obtain samples of 20 g each in three shades: yellowish, gray, and mixed. Images were acquired by the HP C4480 Scanner and processed in the MATLAB software; then, a histogram was constructed for each color analyzed in each sample by the RGB system. ANOVA tested the averages of the scales to ratify the difference in the components’ distributions. The germination test was performed to confirm the results of seed separation using image analysis. The best selection of coriander seeds was achieved by the blue scale, and the germination test indicated that yellow seeds have a higher physiological quality than brownish/greyish seeds.

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Image Analysis: A Modern Approach to Seed Quality Testing

Cited by 13 publications

References 36 publications

Integrating Optical Imaging Tools for Rapid and Non-invasive Characterization of Seed Quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as Study Cases

Integrating Optical Imaging Tools for Rapid and Non-invasive Characterization of Seed Quality: Tomato (Solanum lycopersicum L.) and Carrot (Daucus carota L.) as Study Cases

Analysis and prediction of seed quality using machine learning

Separation of coriander seeds by Red, Green and Blue image processing

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