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An analysis of the performance of Artificial Neural Network technique for apple classification

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Abstract

The purpose of this paper is to develop Artificial Neural Network (ANN)-based apple classifier. Testing effort is calculated using ANN method. The complete system is divided into two modules. In the first module, input (surface level apple quality parameter) from the different sources is collected by the software developed in Visual Basic through different input device like web camera, weight machine, etc. In the second module, the input data are used by ANN simulator to classify the apple according to their quality. The final result of an ANN model for apple classification is discussed; however, the modeling results showed that there is excellent agreement between the experimental data and predicted values. A low level of error prediction confirmed the fact that the Neural Network model is an effective instrument of the apple quality estimation. There is not any misclassification during testing. The paper presents alternative method for quality assessment of apple and provides consumers with a safer food supply.

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References

  • Alchanatis V, Peleg K, Ziv M (1993) Classification of tissue culture segments by colour machine vision. JAER 55:299–311

    Article  Google Scholar 

  • Bishop CM (1995) Neural Networks for Pattern Recognition. Chapter 7, Oxford University Press, Oxford, pp 253–294. http://nguyendangbinh.org/LyThuyetNhanDang/NeuralNetworksforPatternRecognition.pdf

  • Dickson MA, Bausch WC, Howarth MS (1994) Classification of a broadleaf weed, a grassy weed, and corn using image processing techniques. SPIE 2345:297–305

    Article  Google Scholar 

  • German S, Bienenstock E, Doursat R (1992) Neural networks and the bias/variance dilemma. Neural Comput 4:1–58

    Article  Google Scholar 

  • Growe TG, Delwiche MJ (1996) A system for fruit defect detection in real-time. AGENG 96, Paper No. 96F-023

  • Guyer DE, Miles GE, Gaultney LD, Schereiber MM (1993) Application of machine vision to shape analysis in leaf and plant identification. TASAE 36(1):163–171

    Google Scholar 

  • Mathworks Online (2009) Available: http://www.mathworks.com

  • McCulloch W, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133

    Article  MATH  MathSciNet  Google Scholar 

  • Miller BK, Delwiche MJ (1991) Peach defect detection with machine vision. TASAE 34(6):2588–2597

    Google Scholar 

  • Moller MF (1993) A scaled conjugate gradient algorithm for fast supervised learning. Neural Netw 6:525–533

    Article  Google Scholar 

  • Moltó E, Aleixos N, Ruiz LA, Vázquez J, Juste F (1996) An artificial vision system for fruit quality assessment. AGENG 96, Madrid, Paper No. 96F-078

  • Okamura NK, Delwiche MJ, Thompson JF (1991) Raising grading by machine vision. ASAE Paper No. 91-7011. St. Joseph, MI

  • Ruiz LA, Moltó E, Juste F, Aleixos N (1995) Aplicación de métodos ópticos para la inspección automática de productos hortofrutícolas. VI Congreso de la Sociedad Española de Ciencias Hortícolas, 25–28 de Abril de 1995, Barcelona

  • Sarkar N, Wolfe RR (1985) Feature extraction techniques for sorting tomatoes by computer vision. TASAE 28(3):970–974

    Google Scholar 

  • Suen JP, Eheart JW (2003) Evaluation of neural networks for modeling nitrate concentration in rivers. J Water Resour Plan Manag 129(6):505–510

    Article  Google Scholar 

  • Tao Y, Morrow CT, Heinemann PH, Sommer JH (1990) Automated machine vision inspection of potatoes. ASAE Paper No. 90-3531, St. Joseph, MI

  • Varghese ZY, Morrow CT, Heinemann PH, Sommer III, JH, Tao Y, Crassweller RM (1991) Automated inspection of golden delicious apples using color computer vision. ASAE Paper No. 91-7002

  • Ye Q, Liang B, Li Y (2005) Amnestic Neural Network for classification: application on StockTrend Prediction 0-7803-8971-9/05/2005 IEEE

  • Zakaria Z, Isa NAM, Suandi SA (2010) A study on neural network training algorithm for multiface detection in static images. World Acad Sci Eng Technol 38:170–173

    Google Scholar 

  • Zweiri YH, Whidborne JF, Sceviratne LD (2002) A three-term backpropagation algorithm. Neurocomputing 50:305–318

    Article  Google Scholar 

Download references

Acknowledgments

This work is fully supported by Department of Science and Technology (science and society division), Government of India under the scheme for Young Scientist and Professional.

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Authors and Affiliations

  1. Department of Computer Science, Birla Institute of Applied Sciences, Bhimtal, Nainital District, Uttarakhand, India

    Ashutosh Kumar Bhatt

  2. Department of Computer Science, Uttarakhand Open University, Dehradun, Uttarakhand, India

    Durgesh Pant

  3. Birla Institute of Applied Sciences, Bhimtal, Nainital, Uttarakhand, India

    Richa Singh

Authors
  1. Ashutosh Kumar Bhatt
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  2. Durgesh Pant
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  3. Richa Singh
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Correspondence to Ashutosh Kumar Bhatt.

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Bhatt, A.K., Pant, D. & Singh, R. An analysis of the performance of Artificial Neural Network technique for apple classification. AI & Soc 29, 103–111 (2014). https://doi.org/10.1007/s00146-012-0425-z

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  • DOI: https://doi.org/10.1007/s00146-012-0425-z

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