ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Analysis of Sulphur Content in Copra


Affiliations
1 Department of Electronics and Communication Engineering, Jai Shriram Engineering College, India
     

   Subscribe/Renew Journal


Agriculture is the largest economic sector in India. Coconut is one of the most demanded fruit amongst all. The dried coconut, copra is the main source of coconut oil. Naturally it contains 70% of moisture content and it is dried to about 7% for production of coconut oil. The sulphur is added as preservative which acts as anti-microbial agent for preventing bacteria, fungus etc. Sulphur is a toxic food preservative which restricts lung performance and leads to direct allergenic reactions. The survey of World Health Organisation says that 65% of asthmatic children are sensitive to sulphur and 75% of children exposed to sulphur exhibits changes in their behaviour. The sulphur fumigation over coconut affects human both externally and internally. Fumigation leads to cancer and environmental pollution. In order to prevent this devastating effect, copra is examined using image processing. The proposed idea is to identify the presence and percentage of sulphur region present in copra. The region of interest is segmented by method of superimposition thereby segmenting white layers in copra. The RGB colour features are extracted to differentiate the sulphur added copra from normal copra. The coconut is dried under 60℃ in a tray drier and shapes of copra decreases at regular interval of time are extracted using image processing. The decreasing percentage of shape features are measured to identify the sulphur added in the copra. The k-means clustering technique is used to discriminate the copra at different levels. The segmented patch area is measured to determine the percentage of sulphur present in copra. The percentage of sulphur over copra is divided into three levels (low sulphur added region, medium sulphur added region and high sulphur added region). The K-Nearest Neighbour classification is also used to classify the sulphur added copra at different levels. The proposed algorithm classifies the sulphur added copra at three different levels with 86% accuracy.

Keywords

Fumigation, RGB, k-Means Clustering, KNN.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Adel Bakhshipour, “Vision based Features in Moisture Content Measurement during Raisin Production”, World Applied Sciences Journal, Vol. 7, No. 7, pp. 860-869, 2012.

  • Alok Mishra, “The Quality Identification of Fruits in Image Processing using MATLAB”, International Journal of Research in Engineering and Technology, Vol. 3, No. 10, pp. 123-129, 2014.

  • S. Arivazhagan, “Fruit Recognition using Colour and Texture Features”, Journal of Emerging Trends in Computing and Information Sciences, Vol. 2, No. 2, pp. 90-94, 2010.

  • P. Deepa and S.N. Geethalakshmi, “Comparative Analysis of Feature Extraction Methods for Fruit Grading Classifications”, International Association of Scientific Innovation and Research, Vol. 2, No. 9, pp. 221-225, 2013.

  • Devrim Unay and Bernard Gosselin, “Automatic Grading of Bi-Coloured Apples by Multispectral Machine Vision”, Computers and Electronics in Agriculture, Vol. 75, No. 1, pp. 204-212, 2010.

  • V. Elamaran, “FPGA Implementation of Point Processes using Xilinx System Generator”, Journal of Theoretical and Applied Information Technology, Vol. 41, No. 2, pp. 201-206, 2012.

  • Kiran Wagh, “Quality Inspection and Grading of Mangoes by Computer Vision and Image Analysis”, Journal of Engineering Research and Applications, Vol. 3, No. 5, pp. 1208-1213, 2015.

  • V. Leemansa, “On-line Fruit Grading According to their External Quality using Machine Vision”, Biosystems Engineering, Vol. 83, No. 4, pp. 397-404, 2002.

  • Monika Jhuria, “Image Processing for Smart Farming: Detection of Diseases and Fruit Grading”, Proceedings of 2nd IEEE International Conference on Image Information Processing, pp. 521-526, 2013.

  • Neha. P. Raut, “FPGA Implementation for Image Processing Algorithms Using Xilinx System Generator”, IOSR Journal of VLSI and Signal Processing, Vol. 2, No. 4, pp. 26-36, 2013.

  • U. Pavan Kumar and P. Padmaja, “Image Enhancement Using Adaptive Filtering”, International Journal of Engineering Trends and Technology, Vol. 6, No. 1, pp. 1-4, 2013.

  • Shiv Ram Dubey and A.S. Jalal, “Detection and Classification of Apple Fruit Diseases using Complete Local Binary Patterns”, Proceedings of IEEE International Conference on Computer and Communication Technology, pp. 44-58, 2012.

  • Sima Kumari and Neelamegam, “Analysis of Apple Fruit Diseases using Neural Network”, Research Journal of Pharmaceutical, Biological and Chemical Science, Vol. 2, No. 2, pp. 641-646, 2015.

  • D.F. Specht, “Experience with Adaptive Probabilistic Neural Network and Adaptive General Regression Neural Network”, Proceedings of IEEE International Conference on Neural Networks, pp. 1203-208, 1994.

  • Tomas U. Ganiron, “Size Properties of Mangoes using Image Analysis”, International Journal of Bio-Science and Bio-Technology, Vol. 6, No. 2, pp. 31-42, 2014.

  • Vani Ashok “Automatic Quality Evaluation of Fruit Using Probabilistic Neural Network Approach”, Proceedings of International Conference on Contemporary Computing and Informatics, pp. 308-311, 2014.

  • D. Vilas Sadegaonkar and Kiran H. Wagh, “Improving Quality of Apple using Computer Vision and Image Processing based Grading System”, International Journal of Science and Research, Vol. 4, No. 4, pp. 543-546, 2013.


Abstract Views: 317

PDF Views: 1




  • Analysis of Sulphur Content in Copra

Abstract Views: 317  |  PDF Views: 1

Authors

A. Stephen Sagayaraj
Department of Electronics and Communication Engineering, Jai Shriram Engineering College, India
G. Ramya
Department of Electronics and Communication Engineering, Jai Shriram Engineering College, India
N. Dhanaraj
Department of Electronics and Communication Engineering, Jai Shriram Engineering College, India

Abstract


Agriculture is the largest economic sector in India. Coconut is one of the most demanded fruit amongst all. The dried coconut, copra is the main source of coconut oil. Naturally it contains 70% of moisture content and it is dried to about 7% for production of coconut oil. The sulphur is added as preservative which acts as anti-microbial agent for preventing bacteria, fungus etc. Sulphur is a toxic food preservative which restricts lung performance and leads to direct allergenic reactions. The survey of World Health Organisation says that 65% of asthmatic children are sensitive to sulphur and 75% of children exposed to sulphur exhibits changes in their behaviour. The sulphur fumigation over coconut affects human both externally and internally. Fumigation leads to cancer and environmental pollution. In order to prevent this devastating effect, copra is examined using image processing. The proposed idea is to identify the presence and percentage of sulphur region present in copra. The region of interest is segmented by method of superimposition thereby segmenting white layers in copra. The RGB colour features are extracted to differentiate the sulphur added copra from normal copra. The coconut is dried under 60℃ in a tray drier and shapes of copra decreases at regular interval of time are extracted using image processing. The decreasing percentage of shape features are measured to identify the sulphur added in the copra. The k-means clustering technique is used to discriminate the copra at different levels. The segmented patch area is measured to determine the percentage of sulphur present in copra. The percentage of sulphur over copra is divided into three levels (low sulphur added region, medium sulphur added region and high sulphur added region). The K-Nearest Neighbour classification is also used to classify the sulphur added copra at different levels. The proposed algorithm classifies the sulphur added copra at three different levels with 86% accuracy.

Keywords


Fumigation, RGB, k-Means Clustering, KNN.

References