Hybridized approach of image segmentation in classification of fruit mango using BPNN and discriminant analyzer
Tóm tắt
In machine learning, image classification accuracy generally depends on image segmentation and feature extraction methods with the extracted features and its qualities. The main focus of this paper is to determine the defected area of mangoes using image segmentation algorithm for improving the classification accuracy. The Enhanced Fuzzy based K-means clustering algorithm is designed for increasing the efficiency of segmentation. Proposed segmentation method is compared with K-means and Fuzzy C-means clustering methods. The geometric, texture and colour based features are used in the feature extraction. Process of feature selection is done by Maximally Correlated Principal Component Analysis (MCPCA). Finally, in the classification step, severe portions of the affected area are analyzed by Backpropagation Based Discriminant Classifier (BBDC). Proposed classifier is compared with BPNN and Naive Bayes classifiers. The images are classified into three classes in final output like Class A –good quality mango, Class B-average quality mango, and Class C-poor quality mango. Finally, the evaluated results of the proposed model examine various defected and healthy mango images and prove that the proposed method has the highest accuracy when compared with existing methods.
Tài liệu tham khảo
Agarwal A, Sarkar A, Dubey AK (2019) Computer vision based fruit disease detection and classification, Smart innovations in communication and computational sciences. Proceedings of ICSICCS. Springer, Berlin, pp 105–115
Ahmed AH, Islam T, Ema RR (2019) A New Hybrid Intelligent GAACO Algorithm for Automatic Image Segmentation and Plant Leaf or Fruit Diseases Identification Using TSVM Classifier, International Conference on Electrical, Computer and Communication Engineering (ECCE), PP. 1–6
Awate A, Deshmanker D, Amrutkar G, Bagul U, Sonavane S (2015) Fruit disease detection using color ,texture analysis and ANN, International Conference on Green Computing and Internet of Things (ICGIOT), pp. 970–975
Azarmdel H, Jahanbakhshi A, Mohtasebi SS, Muñoz AR (2020) Evaluation of image processing technique as an expert system in mulberry fruit grading based on ripeness level using artificial neural networks (ANNs) and support vector machine (SVM). Postharv Biol Technol 166. https://doi.org/10.1016/j.postharvbio.2020.111201
Behera SK, Sangita S, Rath AK, Sethy PK (2019) Automatic classification of mango using statistical features and SVM, Advances in computer, communication and control. Springer, Berlin, pp 469–475
Bhange M, Hingoliwala H (2015) Smart farming: pomegranate disease detection using image processing. Procedia Comput Sci 58:280–288
Bhatt AK, Pant D (2013) Automatic apple grading model development based on backpropagation neural network & machine vision, and its performance evaluation. AI & Society: Journal of Knowledge, Culture and Communication, pp 45–56
Bhatt AK, Pant D, Singh R (2014) An analysis of the performance of artificial neural network technique for apple classification. Journal AI & Society: Journal of Knowledge, Culture and Communication, imprint by Springer, pp 103–111
Bielza C, Barreiro P, Rodríguez-Galiano MI, Martín J (2003) Logistic regression for simulating damage occurrence on a fruit grading line. Comput Electron Agric 39(2):95–113
Blasco J, Aleixos N, Moltó E (2003) Machine vision system for automatic quality grading of fruit. Biosyst Eng 85(4):415–423
Dhakete M, Ingola A (2015) Diagnosis of pomegranate plant diseases using neural network, fifth national conference on computer vision, pattern recognition, image processing and graphics, pp 1–4
Dorj UO, Lee M, Yun S (2017) An yield estimation in citrus orchards via fruit detection and counting using image processing. Comput Electron Agric 150:103–112
Gavhale KR, Gawande U (2014) An overview of the research on plant leaves disease detection using image processing techniques. IOSR J Comput Eng (IOSR-JCE) 16(1):10–16
Geng Y, Liang RZ, Li W, Wang J, Liang G, Xu C, Wang JY (2016a) Learning convolutional neural network to maximize pos@ top performance measure, ESANN 2017 - Proceedings, pp. 589–594
Gonzalez RC, Woods RE (2002) Digital image processing, 2nd edn. Prentice Hall, New Jersey
Gurubelli Y, Ramanathan M, Ponnusamy P (2019) Fractional fuzzy 2DLDA approach for pomegranate fruit grade classification. Comput Electron Agric 162:95–105
Hung C, Underwood J, Nieto J, Sukkarieh S (2015) A feature learning based approach for automated fruit yield estimation, Filed and service robotics, pp. 485–498
Iqbal Z, Khan MA, Sharif M, Shah JH, Rahman MH, Javed K (2018) An automated detection and classification of citrus plant diseases using image processing techniques: A review. Computers and electronics in agriculture, Computers and electronics in agriculture, pp 12–32
Kondo N (2010) Automation on fruit and vegetable grading system and food traceability. Trends Food Sci Technol 21(3):145–152
Kumari N, Bhatt AK, Dwivedi RK, Belwal R (2018) Physical parameter extraction of fruit mango using image processing in MATLAB. IJERA
Kumari N, Bhatt AK, Dwivedi RK, Belwal R (2019) Performance analysis of support vecor machine in defective and non defective mangoes classification. IJEAT: 1563–1572
Lee D-J, Archibald JK, Xiong G (2011) Rapid Color Grading for Fruit Quality Evaluation Using Direct Color Mapping. IEEE Transact Auto Sci Eng 8(2):292–302
Liang N, Ni F, Zhang K, Tang Y, Hu Y (2018) Optimized installation angle and distance of a grading channel for dried jujube fruit with a push-pull actuating mechanism. Comput Electron Agric 150:134–142
Matteoli S, Diani M, Massai R, Corsini G, Remorini D (2015) A Spectroscopy-Based Approach for Automated Nondestructive Maturity Grading of Peach Fruits. IEEE Sens J 15(10)
Momin MA, Rahman MT, Sultana MS, Igathinathane C, Grift TE (2017) Geometry-based mass grading of mango fruits using image processing. Info Process Agric 4(2):150–160
Nambi VE, Thangavel K, Jesudas DM (2015) Scientific classification of ripening period and development of colour grade chart for Indian mangoes (Mangifera indica L.) using multivariate cluster analysis. Sci Hortic 193:90–98
Nandi CS, Tudu B, Koley C (2016) A Machine Vision Technique for Grading of Harvested Mangoes Based on Maturity and Quality. IEEE Sens J 16(16)
Ohali YA (2011) Computer vision based date fruit grading system: design and implementation. J King Saud Univ - Comp Info Sci 23(1):29–36
Pujari JD, Yakkundimath R, Byadgi AS (2015) Image processing based detection of fungal diseases in plants. Procedia Comput Sci 46:1802–1808
Qiao J, Sasao A, Shibusawa S, Kondo N, Morimoto E (2005) Mapping yield and quality using the mobile fruit grading robot. Biosyst Eng 90(2):135–142
Ronald M, Evans M (2016) Classification of selected apple fruitvarieties using naive Bayes, Indian J Comp Sci Eng pp 13–19
Sa’ad FSA, Ibrahim MF, Shakaff AYM, Zakaria A, Abdullah MZ (2015) Shape and weight grading of mangoes using visible imaging. Comput Electron Agric 115:51–56
Sajjad M, Ullah A, Ahmad J, Abbas N, Rho S, Baik SW (2018) Integrating salient colors with rotational invariant texture features for image representation in retrieval systems. Multimed Tools Appl 77:4769–4789
Sajjad M, Zahir S, Ullah A, Akhtar Z, Muhammad K (2019) Human behavior understanding in big multimedia data using CNN based facial expression recognition. Mobile Netw Appl 25:1611–1621
Vishnu S, Ranjith RA (2015) Plant disease detection using leaf pattern: A review. IJISET 2(6):774–780
Yan Z, Zheng L-J, Nie J-Y, Li Z-X, Cheng Y (2018) Evaluation indices of sour flavor for apple fruit and grading standards. J Integr Agric 17(5):994–1002
Zhang G, Liang G, Li W, Fang J, Wang J, Geng Y, Wang JY (2017) Learning convolution ranking-score function by query preference regularization. In International conference on intelligent data engineering and automated learning (pp. 1-8). Springer, Cham
Zhang G, Liang G, Su F, Qu F, Wang JY (2018) Cross-domain attributes representation based on convolutional neural network. In International Conference on Intelligent Computing (pp. 134-142). Springer, Cham