International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 4 (2018) Spl. © Research Publications. http://www.ripublication.com

Rice Variety Identification of Western Based on Geometrical and Texture Feature

Prabira Kumar Sethy*1,Ajay Chatterjee1

1 Department Electronics, University,Odisha,India 768019 2Department of Computer Science and Engineering, SUIIT,, India768019

the rice plant requires water, pesticides, Abstract. This paper presented a novel fertilizers and also cultivation time. The approach for identifying six varieties (Asan classification of rice seed involves preparation of Chudi, Bada Kadalia, Babulal, Chit Pagalia, samples, preprocessing, extraction of texture and Radha Jugala and Sahabhagi) of rice grain geometrical feature and finally classification [4]. mostly cultivated in western part of Odisha. The objective of this research is to identify and district of is always in classify rice seed of six variety of western region center of attention for good quality, quantity of Odisha, which makes easy to identify the rice with different varieties of rice production. This seed by the non-expert person. Rice seed is an research classify six variety of rice grain using agricultural object which have a particular geometrical and texture feature with multiclass shape, size, colour and texture. The SVM. This research also includes analysis of classification accuracy of our proposed performance of classification of rice seed with algorithm depends on extracted both texture respect to different texture features and feature and geometrical feature. geometrical features. The geometrical feature Kuo-Yi Huang and Mao-Chien Chien [5] have distinct in nature whereas texture feature approached classifying three variety of rice seed have similarity. By considering both using image processing. They mostly focus on Geometrical and Texture feature the proposed shape feature of rice seed which is needful to methodology achieved 92% of accuracy. BBNN for classification and achieved 95.26 % of accuracy. H.K Mebatison, J. Paliwal and D.S. Keywords: Texture feature, geometrical feature, Jayas [6] established a technique for multiclass SVM, Rice grain classification. classification of cereal grains i.e Barley, Oat and wheat using limited number of morphological and colour features. For classification they not 1. Introduction only consider on the general feature like length, width and area but also consider shape of kernel In Odisha rice is synonymous with food. In in terms of IEFDs. Archana Chaugule and Odisha there are six major rice producing Suresh N. Mali [7] established a methodology districts in western Odisha i.e. Koraput, for classification of four paddy varieties using Subarnapur, Bargarh, Nabrangpur, Malkangiri, texture and shape feature. This study also Rayagada, Sambalpur [1]. In western Odisha, includes evaluation of different features to affect the largest dam of Asia i.e. dam is the performance of classification using ANN and situated which is mainly meant for agriculture give conclusion that for classification the role of [2]. Since it provides good irrigation to texture feature like contrast, energy and Sambalpur and , it is rightly homogeneity are negligible but the shape feature called the rice bowl of Odisha [3]. The most have significant role. Min Zhao et. al [8] practice agriculture crops in this two districts is rice in proposed identification of different variety of both kharif and Rabi cropping seasons. In these corn seed by considering a huge number of two districts mostly six known variety of rice are original feature, colour feature and shape being cultivated, these are Asan Chudi, Bada feature. The genetic algorithm optimized the Kadalia, Babulal, Chit Pagalia, Radha Jugala extracted feature and SVM successfully classify and Sahabhagi. Currently the majority of the the varieties of corn seed. farmers of western Odisha cultivated these six Mousavi Rad et al. [9] compared the varieties of rice because of its demand in both performance of BPNN and SVM for local and foreign market. Choosing the proper classification of five Iranian rice varieties by variety of rice seed is one of the most important using forty one shape features with accuracy of factors for agriculture because as per the variety 96 % and 97% respectively. Tzu-Yi Kuo et. al

35 International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 4 (2018) Spl. © Research India Publications. http://www.ripublication.com

[10] studied to identify thirty varieties of rice collection of sample and by using LDA and grain using image processing and sparse- PCA, the BPNN established four forms representation based classification. In this according to the identified features. Harish S approach for acquisition of image they used Gujjar and M. Siddappa [12] proposed a microscope and digital camera at a resolution of technique for identification Basmati rice in India 95 pixels per millimeter. Because of high according to its quality. This work is based on resolution the image details are observed and image warping and image analysis by quantified and SRC classifier predict the type of eliminating the effect of orientation with proper variety of rice grain with accuracy of 89.1 %. scalling. The BPNN was used as classifier by Lee et al. [11] established a technique to identify considering 9 morphological features and six seven variety of grain kernel using 10 shape colour features. feature and four colour feature. The CCD (charge-coupled device) camera is used for

Asan Chudi Babulal Radha Jugala Bada Kadalia Chit Pagalia Sahabhagi

Fig.1 Different variety of rice seed (a) Aasana Chudi (b) Babulal (c) Radha Jugala (d) Bada Kadalia (e) Chit Pagalia (f) Sahabhagi

2. Materials and Methods

The system overview of the proposed methodology is shown in fig. 2.

Training Image

Texture Geometrical Multiclass

Feature Feature SVM

classification of

Test Image Rice Seed

Texture Geometrical Feature Feature

Fig.2 proposed a system for rice seed classification.

The proposed system has three sections: first, the SVM classifies the rice seed with respect to their geometrical and texture feature is extracted from feature in a different type. collected sample images and train to the multi- class SVM. Secondly, the geometrical and 2.1. Collection of Rice Seed Samples texture feature of the test image is compared The rice seed varieties Asan Chudi, Bada with the trained images. Finally, the multi-class Kadalia, Babulal, Chit Pagalia, Radha Jugala and Sahabhagi. were provided by the District

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Agriculture office, Sambalpur. Good quality approximately similar to human vision and and no breakage rice seeds each of 50 samples perception of lightness. Thus it makes accurate of above six varieties were collected. color balanced by varying a and b components.

2.2. Image capture system 2.4. Feature extraction

The image capturing system developed in this Feature Extraction is mainly comprised of study comprised of a color camera of the 16- two parts i.e Texture Feature and Geometrical Megapixel lens, a photography box , a computer Feature. Here we are extracting 21 numbers of of Intel(R) Core(TM) i3-6006U CPU with 4GB Texture Feature and 15 numbers of Geometrical RAM with MATLAB R2016a. the dimension of Feature of the above six variety of Rice seed. the digital photography box is 30cm*30cm*30cm. it is customized with high 2.4.1. Texture feature brightness LEDs which provides enough brightness for shooting, with one top shooting The gray level co-occurrence matrix (GLCM) window. The power supply given to the LEDs method is a way of extracting second-order through a 9V DC battery. It captured RGB color statistical texture features. To study the effect of images measuring 640 × 480 pixels in the parameters on the classification ability of SVM, bitmap format. The camera was employed for twenty-one texture features concerning four image acquisition and the work distance was 6.0 features and four offsets were used for cm. Image. classifying the rice seeds in six categories. To specify the distance between the pixel of interest 2.3. Image preprocessing and its neighbor, offset (the relationship, of a pair of pixels) is used. The offset is expressed as The image analysis software was developed an angle. Figure3 illustrates the offset values that in Matlab version R2016a. In order to extract specify common angles, given the pixel distance, rice seed features i.e Texture Feature and the distance specified here is 1 from the center Geometrical Feature image preprocessing is pixel. required. Here the captured RGB image is transported to Lab color space. Lab color is

135o 90o 45o [-1 -1] [-1 0] [-1 1] o 0

[0 1]

Fig. 3 Offset Values.

Offset = { [01 ] for (0∘);[−1 1] for (45∘); [−1 0] for (90∘); [−1 − 1] for (135∘)}.

2.4.2. Geometrical feature Geometric descriptors include area, perimeter, major axis length, minor axis length, axis ratio, Feature analysis of grains includes extraction of shape factor, eccentricity, orientation, a a total of 15 geometric features, four shape bounding box. From the value of axis length, factors. perimeter and area, shape factor 1-4 are determined as follows:

Shape factor 1 = Major axis length, Area

Shape factor 2 = Area Major axis length

Shape factor 3 = Area (Major axis length/2)(Major axis length/2)휋

37 International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 4 (2018) Spl. © Research India Publications. http://www.ripublication.com

Shape factor 4 = Area (Major axis length/2)(Minor axis length/2)휋

3. Classification In the proposed algorithm, first twenty-one number of the texture feature, 11 number of the Support vector machines are supervised geometrical feature are extracted. Geometrical learning models with an associated learning feature includes area, perimeter, major axis algorithm that analyzed data used for length, minor axis length, axis ratio, eccentricity, classification. Given a set of training example, orientation, a bounding box. From the value of each marked as belonging to one or the other of axis length, perimeter and area, shape factor 1-4 two categories, module an SVM training are determined. By considering those 32 number algorithm builds a model that assigns a new of features and 4 number of shape factor we can example to one category or the other, making it a classify six variety of rice seeds of western non-probabilistic binary linear classifier. Multi- Odisha. We have used 10 number of six variety class SVM classifies different types of rice seeds of rice seed for training purposes. Again we by considering the texture feature, geometrical experimented 10 number of rice seed of each feature and four number of shape factors. variety for validation, illustrated in table 1. From validation, the proposed methodology is capable 4. Result and Discussion of classifying rice seed with an accuracy of 92%.

Table 1 Rice Seed Classification of six varieties.

Aasana Babulal Bada Chit Radha Sahabhagi Accuracy Chudi Kadalia Pagalia Jugala (%)

Aasana Chudi 9 1 × × × × 90 Babulal × 10 × × × × 100 Bada Kadalia × × 9 1 × × 90 Chit Pagalia × × × 8 × 2 89 Radha Jugala × × × 1 9 × 90 Sahabhagi × × × × × 10 100 Average 92

5. Conclusion SUIIT Sambalpur University for supporting capturing rice seed image samples. In this research we developed a method for classifying six variety () of rice seed of western References Odisha. The 21 number of texture feature, 11 number of geometrical feature and four shape [1] Food Odisha Portal, Food Supplies, and factor were obtained to establish classification Consumer Welfare Department, Govt. of by use of multi-class SVM. The experimental Odisha. result shows that six variety of rice seed of [2] Pranab Chaudhury, Zinda Sandhor, western Odisha can be classified successfully by Priyabrata Satapathy, Forum for Policy use of this method with accuracy of 92 %. Dialogue on Water Conflicts In India, This research may be extended by use of other Odisha State Resource Centre, 2012 refined classification algorithm with more [3] Dr. Taradatt, IAS, Chief Editor, Gazetteers variety of rice seed. & Director General, Training Coordination, Odisha Districts Gazetteers Acknowledgments: The author thanks to [4] B. Lurstwut and C. Pornpanomchai, District Agriculture Office, Sambalpur for “Application of Image Processing And providing non-breakage and good quality rice Computer Vision on Rice Seed Germination seeds. Further author thanks to Paresh Kumar Analysis”, International Journal of Applied Mohanty, Subham Mohapatra of B.Tech CSE, Engineering Research, Vol. 11, pp 6800- 6807, 2016

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[5] Huang, Kuo-Yi; Chien, Mao-Chien. 2017. kernel varieties using optimal "A Novel Method of Identifying Paddy morphological features and an ensemble Seed Varieties." Sensors 17, no. 4: 809. classifier by image processing. Majlesi J. [6] Mebatsion, H.K.; Paliwal, J.; Jayas, Multimedia Process.2012, 1, 1–8. D.S. Automatic classification of non- [10] Kuo, T.Y.; Chung, C.L.; Chen, S.Y.; Lin, touching cereal grains in digital images H.A.; Kuo, Y.F. Identifying rice grains using limited morphological and color using image analysis and sparse- features. Comput. Electron. Agric. 2012, 90, representation-based classification. Comput. 99–105. Electron. Agric. 2016, 127, 716–725. [7] Chaugule and S. Mali, “Seed technological [11] Lee, C.Y.; Yan, L.;Wang, T.F.; Lee, S.R.; development—a survey,” in Proceedings of Park, C.W. Intelligent classification the International Conference on Information methods of grain kernels using computer Technology in Signal and Image vision analysis. Meas. Sci. Technol. 2011, Processing, pp. 71–78, ACEEE, 2013. 22, 64006–64012. [8] Min Zhao, Wenfu Wub, Ya Qiu Zhang and [12] Harish S Gujjar, Dr. M. Siddappa, “A Xing Li, “Combining genetic algorithm and Method for Identification of Basmati Rice SVM for corn variety identification”, 2011 grain of India and Its Quality Using Pattern International Conference on Mechatronic Classification”, International Journal of Science, Electric Engineering and Computer Engineering Research and Applications August 19-22, 2011, Jilin, China. (IJERA) Vol. 3, Issue 1, January -February [9] MousaviRad, S.J.; Rezaee, K.; Nasri, K. A 2013, pp.268-273. new method for identification of Iranian rice

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