Machine Vision Technique Based Smart Fruit Sorter

References

[1] Chandra Sekhar Nandi, Bipan Tudu and Chiranjib Koley, “A Machine Vision Technique for Grading of Harvested Mangoes based on Maturity and Quality”, IEEE Sensor Journal,

[2] ‘M.Stefania, D.Marco, M.Rossano, C.Giovanni and R.Damiano,“A Spectroscopy-Based Approach Nondestructive Maturity Grading of Peach Fruits” IEE Sensors Journel, vol.15, no.10,pp.5455-5464, Oct.2015.

[3] C.McCarthy, “Practical Application of Machine Vision in Australian Agriculture Research at NCEA”, IEEE RAS TC on Agricultural Robotics and Automation Webinar, March 28, 2014, Brisbane, Australia.

[4] L.Wang, X.Tian, A.Li, and H.Li, “Machine vision Application in Application Food Logistics” IEEE Sixth Int. Conf. on Business Intelligence and Financial Engg. (BIFE), 14-16 Nov. 2013 pp.125-129.

[5] K K Patel, A. Kar, S.N.Jha and M.A.Khan, “Machine Vision System: A tool for quality inspection of food and agriculture products”, J. of Food Sci. and Technol. 2012 April 49(2), pp.123-141.

[6] A.P.S.Chauhan and A.P.Singh, “Intelligent Estimate for Assessing Apple Fruit Quality”, Int.J. ofComp.Appl.vol.60, no.5, Dec.2012.

[7] H.Zheng and H.Lu, “A least-squares support vector machine (LS-SVM) based on fractal analysis and CIELab parameters for the detection of browing degree on mango (Mangiferaindica.L),” J.Comp. and Electr. Agri., vol.83, 2012, pp.47-51.

[8] Yan Cui, Liya Fan, “Feature extraction using fuzzy maxi-mum margin criterion”, Neurocomputing, 86(2012): 52-58.

[9] D.J.Lee, J.K.Archibald, and Guangming Xiong. “Rapid color grading for fruit quality evaluation using direct color mapping”, IEEE Trans. Autom. Sci. Eng., vol.8(2), 2011, pp.292-302.

[10] X. Liming and Z.Yancho, “Automated Straberry grading system based on Image Processing”, Comp. and Elec. In Agri., 71S (2010), S32-S39.

@article{1701066,
      author = {Mr.A.Gnana Selvakumar, S.Aathisha, S.Dharani, N.Revathi},
      title = {Machine Vision Technique Based Smart Fruit Sorter},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {2},
      number = {9},
      pages = {166-168},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1701066.pdf},
      abstract = {In large scale food production industry, the grading of fruits take place a major role. In this paper automatic grading of artificial mango is done according to artificial ripening and natural ripening. In terms of texture, shape, size. The proposal scheme work based on Machine Learning technique for grading of mangoes in two different categories, they are natural ripening and artificial ripening. In this system images captured by Raspberry pi V2 camera. Several processing techniques are applied to collect features that required for grading of mangoes. For grading prediction, we are using Convolution Neural Network (CNN) algorithm in Machine Learning technique. The proposed system for grading of mango fruit is nearly 92%. Moreover, the repeatability of the proposed system is found to be 100%.},
      keywords = {CNN algorithm, Machine learning,Raspberry pi},
      month = {March},
  }