Abstract
Fruit quality detection is important to maintain the quality of fruits. Generally, fruit inspection is done manually which is ineffectual for farmers in the agriculture industry. This paper proposes an approach to identify fruit defects in the agricultural industry for reducing the production cost and time. Since different fruit images may have similar or identical color and shape values. Therefore, we used a method to increase the accuracy level of fruit quality detection by using color, shape and size-based method with Artificial Neural Network (ANN). ANN is a special kind of tool that is used to estimate the cost and various artificial intelligence field like - voice recognition, image recognition, robotics and much more. The fruit quality inspection acquires images as external input and then the captured image is used for detecting defects by applying segmentation algorithm. The output of the processed image is then used as input to ANN. The network uses backpropagation algorithm, was tested with training dataset and hence predicted defects with good efficiency and in much shorter time than the human inspectors. On comparing predicted and training dataset together, the feasibility of an approach reveals the efficient defect detection and classification in the agricultural industry.
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References
Yogesh, Dubey, A.K.: Fruit defect detection based on speeded up robust feature technique. In: 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), Noida, pp. 590–594 (2016)
Kaur, M., Sharma, R.: Quality detection of fruits by using ANN technique. IOSR J. Electron. Commun. Eng. 10, 35–41 (2015)
Samajpati, B.J., Degadwala, S.D.: A survey on apple fruit diseases detection and classification. Int. J. Comput. Appl. (0975–8887) 130(13) (2015)
Rogers, R.D., Riess, A.G.: Detection and classification of CCD defects with an artificial neural network. Publ. Astron. Soc. Pac. 106(699), 532 (1994)
Jiang, J., Trundle, P., Ren, J.: Medical image analysis with artificial neural networks. Comput. Med. Imaging Gr. 34(8), 617–631 (2010)
Banumathi, P., Nasira, G.M.: Fabric inspection system using artificial neural networks. Int. J. Comput. Eng. Sci. 2, 20–27 (2012)
Singh, S.P., et al.: Classification ofapples using neural networks (2015)
Gill, J., Sandhu, P.S., Singh, T.: A review of automatic fruit classification using soft computing techniques. Int. J. Comput. Sci. Electron. Eng. 2(2), 3–16 (2014)
Xiao-bo, Z., Jie-wen, Z., Yanxiao, L., Holmes, M.: In-line detection of apple defects using three color cameras system. Comput. Electron. Agric. 70(1), 129–134 (2010)
Dubey, S.R., Jalal, A.S.: Application of image processing in fruit and vegetable analysis: a review. J. Intell. Syst. 24(4), 405–424 (2015)
Banot, S., Mahajan, P.M.: A fruit detecting and grading system based on image processing-review. Int. J. Innov. Res. Electr. Electron. Instrum. Control Eng. 4(1), 47–53 (2016)
Mehdy, M.M., et al.: Artificial neural networks in image processing for early detection of breast cancer. Comput. Math. Methods Med. 2017 (2017)
Satyawana, S.: A review paper on image segmentation and object recognition procedures. Sci. Eng. Technol. 67 (2016)
Zhao, W., Zhang, J., Li, P., Li, Y.: Study of image segmentation algorithm based on textural features and neural network. In: International Conference on Intelligent Computing and Cognitive Informatics (ICICCI), pp. 300–303 (2010)
Chandhok, C.: A novel approach to image segmentation using artificial neural networks and K-means clustering. Int. J. Eng. Res. Appl. (IJERA) ISSN 2(3), 274–279 (2012)
Pushpa, B.R., Meghana, T.K., Tripulla, K.H.: Detection and classification of fungal disease in fruits using image processing. Int. J. Appl. Eng. Res. (IJAER) 10(55) (2015)
Raut, S., Fulsunge, A.: Review on fruit disease detection using image processing techniques
Sonka, M., Hlavac, V., Boyle, R.: Image pre-processing. In: Sonka, M., Hlavac, V., Boyle, R. (eds.) Image Processing Analysis and Machine Vision, pp. 56–111. Springer, New York (1993). https://doi.org/10.1007/978-1-4899-3216-7
Jeon, B., Yung, Y., Hong, K.: Image segmentation by unsupervised sparse clustering. Pattern Recognit. Lett. 27, 1650–1664 (2006). science direct
Bhosale, P., Gokhale, A.: Segmentation of color images based on different. Int. J. Electron. Comput. Sci. Eng. 2, 640–644 (2013)
Mohamed Razali, M.R., Ahmad, N.S., Zaki, Z.M., Ismail, W.: Region of adaptive threshold segmentation between mean, median and otsu threshold for dental age assessment. In: 2014 International Conference on Computer, Communications, and Control Technology (I4CT), Langkawi, pp. 353–356 (2014)
Morse, B.S.: 1998–2000 Last modified on Wednesday, 12 January 2000 at 10:00 AM. Brigham Young University
Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. Int. J. Comput. Vis. 30(2), 117–154 (1998)
Nataraj, K., Patnekar, N.: Neural networks for image analysis and processing in measurements, instrumentation and related industrial applications. Neural Netw. Instrum. Meas. Relat. Ind. Appl. 185, 145 (2003)
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Makkar, T., Verma, S., Yogesh, Dubey, A.K. (2018). Analysis and Detection of Fruit Defect Using Neural Network. In: Panda, B., Sharma, S., Roy, N. (eds) Data Science and Analytics. REDSET 2017. Communications in Computer and Information Science, vol 799. Springer, Singapore. https://doi.org/10.1007/978-981-10-8527-7_46
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DOI: https://doi.org/10.1007/978-981-10-8527-7_46
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