Shape and texture based classification of citrus using principal component analysis

Naeem Akhtar, Muhammad Idrees, Furqan ur Rehman, Muhammad Ilyas, Qaiser Abbas, Muhammad Luqman


Citrus family consists of a variety of eatable, consumable and usable items with varying nutritional contents. Naked eye citrus classification needs expert human effort, which provides poor decision reliability. The unreliable classification decision may be extremely hazardous when the citrus is being classified for exports or usage in pharmacy products and various food items. In this paper, citrus fruit has been classified on shape and texture features. Principal Component Analysis (PCA) was used as a methodology to explore statistical findings. The average accuracy of the system proposed is 84%. This system can be implemented on pharmacy stores, food production units, or industries, and citrus export centers for reliable citrus fruit classification.


Texture; Region of Interest (ROI) Contrast; Correlation; Image Processing (IP); Bitmap Image (BMP)


Abbas, Q., M. M. Iqbal, S. Niazi, M. Noureen, M. S. Ahmad, M. Nisa and M. K. Malik. 2018. Mango classification using texture & shape features. International Journal of Computer Science and Network Security, 18: 132-38.

Albregtsen, F. 2008. Statistical texture measures computed from gray level coocurrence matrices. Image processing laboratory, department of informatics, university of oslo, 5: 01-14.

Arakeri, M. P. and Lakshmana. 2016. Computer Vision Based Fruit Grading System for Quality Evaluation of Tomato in Agriculture industry. Procedia Computer Science, 79: 426-33.

Arlimatti, S. R. 2012. Window based method for automatic classification of apple fruit. International Journal of Engineering Research and Applications, 2: 1010-13.

Ashraf, S., G. A. Khan, S. Ali and M. Iftikhar. 2015. Socio-economic determinants of the awareness and adoption of citrus production practices in Pakistan. Ciência Rural, 45: 1701-06.

Ashraf, S., R. Saqib, Z. Y. Hassan, M. Luqman and A. Rehman. 2020. Analysis of Intermediaries’ Influence in Citrus Supply Chain in Pakistan. Sarhad Journal of Agriculture, 36: 210-16.

Bhanuprakash, C., P. GK, A. G. Karegowda and C. Ramesh. 2016. Texture Based Flower Species Classification Using Neural Network. International Journal of Advance Foundation and Research in Computer, 3: 2348-4853.

Bhargava, A. and A. Bansal. 2021. Classification and Grading of Multiple Varieties of Apple Fruit. Food Analytical Methods.

Capizzi, G., G. L. Sciuto, C. Napoli, E. Tramontana and M. Woźniak. 2015. Automatic Classification of Fruit Defects based on Co-Occurrence Matrix and Neural Networks Proceedings of the 2015 Federated Conference on Computer Science and Information Systems. IEEE.

Christodoulou, C. I., S. C. Michaelides and C. S. Pattichis. 2003. Multifeature texture analysis for the classification of clouds in satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 41: 2662-68.

Dubey, S. R. and A. S. Jalal. 2015. Apple disease classification using color, texture and shape features from images. Signal, Image and Video Processing, 10: 819-26.

Golzarian, M. R. and R. A. Frick. 2011. Classification of images of wheat, ryegrass and brome grass species at early growth stages using principal component analysis. Plant methods, 7: 28-28.

Jhawar, J. 2015. Grading/sorting of lemons by applying pattern recognition techniques on color images. International Journal of Advanced Computational Engineering and Networking, 3: 26-29.

Khan, F. U., N. Khan and F. Anjum. 2016. Farmers perception about yield losses of kinnow (Citrus reticulate) during its harvesting and post harvesting operations: A case study of tehsil Sargodha, Pakistan. Journal of Pure and Applied Agriculture, 1: 12-19.

Khojastehnazh, M., M. Omid and A. Tabatabaeefar. 2010. Development of a lemon sorting system based on color and size. African Journal of Plant Science, 4: 122-27.

Kumar, C., S. Chauhan, R. N. Alla and H. Mounica gurram. 2015. Classifications of citrus fruit using image processing -GLCM parameters 2015 International Conference on Communications and Signal Processing (ICCSP). IEEE.

Marcene, B. O. 2021. Nutrition Facts and Health Benefits. [Online]. Available: [Accessed 21 January 2021].

Milind, P. and C. Dev. 2012. Orange: range of benefits. International Research Journal of Pharmacy 3: 59-63.

Moallem, P., A. Serajoddin and H. Pourghassem. 2017. Computer vision-based apple grading for golden delicious apples based on surface features. Information Processing in Agriculture, 4: 33-40.

Mohanaiah, P., P. Sathyanarayana and L. GuruKumar. 2013. Image texture feature extraction using GLCM approach. International journal of scientific and research publications, 3: 1-5.

Nandi, C. S., B. Tudu and C. Koley. 2016. A Machine Vision Technique for Grading of Harvested Mangoes Based on Maturity and Quality. IEEE Sensors Journal, 16: 6387-96.

Raut, M. A., M. M. A. Patil, M. C. P. Dhondrikar and M. S. D. Kamble. 2016. Texture Parameters Extraction of Satellite Image. IJSTE-International Journal of Science Technology & Engineering, 2: 13-18.

Sabrol, H. and K. Satish. 2016. Tomato plant disease classification in digital images using classification tree 2016 International Conference on Communication and Signal Processing (ICCSP). IEEE.

Sandoval, Z., F. Prieto and J. Betancur. 2010. Digital Image Processing for Classification of Coffee Cherries 2010 IEEE Electronics, Robotics and Automotive Mechanics Conference. IEEE.

Sharif, M., U. Farooq and W. Malik. 2005. Citrus Marketing in Punjab: Constraints and Potential for Improvement. The Pakistan Development Review, 44: 673-94.

Szczypiński, P. M., M. Strzelecki, A. Materka and A. Klepaczko. 2009. MaZda – The Software Package for Textural Analysis of Biomedical Images Advances in Soft Computing. Springer Berlin Heidelberg. pp. 73-84.

Tahir, A. 2014. Forecasting citrus exports in Pakistan. Pakistan Journal of Agricultural Research, 27: 64-68.

Thendral, R. and A. Suhasini. 2017. Automated Skin Defect Identification System for Orange Fruit Grading Based on Genetic Algorithm. Current Science, 112: 1704.

Uyun, S., S. Hartati and A. Harjoko. 2013. Selection Mammogram Texture Descriptors Based on Statistics Properties Backpropagation Structure. International Journal of Computer Science and Information Security (IJCSIS) 11: 1-5.

Wen, Z.-y., L.-m. Shen, H.-p. Jing and K. Fang. 2010. Color and shape grading of citrus fruit based on machine vision with fractal dimension 2010 3rd International Congress on Image and Signal Processing. IEEE.

Xinlu, L. 2001. The past, present, and future of China's citrus industry. Online available at

Full Text: PDF

DOI: 10.33687/ijae.009.02.3525


  • There are currently no refbacks.

Copyright (c) 2021 Naeem Akhtar, Muhammad Idrees, Furqan ur Rehman, Muhammad Ilyas, Qaisar Abbas, Muhammad Luqman

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.