Design of Semen Straw Counting System Using LabVIEW

IJSTE - International Journal of Science Technology & Engineering | Volume 2 | Issue 07 | January 2016 ISSN (online): 2349-784X

Design of Semen Straw Counting System using LabVIEW Phadatare Swati M. PG Student Department of Electronics and Telecommunication Vidya Pratishthan’s College of Engineering, Baramati

Shastri Rajveer K. Assistant Professor Department of Electronics and Telecommunication Vidya Pratishthan’s College of Engineering, Baramati

Shastri Aparna R. Assistant Professor Department of Electronics and Telecommunication Vidya Pratishthan’s College of Engineering. Baramati

Abstract The object counting and object detection are the necessary tasks in the field such as agriculture industries, manufacturing industry, biological sciences. Various techniques are discovered and still developing for counting purpose. The proposed algorithm is to count of the semen straws that held in a compact bunch by using image processing techniques. These straws are kept in the liquid nitrogen for the straw content preservation purpose. Safety requirements of the semen straws as well as workers working in the liquid nitrogen environment, a non-harming, short-term process for straw counting is required. So, the best possible way for straw counting is to use image processing. For counting straw, operations such as thresholding, segmentation, morphological processing, and color based segmentation, advanced morphological processing, etc. are useful. The objectives are to count the straws in the container that kept in liquid nitrogen without harming any straw. The counting algorithm evaluated using Vision Builder Automated Inspection, which is supported by LabVIEW. Keywords: Thresholding, Segmentation, Morphological Processing, LabVIEW ________________________________________________________________________________________________________

I. INTRODUCTION The semen straw counting is an essential task in the field of animal husbandry [1]. The semen samples are kept in straws, and this compact bunch of straws is kept in the container. These straws preserved in liquid nitrogen. It evaporates quickly at room temperature rapidly. The atmosphere with an oxygen level below 19.5% causes dizziness, lethargy, nausea, vomiting, excess salivation, diminished mental alertness, and death. The absence of adequate oxygen can bring about genuine sicknesses or death.

Skin contact with liquid nitrogen can cause tissue solidifying, causing serious blazes. Liquid nitrogen is an achromic, inodorous, extremely cold liquid and gas under pressure. In such environment, worker requires Self Contained Breathing Apparatus (SCBA).

The straw counting becomes difficult in such environment [2]- [3].

Straw Counting is one of the basic proposed requirements of the Bharatiya Agro-Industrial Foundation (BAIF), Central Research Station, Urulikanchan, and District-Pune. The semen samples kept in straws, and these straws maintained in a cylindrical container.

These straws are preserved in Liquid Nitrogen. The worker picks up one or two straws in a fork, counts it in mind and rapidly puts in the empty container. The workers require too much concentration in the straw counting process, and he cannot even speak or respond to others during the task. But still, there is doubt in accuracy [2].

Various algorithms are available for object counting when the object is overlapped, not separable, and small in size. Algorithms such as Blob analysis, connected components analysis, statistical area measurements, Minimum area estimation, particle analysis, etc. are useful for object detection and counting [1].

The rest of the paper is organized as follows. Proposed counting algorithm is explained in section II. Experiment and results are presented in section III. Concluding remarks are given in section IV.

II. PROPOSED ALGORITHM Vision Builder AI software is useful for straw counting. Several different digital cameras are used, with different resolutions and features for image acquisition. High-resolution cameras are good to achieve a vast range of data and to give high-quality images.

The block diagram of the straw counting system is shown in Fig.1.

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Design of Semen Straw Counting System using LabVIEW (IJSTE/ Volume 2 / Issue 07 / 036)

Fig. 1: Block Diagram for Straw Counting System

Various segmentation techniques are used either in gray level or color images. Thresholding technique is conventional color image segmentation techniques. Thresholding is used to separate objects from background in the image [2]. Morphological processing refers to certain operations performed on the binary image where an object is hit with a structuring element. By using morphological operation, obtained object is more relevant. The segmented image is used for straw counting because it separate straw area from the background. Detect object and calculator functions are available in LabVIEW for detecting and counting purposes [4].

III. EXPERIMENT AND RESULT Vision Builder Automated Inspection is used to count straws from a container. Vision Builder AI consists of a set of tools for acquiring, analyzing, displaying and storing data. With the help of Vision Builder AI, images can be directly loaded into the program, and various operations can be performed on it. Straw counting algorithm can be built with the Vision Builder configuration window that records every step of the processing algorithm [5].

Fig. 2: State Configuration Window for Counting Algorithm

Fig.2 shows “State Configuration Window” for straw counting. There are following steps for straw counting.

Acquisition: A. First step is to acquire the image. Image acquisition can be done by selecting the acquire images tab and clicking the simulate acquisition step from “Inspection Step” palette. Images with resolution 2048 X 1536 are selected which shown in Fig. 3 (a).

Thresholding: Thresholding is used to separates objects of interest in an image from the rest of the image. The thresholding step in inspection pallet sets all pixels within the threshold range to 255, and other pixels are sets to 0. For segmentation, thresholding function plays an important role. In this method HSL, color space is selected. HSL stands for Hue Saturation Luminance, and it is also called HLS model [5]. Hue is an attribute associated with a dominant wavelength in a mixture of the light wave. Saturation is relative purity of the color. Saturation also refers relative amount of white light mixed with hue [6]-[7].

The upper and lower thresholds are selected by manually by selecting manual threshold option. In additive colors, by combining two primary color hues are formed, and value of hue is adjusted in thresholding option. Preview Color option specifies the color of the overlay to use for pixels that meet the threshold parameters. Hue, Saturation, and Luminance have a range from 0 to 255 in the Vision Builder AI. The output of thresholding showed in Fig. 3(b) [5].

Morphological Operation: Erosion and dilation are two fundamental morphological processing. Morphological functions can improve the information in a binary image by removing unwanted particles from the image e.g. noise particles, particles touching the border of images, particles touching each other, or particles with uneven edges. Erode objects eliminate pixels isolated in the background and erodes the contour of objects based on the structuring element. Erosion makes objects smaller. Here fundamental morphological operation erosion is used regarding the intersection of an image with a translated shape for extracting features from an image. The morphological process of erosion is useful to remove unwanted segments of the image. Erosion causes objects to shrink [4].

In this process structuring element selected with dimensions 3 X 3. Let A and B are two sets in Z then erosion operation of the binary image A by using structuring element B is defined by

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Design of Semen Straw Counting System using LabVIEW (IJSTE/ Volume 2 / Issue 07 / 036)

AӨB = {Z│(B)Z ⊆A } (1) From equation (1) erosion of A by B is set of all points Z in such way that Z translates b is contained in A [6]. Erode object and removing small object operation output is shown in Fig. 3(c). Advanced morphology performs high-level operations on particles in binary images. These functions are useful for tasks such as removing small particles from an image, filling holes in particulate matter and removes small objects from an image. A removing small object is defined by the number of iterations needed to remove the object [5].

(a)

(b)

(c)

(d)

Fig. 3: (a) Original Image (b) Thresholding in HSL Model (c) Output of Morphological Operation (d) Area for One Straw

Detect Objects: For obtaining area for one straw Vision Builder AI provide detect object function. Area for one straw is obtained by specifying the region of the image to process. Threshold tab Specifies the type of objects e.g. bright object, dark object or gray object. By observation of images, a region of one straw is selected. Fig. 3(d) shows the blue color region that is an area of one straw tip. for one straw is equal to the 1598 pix2. Similarly, total white portion area is calculated by detect object function [5].

Calculator for Straw Counting: Straw counting can be achieved by dividing the whole white area of the image by area required to one straw [2]. This count is obtained by using the calculator.

Fig. 4: Calculator Design for Straw Counting

In Fig. 4, detect object 1 is defined the area of a total white portion that is divided by area of one straw. There is one limit tab specifies that the inspection passes when the specified input measurements are found, and the result of the computation connected to the Step result output terminal is True. When we use the conditional block that shows that count is greater than 0, then the step is correct [5]. Count for the yellow straw image is 469, and it displayed on state configuration window.

IV. CONCLUSION Semen straw counting algorithm is developed by using Vision Builder AI. Calculator step provides a total number of straws in the container. This method requires setting of Hue and saturation limit manually, and each color requires different hue and saturation limit. The future scope is to develop the algorithm that will be applicable for all color straw.

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Design of Semen Straw Counting System using LabVIEW (IJSTE/ Volume 2 / Issue 07 / 036)

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