ISSN ONLINE(2320-9801) PRINT (2320-9798)

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Chetna V. Maheshwari
Asst. Professor, Dept. of EC., G. H. Patel College of Engg. and Tech, Vallabh Vidyanagar, Gujarat, India
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Food quality is complex, being determined by the combination of sensory, nutritive, hygienictoxicological, and technological properties. The paper presents a solution for the problem of quality evaluation and category of Rice in an agricultural industry via computer vision, image analysis. This paper proposes parametric superiority method for quality assessment which is non-destructive and cost-effective technique. This paper also provides one automated method for counting the number of Oryza sativa L (rice seeds) from the foreign elements with a high degree of quality and then quantify the same by introducing Q-curves for quantification and assessment of the rice seeds. There is a high degree of quality achieved using computer vision as compared to human vision inspection.


Computer vision; Quality; Image processing; Oryza sativa L; ISEF edge detection; Combined measurements.


In this hi-tech uprising, an agricultural industry has become more intellectual and automatic machinery has replaced the human efforts [1]. In India to overcome the need of ever-increasing population it is necessary to make advancement in agricultural industry. Due to automation need of high quality and safety standards achieved with accurate, fast and cost effective quality determination of agricultural products[15]. Quality control is of major importance in the food industry because after harvesting, based on quality parameter a food product has been sorted and graded in different grades. Traditionally quality of food product is defined from its physical and chemical properties by human sensory panel which is time consuming, may be varying results and costly[14].
Machine vision is one of the important advanced technological field where significant developments have been made[6,10].Machine vision attempts to impersonate sensory perception of human beings viz. vision, touch, smell, taste, hearing etc [1]. Efforts are being geared towards the replacement of traditional human sensory panel with automated systems, as human operations are inconsistent and less efficient [8]. Scientists have successfully endowed computers with machine vision by digital cameras and machines.[4] Extreme research is in progress all over the country on application of electronic eye and nose in food, beverage and agricultural industry [20].Many industries have come up with the same but its quite costly.
Oryza Sativa L(Rice) is a vital worldwide agriculture product. It is one of the leading food crops of the world as more than half of the world’s population relies on rice as the major daily source of calories and protein[8]. Rice (Oryza sativa L) is cultivated in several countries such as India, China, Indonesia, Bangladesh and Thailand which are considered as the major producers. India is the world’s 2nd largest producer and consumer country of rice for a very long time.
This paper presents a solution to the problem faced by Indian Rice industry. Section 2 discusses the Particular problem of quality evaluation of Masoori Rice seed (Oryza sativa L). Section 3 talks about the materials and methods proposed for calculating parameters for the quality of rice seeds (Oryza sativa L).The proposed system and proposed algorithm for computing Rice seed (Oryza sativa L)with long seed as well as small seed being present in the sample is also discussed in the same section. Section 4 discusses the quantification for the quality of rice seeds based on image processing and analysis. Section 5 discusses results based on quality analysis. Section 6 provides the conclusion of the proposed process.


In agricultural industry quality assessment of product is main problem. Nowadays, the quality of grain seed has been determined manually through a visual inspection by experienced technicians. So it requires high degree of accuracy to satisfy customer need of high level of quality as well as correctness for a non-destructive quality evaluation method which is proposed based on image processing [4,12].
Masoori rice(Oryza sativa L) seed contains foreign elements in terms of long as well as small seed. As shown in Figure 1 the red colored circles are long and yellow colored ones are too small.These seeds are having very much importance in quantifying quality. At the time of processing these seeds are removed. Proper removal of this seed is necessary if it is not so then it creates degradation in quality of rice seed. This paper proposes a new method for counting the number of Masoori rice (Oryza sativa L) seeds with these foreign elements using computer vision non destructive technique based on combined measurement techniques to quantify the quality of Masoori rice(Oryza sativa L) seeds.


In this section we discuss the proposed algorithms. Here we have used different varietal samples of Masoori rice. we define quality based on the combined measurement technique. we use area, major axis length, minor axis length and eccentricity of rice seed for counting the number of Masoori rice (Oryza sativa L) seeds with long seeds, normal seeds as well as small seeds.

System Description and Operating Procedure

A schematic diagram of the proposed system is in Figure 2.In our proposed system there is a camera which is mounted on the top of the box at point 1. The camera is having 12 mega pixels quality with 8X optical zoom. After capturing images of rice seed by camera is stored for further processing. To evade problem of illuminance and for good quality of image, we used two lights at point 2 and 3. We can also use butter paper for uniform distribution of light on the tray. In our system box contains opening which can be seen at point 4. At point 5 which is a tray in which rice seeds will be inserted for capturing an image[16].
The simplicity of operation of system can be concluded from the operating procedure detailed in Table 1.

Proposed algorithm to detect rice seeds with long and small seeds:

According to our proposed algorithm as in Table 2, first capture image of sample spread on the black or butter paper using camera. This image is color image so we convert it in to gray scale image as the color information is not of importance. The identification of objects within an image is a very difficult task. One way to make straightforward the problem is to use optimal edge detector, ISEF, for extracting edges of gray scale image. This phase identifies individual object boundaries and marks the centre of each object for further processing. Thresholding is used to convert the segmented image to a binary image. The output binary image has values of 1 (White areas) for all pixels and 0 (black) for all other pixels.

ISEF Edge Detection

The edge can be detected by any of template based edge detector but Shen-Castan Infinite symmetric exponential filter based edge detector is an optimal edge detector like canny edge detector which gives optimal filtered image[18]. First the whole image will be filtered by the recursive ISEF filter in X direction and in Y direction. Then the Laplacian image can be approximated by subtracting the filtered image from the original image. For thinning purpose apply non maxima suppression as it is used in canny for false zero crossing. The gradient at the edge pixel is either a maximum or a minimum. Now gradient applied image has been thinned, and the problem of Streaking can be eliminated by thresholding with Hysteresis. Finally thinning is applied to make edge of single pixel. The ISEF algorithm is given inTable3.
In Figure 3(a) normal rice seed of good quality is shown, while Figure 3(b) and Figure 3(c) contains an image of a long seed and small seed. After applying the Edge detection operation, we get images of Figures 4(a) ,(b) and (c) respectively.

Parameter Calculation

Here we are extracting four parameters area, major axis ,minor axis length and eccentricity for differentiating normal rice seed from long seed as well as small seed.
“The area (A) of any object in an image is defined by the total number of pixels enclosed by the boundary of the object.”
“The major axis length ( N) of an image is defined as the length (in pixels) of the major axis of the ellipse that has the same normalized second central moments as the region.”
“ The minor axis length (M) of an image is defined as the length (in pixels) of the minor axis of the ellipse that has the same normalized second central moments as the region.”
“The eccentricity is the ratio of the distance between the foci of the ellipse and its major axis length. The value is between 0 and 1.”
For Area calculation, we define area of a normal seed is A, area of long seed is B and area of small seed is C. Area A is having a normally less value then area B and area C is having a less value than area A. Use of Vernier caliper for quality evaluation by human inspector can be replaced by Major axis, Minor axis calculation. For eccentricity calculation a long seed is having bigger value than the normal seed and small seed.
Diagram for histogram for area, Major axis length ,Minor axis length and Eccentricity calculation computed from sample is shown in Figure 5 (a) -to-(d).


Classification of Rice Seeds can be done based on assessment of parameters like Area, Major axis, Minor axis and eccentricity. The original image is shown in figure 6 and gray scale image is shown in figure7.Image after performing edge detection operation is in Figure 8.
Table 4 shows intended parameters value for twenty seeds of one of the sample of Masoori Rice out of ten samples based on histogram for normal seeds, ling seeds and small seeds respectively. Table 5 and Table 6 shows calculated parameters value and percentagewise calculated parameters value based on histograms.
We compare the results with ground truth data. Table 7 shows values calculated based on Human Sensory Evaluation Panel for normal seeds, Long seeds and Small seeds of various sample. Table 8 shows the percentagewise calculated value based on Human Sensory Evaluation Panel for the same.


For finding out the number of normal rice seeds, long rice seeds and small rice seeds we compute thresholds values using the histograms of Figure 5 (a) -to-(d) for area, minor axis length, major-axis length and eccentricity as mention in Table 7.
The quality Q, of Rice seeds is not having any particular formula for quantification and assessment. The quality factor Q depends on foreign materials. If these parameters increase percentage wise in the bulk of Rice seeds then as a result the quality Q would decrease. Hence the quality factor Q is inversely proportional to Rice seed with foreign materials. So the Q factor can be defined as
Assuming C to be 100, Q Table can be prepared defining various Q values on the basis of two parameters x1 and x2. For instance if one consider x1 to be 11% and x2 to be 1% the quality factor Q would be 9.09. This particular instance predicts the grade of Rice seeds to be ”A” as seen from the Figure 8. The top most ellipse represents Grade A normal seeds, the red color filled area represents grade B which comprise of higher percentage of long seeds .The bottom most encircled area is grade C for small seeds.


This paper presents a quality analysis of Masoori rice seeds via image analysis. We are calculating area, major axis length, minor axis length and eccentricity for counting normal seed and foreign element in terms of long as well as small seed for a given sample. This paper illustrates new method which is non-destructive for quality analysis. Traditionally quality evaluation and assessment is done by human sensory panel which is time consuming, may be variation in results and costly. For quality analysis more parameters can be calculated to make more accurate results.


The author expresses their gratitude to Mr.H.N.Shah the owner of Shri Krishna Rice and Pulse Mill, Borsad for the system model as per the requirements desired by the image processing group.

Tables at a glance

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Table 1 Table 2 Table 3 Table 4 Table 5
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Table 6 Table 7 Table 8 Table 9

Figures at a glance

Figure 1 Figure 2 Figure 3 Figure 4 Figure 5
Figure 1 Figure 2 Figure 3 Figure 4 Figure 5
Figure 6 Figure 7 Figure 8 Figure 9
Figure 6 Figure 7 Figure 8 Figure 9