Open Access Open Access  Restricted Access Subscription Access

Comparison and Approximation of Copra's Sulfur Content

Sneha Sabarwal

Abstract


In the Indian economy, agriculture is a crucial industry. More than 70 % of the rustic families rely upon horticulture. There is 11.1 million of ton of coconut was developed. The dried coconut meat is known as copra and it is a wellspring of coconut oil, which is utilized in tremendous amounts for making fats for baking and candy parlor. The copra is dried under open daylight on the drying yard. The post-handling incorporates the fumigation of copra under sulfur. The Sulfur in overabundance can cause synapse passing, mind harm, visual deficiency. The thought is to distinguishing the presence of sulfur utilizing picture handling methods and contrasting and the continuous assessment. Images of copra are taken both with and without sulphur present. The locale of interest is divided by utilizing multithresh and dynamic shape. Using the GLCM method, features are extracted from the image to distinguish between normal and sulfur copra. The K-NN arrangement is utilized to characterize the sulfur added copra at various levels.


Full Text:

PDF

References


Sagayaraj, A. S., Ramya, G., & Dhanaraj, N. (2018). Analysis of sulphur content in copra. International journal on image and video processing,(ICTACT), 9(02).

Bakhshipour, A., Jafari, A., & Zomorodian, A. (2012). Vision based features in moisture content measurement during raisin production. World Appl. Sci. J, 17(17), 860-869.

Mishra, A., Asthana, P., & Khanna, P. (2014). The quality identification of fruits in image processing using Matlab. International Journal of Research in Engineering and Technology, 3(10), 92-95.

Arivazhagan, S., Shebiah, R. N., Nidhyanandhan, S. S., & Ganesan, L. (2010). Fruit recognition using color and texture features. Journal of Emerging Trends in Computing and Information Sciences, 1(2), 90-94.

Deepa, P., & Geethalakshmi, S. N. (2013). A comparative analysis of feature extraction methods for fruit grading classifications. International journal of emerging technologies in computational and applied sciences (IJETCAS), 4(2), 221-225.

Unay, D., Gosselin, B., Kleynen, O., Leemans, V., Destain, M. F., & Debeir, O. (2011). Automatic grading of Bi-colored apples by multispectral machine vision. Computers and electronics in agriculture, 75(1), 204-212..

Elamaran, V., & Rajkumar, G. (2012). FPGA implementation of point processes using Xilinx system generator. Journal of Theoretical and Applied Information Technology, 41(2), 201-206.


Refbacks

  • There are currently no refbacks.