Diseased Area Segmentation Using a Novel Gray-Scale Thresholding Algorithm and Classification Using a New Deep CNN Model for Apple Fruit Sorting
DOI:
https://doi.org/10.26438/ijcse/v13i2.3948Keywords:
Thresholding Algorithm, Region of Interest, Classification, Deep Neural Network, Apple Fruit SorterAbstract
A novel Gray-Scale Thresholding Method [GSTM] for segmenting the region of interest and a Deep Convolutional Neural network model for the apple fruit sorting system has been proposed in this paper. First, the GSTM method converts the acquired colour image into a grayscale image and calculates the threshold value using the Gray pixel values. The acquired colour apple image was then segmented using the calculated threshold value to extract the diseased/defective part alone for further processing. Second, a Deep Convolutional Networking model was designed to classify the apple images as sound or diseased/defective apple images. The result obtained using the GSTM was compared with similar Grayscale thresholding methods like Otsu and Kapur. It was found that GSTM’s execution time was less and the visual segmentation was good compared to the other two methods in extracting the diseased/defective area. The Deep Convolutional Network using GSTM segmented images gave a classification/sorting accuracy rate of 91.67%.
References
[1] Mohamed Abd Elaziz, Diego Oliva, Ahmed A Ewees, and Shengwu Xiong. “Multi-level thresholding-based grey scale image segmentation using multi objective multi-verse optimizer”. Expert Systems with Applications, Vol.125, pp.112– 129, 2019.
[2] Nesar Mohammadi Baneh, Hossein Navid, Jalal Kafashan, Hatef Fouladi, and Ursula Gonzales-Barron. “Development and evaluation of a small-scale apple sorting machine equipped with a smart vision system”. Agri Engineering, Vol.5, Issue.1, pp.473–487, 2023.
[3] Ariel E Baya, Monica G Larese, and Rafael Nam’?as. “Clustering stability for automated color image segmentation". Expert Systems with Applications, Vol.86, pp.258–273, 2017.
[4] Santi Kumari Behera, Amiya Kumar Rath, Abhijeet Mahapatra, and Prabira Kumar Sethy. “Identification, classification & grading of fruits using machine learning & computer intelligence: a review.” Journal of Ambient Intelligence and Humanized Computing, pp.1–11, 2020.
[5] Jaime Gallego and Montse Pardas. “Region based foreground segmentation ` combining color and depth sensors via logarithmic opinion pool decision”. Journal of visual communication and image representation, Vol.25, Issue.1, pp.184–194, 2014.
[6] Farid Garcia-Lamont, Jair Cervantes, Asdrubal L ´ opez, and Lisbeth Rodriguez. “Segmentation of images by color features: A survey”. Neurocomputing, Vol.292, pp.1–27, 2018.
[7] A Gongal, M Karkee, and S Amatya. “Apple fruit size estimation using a 3d machine vision system.” Information Processing in Agriculture, Vol.5, Issue.4, pp.498– 503, 2018.
[8] David Ireri, Eisa Belal, Cedric Okinda, Nelson Makange, and Changying Ji. “A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing. Artificial Intelligence in Agriculture, Vol.2, pp.28–37, 2019.
[9] Anis Ben Ishak. “A two-dimensional multilevel Thresholding method for image segmentation.” Applied SoftComputing, Vol.52, pp.306–322, 2017.
[10] Jagat Narain Kapur, Prasanna K Sahoo, and Andrew KC Wong. “A new method for gray-level picture thresholding using the entropy of the histogram.” Computer vision, graphics, and image processing, Vol.29, Issue.3, pp.273–285, 1985.
[11] SA Karthik and SS Manjunath. “An enhanced approach for spot segmentation of microarray images”. Procedia computer science, Vol.132, pp.226–235, 2018.
[12] NS Lele. “Image classification using convolutional neural network”. International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.22–26, 2018.
[13] Leiming Liu, Ning Yang, Jinhui Lan, and Juanjuan Li. “Image segmentation based on gray stretch and threshold algorithm.” Optik, Vol.126, Issue.6, pp.626–629, 2015.
[14] K Suresh Manic, R Krishna Priya, and V Rajinikanth. “Image multithresholding based on kapur/tsallis entropy and firefly algorithm”. Indian Journal of Science and Technology, Vol.9, Issue,12, pp.89949, 2016.
[15] Nobuyuki Otsu. “A threshold selection method from gray-level histograms”. IEEE transactions on systems, man, and cybernetics, Vol.9, Issue.1, pp.62–66, 1979.
[16] Agus Pratondo, Chee-Kong Chui, and Sim Heng Ong. “Integrating machine learning with region-based active contour models in medical image segmentation.” Journal of Visual Communication and Image Representation, Vol.43, pp.1–9, 2017.
[17] Andreas Richtsfeld, Thomas Morwald, Johann Prankl Michael Zillich, and ¨ Markus Vincze. “Learning of perceptual grouping for object segmentation on rgb-d data”. Journal of visual communication and image representation, Vol.25, Issue.1, pp.64–73, 2014.
[18] Chunshi Sha, Jian Hou, and Hongxia Cui. “A robust 2d otsu’s thresholding method in image segmentation”. Journal of Visual Communication and Image Representation, Vol.41, pp.339–351, 2016.
[19] Neha Sharma, Vibhor Jain, and Anju Mishra. “An analysis of convolutional neural networks for image classification”. Procedia computer science, Vol.132, pp. 377–384, 2018.
[20] Thodeti Srikanth, P Pradeep Kumar, and Ashwin Kumar. “Color image segmentation using watershed algorithm”. (IJCSIT) International Journal of Computer Science and Information Technologies, Vol.2, Issue.5, pp.2332–2334, 2011.
[21] Mingyuan Xin and Yong Wang. “Research on image classification model based on deep convolution neural network.” EURASIP Journal on Image and Video Processing, pp.40, 2019.
[22] Aqing Yang, Huasheng Huang, Chan Zheng, Xunmu Zhu, Xiaofan Yang, Pengfei Chen, and Yueju Xue.“High-accuracy image segmentation for lactating sows using a fully convolutional network”. Biosystems Engineering, Vol.176, pp.36–47, 2018.
[23] Junho Yim, Jeongwoo Ju, Heechul Jung, and Junmo Kim. “Image classification using convolutional neural networks with multi-stage feature”. In Robot Intelligence Technology and Applications-3, pp.587–594, 2015.
[24] Emny Harna Yossy, Jhonny Pranata, Tommy Wijaya, Heri Hermawan, and Widodo Budiharto.”Mango fruit sortation system using neural network and computer vision”. Procedia computer science, Vol.116, pp.596–603, 2017.
[25] Guoqiang Peter Zhang. Neural networks for classification: a survey.” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), Vol.30, Issue.4, pp.451–462, 2000.
[26] ZhiYong Zou, Tao Long, Qi Wang, Li Wang, Jie Chen, Bing Zou, and Lijia Xu. “Implementation of apple’s automatic sorting system based on machine learning”. Food Science and Technology, 42:e24922, 2022.
[27] Poornima Devi. M, M. Sornam, "Contour based Character Segmentation and Nguyen-Widrow Weight Generation for Classification of Tamil Palm Leaf Script Characters - Machine Learning Approach", International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.118-123, 2019.
[28] PL.Chithra, M.Henila, "Fruits Classification Using Image Processing Techniques", International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.131-135, 2019.
[29] David Ademola Oyemade, Diseimokumor Favour Seregbe, "A Machine Learning Model for the Classification of Human Emotions," International Journal of Computer Sciences and Engineering, Vol.12, Issue.4, pp.17-23, 2024.
[30] Domo Omatsogunwa Ereku, Vincent I.E. Anireh, Onate Egerton Taylor, "Model For Email Spam Classification Using Hybrid Machine Learning Technique”, International Journal of Computer Sciences and Engineering, Vol.13, Issue.1, pp.24-32, 2025.
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