Open Access Open Access  Restricted Access Subscription or Fee Access

An Application of Software based Fruit Sorting Machine for Fruit Diseases Classification

A. Chabi-Olaye, Fred A. Gray, Edwin Carlinet

Abstract


The development of agriculture is essential and should be propositional to the population to fulfil the demand. Also, India is one of a major country that exports many agriculture products so it is important that the quality of agricultural commodities must be sustained until it reaches to the end user. The government of India has launched many fruitful and beneficiary schemes to enhance the economic condition of farmers, but due to unawareness, only a few are able to take advantage of such scheme and able to employ this scheme for smart farming. Apple fruit is the most common fruit tree in the home garden with a suitable environment. The quality of the fruit is measured by the health of the tree which yields the fruit. Though Apple ever wanes fruit despite the season, it is highly porn to diseases that are spread through either fungi or bacteria. This paper covers the survey of many papers closely related to computer vision in the agricultural field. The evaluation found that computer vision plays an important role and has a large potential to address the challenges related to the agricultural fields.


Keywords


Image Processing, Segmentation, Sensitivity, Specificity.

Full Text:

PDF

References


Abhijeet V. Jamdar, 2Prof. A. P. Patil,(2017), ‘Apple Fruit Disease Detection using Image Segmentation Algorithm’, IJRTI ,Volume 2, Issue 6 ,2017.

Can, H. Shen, J.N. Turner, H.L. Tanenbaum, B. Roysam, “Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms”, IEEE Trans. on IT in Biomed., Vol. 3, No. 2, pp. 125–138, 1999.

Unay, D., &Gosselin, B. (2005, September).Thresholding-based segmentation and apple grading by machine vision. In 2005 13th European Signal Processing Conference (pp. 1-4). IEEE.

Raj, S., &Vinod, D. S. (2016, August).Automatic defect identification and grading system for ‘Jona gold’ apples. In 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) (pp. 1-5). IEEE.

Song, X., & Yang, L. (2015, December). The study of adaptive multi threshold segmentation method for apple fruit based on the fractal characteristics. In 2015 8th International Symposium on Computational Intelligence and Design (ISCID) (Vol. 2, pp. 168-171).IEEE.

Cheng HD, Lui YM, Freimanis RI. A novel approach to microcalcifications detection using fuzzy logic technique. IEEE Trans Med Imaging 1998; 17:442–450.

Manousakas N, Undrill PE, Cameron GG, et al. Split-and-merge segmentation of magnetic resonance medical images: performance evaluation and extension to three dimensions. Comp Biomed Res 1998; 31: 393–412.

Udupa K, Samarasekera S. Fuzzy connectedness and object definition: theory, algorithms and applications in image segmentation. Graph Models Image Process 1996; 58:246–261.

Javeria Amin, Muhammad Sharif, and Mussarat Yasmin, “A Review on Recent Developments for Detection of Diabetic Retinopathy”, Hindawi’s Scientifica, Vol. 2016, Article ID 6838976, 20 pages, 2016.

Schalkoff J. Pattern recognition: statistical, structural and neural approach. New York: Wiley & Sons, 1992.

Zijdenbos AP, Dawant BM. Brain segmentation and white matter lesion detection in MR images. Crit Rev Biomed Eng 1994; 22:401–465.

Pham D. L., Xu C., and Prince J. L., A Survey of Current Methods in Medical Image Segmentation. Annual Review of Biomedical Engineering, 1998.

Xu R., and Wunsch D. Survey of Clustering Algorithms. IEEE Transactions on Neural Networks, vol. 16, no. 3, May 2005.

Engr. V. C. Chijinduet.al., Medical Image Segmentation Methodologies – A Classified Overview, African Journal of Computing & ICT. ISSN 2006-1781, Vol 5. No. 5, Sept 2012.

M. G Mendonça and A. Campilho, “Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction”, IEEE Trans. Med. Imag, Vol. 147, pp. 1200-1213, 2006.

H.R. Tavakolli, H.R. Pourreza, “An Enhanced Retinal Vessel Detection Algorithm”, Springer Innovations and Adv. Techniques in Systems, Computing Sci. & Software Engg., pp. 6-8, 2008.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.