scholarly journals Seed Quality Analysis Using Image Processing and ANN

2017 ◽  
Vol Volume-1 (Issue-4) ◽  
pp. 705-709
Author(s):  
Miss Shivpriya Desai ◽  
Dr.A.P. Rao ◽  
Keyword(s):  
Image Processing ◽  
Seed Quality ◽  
Quality Analysis

scholarly journals Survey of rice seed quality analysis for varietal purity estimation by using image processing techniques

2018 ◽  
Vol 7 (1.7) ◽  
pp. 34
Author(s):  
S. Durai ◽  
C. Mahesh ◽  
T. Sujithra ◽  
A. Suresh
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
South India ◽  
Seed Quality ◽  
Quality Analysis ◽  
Rice Seed ◽  
Two Factors ◽  
Computer Vision Technology ◽  
Processing Techniques ◽  
Varietal Purity

 In south India rice is the major food source and in agriculture, rice production covers more than 70 percentages of entire forming. But in recent the production only from south India not enough to satisfy the need of all, such a huge demand is there. The better production comes from the selection of good seeds. Up to now formers depend on two factors for selecting better seeds, One is the brand which is approved by some quality standards and second one is analyzed manually by experienced people. Both are risky one, we are not pretty much sure the accuracy of analyze. The second one is seeing and feeling. The inspection is not consistent also very time consuming. In the other way we can use computer vision technology to analyze the quality of the seeds. In recent years many of the big industries they are using computer vision technology with Digital Image Processing for many of the applications. In this Paper we are going to discuss the different seed quality analyzing methods and accuracy of result also. Moreover there are different factors and features are there for it, here we are going to study about varietal purity estimation by different methods.

scholarly journals Genetic variation of seed phosphorus concentration in winter oilseed rape and development of a NIRS calibration

Euphytica ◽  
2021 ◽  
Vol 217 (4) ◽  
Author(s):  
Jakob Eifler ◽  
Jürgen Enno Wick ◽  
Bernd Steingrobe ◽  
Christian Möllers
Keyword(s):  
Genetic Variation ◽  
Field Experiments ◽  
Seed Quality ◽  
Rapeseed Meal ◽  
Quality Analysis ◽  
Phosphorus Concentration ◽  
Genotypic Differences ◽  
Organic P ◽  
P Concentration ◽  
Total P

AbstractPhytic acid is the major organic phosphorus storage compound in rapeseed. Following oil extraction, the defatted meal is used in feed mixtures for livestock. However, monogastric pigs and chickens can only poorly metabolize phytate. Hence, their excrements are rich in phosphorus (P), which when applied as manure may lead to eutrophication of surface waters. The aim of the present study was to analyze the genetic variation for total and organic P concentration (i.e. mainly phytate) in rapeseed and to compare the results with soybean. Two sets of rapeseed material were tested in field experiments in different environments with varying soil P levels and harvested seeds were used for seed quality analysis. Results revealed significant genotypic differences in total seed P concentration, which ranged from 0.47 to 0.94%. Depending on the experiment, the heritability for total P concentration ranged from 52 to 93%. The organic P portion of total P concentration was above 90% for current rapeseed hybrids. In both sets, there was a significant positive correlation between seed protein and P concentration. A NIRS calibration for total P concentration in intact seeds showed in cross validation a standard error of 0.05% and a coefficient of determination of R2 = 0.83. Total P concentration of soybean seeds and meal was between 0.55 and 0.65%, and around 1.1% for rapeseed meal. Rapeseed meal had a twofold higher ratio of total P to nitrogen concentration as compared to soybean which could be considered adverse when the meal is used for feeding livestock.

Fermentation seed quality analysis with self-organising neural networks

1999 ◽  
Vol 64 (1) ◽  
pp. 82-91 ◽  
Author(s):  
M. Ignova ◽  
G. A. Montague ◽  
A. C. Ward ◽  
J. Glassey
Keyword(s):  
Neural Networks ◽  
Seed Quality ◽  
Quality Analysis

scholarly journals Automated high-throughput analysis of B cell spreading on immobilized antibodies with whole slide imaging

2015 ◽  
Vol 1 (1) ◽  
pp. 224-227 ◽  
Author(s):  
Veit Wiesmann ◽  
Dorothea Reimer ◽  
Daniela Franz ◽  
Hanna Hüttmayer ◽  
Dirk Mielenz ◽  
...  
Keyword(s):  
Image Processing ◽  
B Cells ◽  
Quality Analysis ◽  
Imaging Data ◽  
Whole Slide Imaging ◽  
High Quality ◽  
High Throughput Analysis ◽  
Fluorescent Cell ◽  
Immobilized Antibodies ◽  
Automated Image Processing

AbstractAutomated image processing methods enable objective, reproducible and high quality analysis of fluorescent cell images in a reasonable amount of time. Therefore, we propose the application of image processing pipelines based on established segmentation algorithms which can handle massive amounts of whole slide imaging data of multiple fluorescent labeled cells. After automated parameter adaption the segmentation pipelines provide high quality cell delineations revealing significant differences in the spreading of B cells: LPS-activated B cells spread significantly less on anti CD19 mAb than on anti BCR mAb and both processes could be inhibited by the F-actin destabilizing drug Cytochalasin D. Moreover, anti CD19 mAb induce a more symmetrical spreading than anti BCR mAb as reflected by the higher cell circularity.

scholarly journals Self-Organized Crystallization Patterns from Evaporating Droplets of Common Wheat Grain Leakages as a Potential Tool for Quality Analysis

2011 ◽  
Vol 11 ◽  
pp. 1712-1725 ◽  
Author(s):  
Maria Olga Kokornaczyk ◽  
Giovanni Dinelli ◽  
Ilaria Marotti ◽  
Stefano Benedettelli ◽  
Daniele Nani ◽  
...  
Keyword(s):  
Pattern Formation ◽  
Common Wheat ◽  
Seed Quality ◽  
Dark Field ◽  
Quality Analysis ◽  
Wheat Cultivars ◽  
Wheat Kernel ◽  
Self Organized ◽  
Complex Patterns ◽  
Pattern Forming

We studied the evaporation-induced pattern formation in droplets of common wheat kernel leakages prepared out of ancient and modern wheat cultivars as a possible tool for wheat quality analysis. The experiments showed that the substances which passed into the water during the soaking of the kernels created crystalline structures with different degrees of complexity while the droplets were evaporating. The forms ranged from spots and simple structures with single ramifications, through dendrites, up to highly organized hexagonal shapes and fractal-like structures. The patterns were observed and photographed using dark field microscopy in small magnifications. The evaluation of the patterns was performed both visually and by means of the fractal dimension analysis. From the results, it can be inferred that the wheat cultivars differed in their pattern-forming capacities. Two of the analyzed wheat cultivars showed poor pattern formation, whereas another two created well-formed and complex patterns. Additionally, the wheat cultivars were analyzed for their vigor by means of the germination test and measurement of the electrical conductivity of the grain leakages. The results showed that the more vigorous cultivars also created more complex patterns, whereas the weaker cultivars created predominantly poor forms. This observation suggests a correlation between the wheat seed quality and droplet evaporation patterns.

Sweet Pepper Seed Inspection Using Image Processing Techniques

2014 ◽  
Vol 931-932 ◽  
pp. 1614-1618 ◽  
Author(s):  
Prarin Chupawa ◽  
Kiattisin Kanjanawanishkul
Keyword(s):  
Image Processing ◽  
Seed Size ◽  
Seed Quality ◽  
Sweet Pepper ◽  
Seed Color ◽  
Image Processing Techniques ◽  
Human Skills ◽  
Asean Economic Community ◽  
Percent Accuracy ◽  
Processing Techniques

Since seeds are the foundation of agriculture and the Thai government plans to make Thailand an export hub of seeds under the Asean Economic Community (AEC) 2015, seed quality plays an important role in the seed production. Traditionally, physical attributes of seeds are inspected by human. However this method is very time-consuming and it highly relies on human skills and experience. Thus, in this paper, we focus on seed quality inspection of sweet pepper seeds using image processing techniques. Sweet peppers are very interesting since they have been one of the most important vegetable around the world and they have a variety of vitamins and nutrients. To identify defective sweet pepper seeds, two features used in our proposed algorithm are seed color and seed size. As shown in the results, percent accuracy of abnormal seed color and unaccepted seed size detection are 95.82% and 90.76%, respectively.

scholarly journals Interrelationship Among Seed Quality Parameters and Fungi Associated with Seeds of Cotton (Gossypium Hirsutum L.)

2021 ◽  
Vol 8 (1) ◽  
pp. 1061-1067
Author(s):  
Amina Khatun ◽  
Shamim Shamsi ◽  
MA Bashar
Keyword(s):  
Seed Quality ◽  
Rhizopus Oryzae ◽  
Germination Rate ◽  
Trichoderma Viride ◽  
The Other ◽  
Quality Analysis ◽  
Quality Factors ◽  
Seedling Mortality ◽  
Syncephalastrum Racemosum ◽  
Seed Moisture

A total of twenty nine species of fungi namely Aspergillus aculeatus Lizuka, A. flavus Link, A. fumigatus Fresenius, A. niger Van Tiegh, A. nidulans Eidam, A. subramanianii Visagie, Frisvad & Samson, A. tamariiKita G., A. toxicarius Murak, A. wentii Wehmer, Curvularial unata (Wakker) Boedijn, Colletotrichum gloeosporioides (Penz.) Sacc., C. gossypii Southw., Chaetomium globosum Kunze., Fusarium moniliforme J. Shelden, F. nivale (Fr.) Sorauer, F. oxysporum Schlechtendal, F. fujikuroi Nirenberg, F. solani(Mart.) Sacc., Lasiodiplodiatheobromae (Pat.) Griffon & Maubl., Meyerozyma guilliermondii (Wick.) Kurtzman & M. Suzuki., Mucor sp. P. Micheli ex L., Penicillium aculeatum Raper& Fennell, Penicillium citrinum Thom, Rhizoctonia solani Khun., Rhizopus stolonifer (Ehrenb.) Vuill., Rhizopus oryzae Went & Prins. Geerl., Rhizo mucor sp. Luce t & Costantin, Syncephalastrum racemosum Cohn and Trichoderma viride Pers. were found to be associated with the seeds of 14 varieties viz. CB-1 to CB-14 of cotton. Seed quality analysis showed that percentage of pure seeds, germination, moisture, seedling mortality and associated fungi with different varieties of cotton seeds varies from 97.08-99.92%, 80-93%, 10-11.3%, 16.05-50.30% and 24-78.0% respectively. Present study deals with estimation of interrelationship between some quality factors through correlation and regression analysis are important for the assessment of seed quality. There were negative correlations between seedling mortality and purity percentage and between germination rate and fungus frequency. On the other hand, positive correlations were found between germination rate and purity percentage, between seedling mortality and fungus frequency, between purity percentage and fungus frequency, between germination rate and seed moisture and between fungus frequency and seed moisture. CB 8 was superior from the other cotton variety as it showed higher physical purity of seed (98.41%), higher seed germination (92%), lower fungal incidence (24%) and lower mortality of seedling (23.91%). Bioresearch Commu. 8(1): 1061-1067, 2022 (January)

scholarly journals Rice Quality Analysis and Classification Using Image Processing Techniques

2021 ◽  
Vol 10 (6) ◽  
pp. 79-82
Author(s):  
Vijay Sonawane ◽  
Nikhil Gaikwad ◽  
Hrushikesh Mandekar ◽  
Kishore Baradkar ◽  
Chetan Gunjal
Keyword(s):  
Image Processing ◽  
Quality Analysis ◽  
World Population ◽  
Rice Grain ◽  
Rice Grains ◽  
Unique Shape ◽  
Good For ◽  
Phosphorus And Potassium ◽  
Processing Techniques

More than half the world's people consume rice every day and fulfills over 21% calorific requirement of world population. It is considered the whole grain which is rich in fiber and it contains 80 percent with protein, phosphorus, and potassium. There are hundreds of different varieties of rice and each rice grain has a unique shape, texture, and flavor that make it just right for certain dishes. The quality of rice between various types has different standards. Therefore, you must select the best quality rice because rice with best quality is not only good for consumption but also good for health. Analyzing grain sample manually is a tedious task and also time consuming. The paper presents the solution to analysis and grading of rice grains using image processing techniques. Image reduction, image enhancement, and image increment, object recognition in spatial domain is applied on grain by grain of different samples of rice to determine its size, color and quality as whole to grade the grain of rice. We find the endpoints of each grains and after we measure the length and breadth of rice grains.

Sign in / Sign up

Export Citation Format

Share Document