Automatic Phenotyping Test of Sunflower Seeds

Helia ◽  
2020 ◽  
Vol 43 (72) ◽  
pp. 51-66
Author(s):  
Elchyn Aliiev
Keyword(s):  
Test Procedure ◽  
Color Space ◽  
Experimental Studies ◽  
Average Error ◽  
Sunflower Seeds ◽  
Color Distribution ◽  
Individual Measurement ◽  
Functional Features ◽  
Rgb Color Space ◽  
Automatic Phenotyping

AbstractThe development of automated precision technologies for the phenotyping test of seeds by a complex of functional features in the selection process of sunflower is relevant and promising. The task of developing a device for the automatic phenotyping test of seeds and the algorithm for finding and isolating seeds based on color information was set. Research was conducted on a stand, which consisted of the following elements: Video Microscope Camera 1080 P 16MP HDMI USB manufactured by Eakins, a set of LEDs of three types (red, green, blue) and a personal computer.The results of experimental studies of the process of automatic phenotyping test of seeds of different sunflower varieties allowed us to establish an average error of determining the geometric dimensions of sunflower seeds (length L and width B) – 0.06 mm. The histograms of the color distribution of sunflower seeds in the RGB color space with different illumination are established. As a result of the analysis of the obtained histograms of the color distribution of sunflower seeds in the RGB color space it is established that in the case of color homogeneity, the discreteness of the channels with red illumination is most clearly seen.A device for automatic phenotyping test of seeds has been developed, which preserves the accuracy of individual measurement of the geometric dimensions of sunflower seeds, determining their shape and color, which corresponds to modern measuring tools, and provides low complexity and high technological implementation of the phenotyping test procedure (determination, ascertaining and identification) material, according to its morphological and marker features.

Precision separation process of sunflower seeds

2021 ◽  
pp. 117-126
Keyword(s):  
Sunflower Seed ◽  
Low Complexity ◽  
Functional Dependencies ◽  
Sunflower Seeds ◽  
Technological Parameters ◽  
Automated Control ◽  
Separation Processes ◽  
Individual Measurement ◽  
Seed Material ◽  
Automatic Phenotyping

According to the requirements for the technological processes of purification and separation of the seed mixture to obtain the sunflower seed material of the parent components (varietal purity – 98,0-99,9%) for all parts of the breeding and seed production process, a rational precision technological scheme of the separation processes has been developed, which includes automation of technical processes of separation means. In order to increase the efficiency of the sunflower breeding and seed-growing process, a device for automatic seed phenotyping has been added to the developed technological line, which can significantly intensify and shorten the breeding process and improve the design of the breeding program through bioinformatic data analysis and seed sorting. Functional dependencies are established and methods of automated control of precision mechanized process of seed separation are developed on the basis of coordination of its mode and technological parameters. Tape device for automatic phenotyping of sunflower seed material according to its morphological and marker features have been developed. The device are configured for high accuracy of individual measurement of the geometric dimensions of sunflower seeds with determination of their shape and color and provide low complexity and high technological implementation of the phenotyping process (determination, identification and separation) of seeds.

Modeling and Experimental Studies on Oscillating Inclined-Bed Solar Dryer

2013 ◽  
Vol 135 (3) ◽  
Author(s):  
S. Shanmugam ◽  
P. Kumar ◽  
AR. Veerappan
Keyword(s):  
Thermal Efficiency ◽  
Experimental Studies ◽  
Absolute Humidity ◽  
Average Error ◽  
Sunflower Seeds ◽  
Double Pass ◽  
Solar Dryer ◽  
Agricultural Produce ◽  
The Absolute ◽  
Flat Plate Collector

This paper presents a mathematical model for drying agricultural produce using a solar dryer capable of oscillating its bed while kept at an inclined position with respect to vertical. A model of the solar dryer with double-pass flat plate collector and an oscillating-bed has been fabricated and tested for drying agricultural produce (sunflower seeds). The model can predict the change in the absolute humidity of air across the bed, the temperature of the air, the moisture content, and the dryer thermal efficiency. Sunflower seeds were dried on a physical model of the solar dryer with its bed tilted at different angles and oscillated at different frequencies. The predicted and the experimental results are in good agreement. The average error and standard deviation for the absolute humidity of air across the bed is 1.74 and 1.55%, the exit temperature of air leaving the bed is 1.11and 1.21%, and the dryer thermal efficiency is 0.78 and 1.33%, respectively.

scholarly journals The Application of Machine Learning for Cultivar Discrimination of Sweet Cherry Endocarp

Agriculture ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 6
Author(s):  
Ewa Ropelewska
Keyword(s):  
Sweet Cherry ◽  
Color Space ◽  
Geometric Parameters ◽  
Geometric Features ◽  
Color Spaces ◽  
Discriminative Models ◽  
Sweet Cherries ◽  
Rgb Color Space ◽  
Cultivar Discrimination ◽  
Color Channels

The aim of this study was to evaluate the usefulness of the texture and geometric parameters of endocarp (pit) for distinguishing different cultivars of sweet cherries using image analysis. The textures from images converted to color channels and the geometric parameters of the endocarp (pits) of sweet cherry ‘Kordia’, ‘Lapins’, and ‘Büttner’s Red’ were calculated. For the set combining the selected textures from all color channels, the accuracy reached 100% when comparing ‘Kordia’ vs. ‘Lapins’ and ‘Kordia’ vs. ‘Büttner’s Red’ for all classifiers. The pits of ‘Kordia’ and ‘Lapins’, as well as ‘Kordia’ and ‘Büttner’s Red’ were also 100% correctly discriminated for discriminative models built separately for RGB, Lab and XYZ color spaces, G, L and Y color channels and for models combining selected textural and geometric features. For discrimination ‘Lapins’ and ‘Büttner’s Red’ pits, slightly lower accuracies were determined—up to 93% for models built based on textures selected from all color channels, 91% for the RGB color space, 92% for the Lab and XYZ color spaces, 84% for the G and L color channels, 83% for the Y channel, 94% for geometric features, and 96% for combined textural and geometric features.

scholarly journals A Method of Segmenting Apples Based on Gray-Centered RGB Color Space

2021 ◽  
Vol 13 (6) ◽  
pp. 1211
Author(s):  
Pan Fan ◽  
Guodong Lang ◽  
Bin Yan ◽  
Xiaoyan Lei ◽  
Pengju Guo ◽  
...  
Keyword(s):  
Vision System ◽  
False Positive Rate ◽  
Clustering Algorithms ◽  
False Negative ◽  
Color Space ◽  
False Negative Rate ◽  
Feature Selection Method ◽  
Image Features ◽  
Accurate Identification ◽  
Rgb Color Space

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot’s vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red–green–blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.

scholarly journals A Watershed-Segmentation-Based Improved Algorithm for Extracting Cultivated Land Boundaries

2021 ◽  
Vol 13 (5) ◽  
pp. 939
Author(s):  
Yongan Xue ◽  
Jinling Zhao ◽  
Mingmei Zhang
Keyword(s):  
Color Space ◽  
Cultivated Land ◽  
Color Spaces ◽  
Region Merging ◽  
Watershed Segmentation ◽  
Coverage Area ◽  
Time Efficiency ◽  
Image Contrast Enhancement ◽  
Segmentation Accuracy ◽  
Rgb Color Space

To accurately extract cultivated land boundaries based on high-resolution remote sensing imagery, an improved watershed segmentation algorithm was proposed herein based on a combination of pre- and post-improvement procedures. Image contrast enhancement was used as the pre-improvement, while the color distance of the Commission Internationale de l´Eclairage (CIE) color space, including the Lab and Luv, was used as the regional similarity measure for region merging as the post-improvement. Furthermore, the area relative error criterion (δA), the pixel quantity error criterion (δP), and the consistency criterion (Khat) were used for evaluating the image segmentation accuracy. The region merging in Red–Green–Blue (RGB) color space was selected to compare the proposed algorithm by extracting cultivated land boundaries. The validation experiments were performed using a subset of Chinese Gaofen-2 (GF-2) remote sensing image with a coverage area of 0.12 km2. The results showed the following: (1) The contrast-enhanced image exhibited an obvious gain in terms of improving the image segmentation effect and time efficiency using the improved algorithm. The time efficiency increased by 10.31%, 60.00%, and 40.28%, respectively, in the RGB, Lab, and Luv color spaces. (2) The optimal segmentation and merging scale parameters in the RGB, Lab, and Luv color spaces were C for minimum areas of 2000, 1900, and 2000, and D for a color difference of 1000, 40, and 40. (3) The algorithm improved the time efficiency of cultivated land boundary extraction in the Lab and Luv color spaces by 35.16% and 29.58%, respectively, compared to the RGB color space. The extraction accuracy was compared to the RGB color space using the δA, δP, and Khat, that were improved by 76.92%, 62.01%, and 16.83%, respectively, in the Lab color space, while they were 55.79%, 49.67%, and 13.42% in the Luv color space. (4) Through the visual comparison, time efficiency, and segmentation accuracy, the comprehensive extraction effect using the proposed algorithm was obviously better than that of RGB color-based space algorithm. The established accuracy evaluation indicators were also proven to be consistent with the visual evaluation. (5) The proposed method has a satisfying transferability by a wider test area with a coverage area of 1 km2. In addition, the proposed method, based on the image contrast enhancement, was to perform the region merging in the CIE color space according to the simulated immersion watershed segmentation results. It is a useful attempt for the watershed segmentation algorithm to extract cultivated land boundaries, which provides a reference for enhancing the watershed algorithm.

EXTRACTION OF BIBLIOGRAPHY INFORMATION BASED ON IMAGE OF BOOK COVER

2000 ◽  
Vol 14 (07) ◽  
pp. 963-978 ◽  
Author(s):  
HUA YANG ◽  
MASAAKI KASHIMURA ◽  
NORIKADU ONDA ◽  
SHINJI OZAWA
Keyword(s):  
Color Image ◽  
Color Space ◽  
Input Image ◽  
Color Image Processing ◽  
Front Cover ◽  
Stroke Width ◽  
Other Information ◽  
Region Extraction ◽  
Hsi Color Space ◽  
Rgb Color Space

This paper describes a new system for extracting and classifying bibliography regions from the color image of a book cover. The system consists of three major components: preprocessing, color space segmentation and text region extraction and classification. Preprocessing extracts the edge lines of the book and geometrically corrects and segments the input image, into the parts of front cover, spine and back cover. The same as all color image processing researches, the segmentation of color space is an essential and important step here. Instead of RGB color space, HSI color space is used in this system. The color space is segmented into achromatic and chromatic regions first; and both the achromatic and chromatic regions are segmented further to complete the color space segmentation. Then text region extraction and classification follow. After detecting fundamental features (stroke width and local label width) text regions are determined. By comparing the text regions on front cover with those on spine, all extracted text regions are classified into suitable bibliography categories: author, title, publisher and other information, without applying OCR.

scholarly journals Hue-Preserving Saturation Improvement in RGB Color Cube

2021 ◽  
Vol 7 (8) ◽  
pp. 150
Author(s):  
Kohei Inoue ◽  
Minyao Jiang ◽  
Kenji Hara
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Color Space ◽  
Histogram Equalization ◽  
Experimental Results ◽  
Color Image Enhancement ◽  
Histogram Specification ◽  
Target Intensity ◽  
Rgb Color Space ◽  
Intensity Image

This paper proposes a method for improving saturation in the context of hue-preserving color image enhancement. The proposed method handles colors in an RGB color space, which has the form of a cube, and enhances the contrast of a given image by histogram manipulation, such as histogram equalization and histogram specification, of the intensity image. Then, the color corresponding to a target intensity is determined in a hue-preserving manner, where a gamut problem should be taken into account. We first project any color onto a surface in the RGB color space, which bisects the RGB color cube, to increase the saturation without a gamut problem. Then, we adjust the intensity of the saturation-enhanced color to the target intensity given by the histogram manipulation. The experimental results demonstrate that the proposed method achieves higher saturation than that given by related methods for hue-preserving color image enhancement.

A Method to Enhance the Fog Image Based on Dark Object Subtraction

2014 ◽  
Vol 543-547 ◽  
pp. 2484-2487
Author(s):  
Jing Zhang ◽  
Wei Dong ◽  
Jian Xin Wang ◽  
Xu Ning Liu
Keyword(s):  
Dynamic Range ◽  
Color Space ◽  
Threshold Value ◽  
Entropy Method ◽  
Image Contrast ◽  
Dark Object ◽  
Image Contrast Enhancement ◽  
Degraded Image ◽  
Enhancement Method ◽  
Rgb Color Space

Aiming at the problem of poor image contrast and low visibility, a single image contrast enhancement method is put forward in this paper. The method is based on Dark-object subtraction technique, translating the fog degraded image from RGB color space to YIQ color space, and taking out the Y component. Then using the maximum entropy method to get the threshold value of image segmentation, we can put different portion of the image according to the different formula for image restoration. The processed image must be converted from YIQ color space to RGB color space In the back of the steps. Finally, the image needs a linear dynamic range adjustment to enhance the contrast and brightness. Experiments show that the method can effectively remove haze effect on the image. The dehazing effect of the processed image is obvious. The image becomes clear and bright, and the details is outstanding, which is convenient for observation and analysis.

Sign in / Sign up

Export Citation Format

Share Document