International Journal of Image, Graphics and Signal Processing (IJIGSP)
IJIGSP Vol. 15, No. 4, 8 Aug. 2023
Cover page and Table of Contents: PDF (size: 721KB)
Building a Medium Scale Dataset for Non-destructive Disease Classification in Mango Fruits Using Machine Learning and Deep Learning Models
Full Text (PDF, 721KB), PP.83-95
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Author(s)
Index Terms
Dataset, Non-destructive, Discriminant Function Analysis, Support Vector Machine, Convolutional Neural Network.
Abstract
The growing quality and safety concern about fresh agricultural produce among consumers have led to the development of non-destructive quality assessment and testing techniques of fruits and vegetables. Humans judge the quality of fruits based on sensory attributes like taste, aroma etc. The shape, size, color, presence of defects which are external to fruits also influence the degree of consumer acceptability of produce. The traditional time consuming, manual fruit quality inspection is replaced by automated, fast, consistent, non-destructive techniques using computer vision in combination with learning algorithms. But the lack of benchmark datasets for agricultural produce has made an objective comparison of the proposed methods difficult. Hence, the proposed work aims to build a medium scale dataset for mango fruits of “Alphonso” cultivar with three classes: chilling injury, defective and non-defective. The reliability of the proposed dataset consisting of 2279 color images of mango fruits with 736 samples in chilling injury class, 632 samples in defective class and 911 samples in non-defective class, was established using a novel approach of developing a predictive model based on discriminant function analysis (DFA) which assigns group membership to each sample of the dataset. Extensive benchmarking analysis is established on the validated dataset using statistical and deep learning algorithms like support vector machine (SVM) and convolutional neural network (CNN), respectively. SVM achieved significant disease classification accuracy of 95% and 91.52% accuracy was achieved by custom CNN. The results of the proposed work indicate that the proposed color image dataset of mango fruits can be used as a benchmark dataset by other researchers for objective comparison in quality evaluation of mango fruits.
Cite This Paper
Vani Ashok, Bharathi R K, Palaiahnakote Shivakumara, "Building a Medium Scale Dataset for Non-destructive Disease Classification in Mango Fruits Using Machine Learning and Deep Learning Models", International Journal of Image, Graphics and Signal Processing(IJIGSP), Vol.15, No.4, pp. 83-95, 2023. DOI:10.5815/ijigsp.2023.04.07
Reference
[1]Pace B M, Cefola D P, Cavallo and Attolico G, Automatic procedure to contactless and non-destructive quality evaluation of fruits and vegetables through a computer vision system, in VI International Symposium on Applications of Modelling as an Innovative Technology in the Horticultural Supply Chain Model-IT 1311, pp. 99-106, 2019.
[2]Bhargava, Anuja, and Atul Bansal, Fruits and vegetables quality evaluation using computer vision: A review, Journal of King Saud University-Computer and Information Sciences 33, pp. 243-257, no. 3 2021.
[3]Ali M M, Hashim N, Abd Aziz S, Lasekan O, An overview of non-destructive approaches for quality determination in pineapples, J. of Agricultural and Food Engineer, pp. 1-7, 2020.
[4]Naik S, Patel B, Machine vision based fruit classification and grading-a review, International Journal of Computer Applications, 170(9), pp. 22-34, 2017.
[5]Nturambirwe J F, Opara U L, Machine learning applications to non-destructive defect detection in horticultural products, Biosystems Engineering, pp. 60-83, 2020.
[6]Arakeri M P, Computer vision based fruit grading system for quality evaluation of tomato in agriculture industry, Procedia Computer Science, 79, pp. 426-33, 2016.
[7]Deulkar Shweta S, Sunita S. Barve, An automated tomato quality grading using clustering based support vector machine, 3rd International Conference on Communication and Electronics Systems (ICCES), IEEE, 2018.
[8]Ireri D, Belal E, Okinda C, Makange N, Ji CA, Computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing, Artificial Intelligence in Agriculture, 2, pp. 28-37, 2019.
[9]Pise, Dnyaneshwari, G. D. Upadhye, Grading of harvested mangoes quality and maturity based on machine learning techniques, IEEE International Conference on Smart City and Emerging Technology (ICSCET), pp. 1-6, 2018.
[10]Castro W, Oblitas J, De-La-Torre M, Cotrina C, Bazán K, Avila-George H, Classification of cape gooseberry fruit according to its level of ripeness using machine learning techniques and different color spaces, IEEE Access, 7(27), pp. 389-400, 2019.
[11]Narendra V G, An intelligent computer vision system for vegetables and fruits quality inspection using soft computing techniques, Agricultural Engineering International, CIGR Journal, 21(3), pp. 171-8, 2019.
[12]Kaur R, Kumar M, Juneja M, Data Generation and Fruit Grading Using Different Classifiers, 5, pp. 189-96, 2018.
[13]Zhang X, Yang J, Lin T, Ying Y, Food and agro-product quality evaluation based on spectroscopy and deep learning: A review. Trends in Food Science & Technology, pp. 431-41, 112, 2021.
[14]Pande, Aditi, Mousami Munot, R. Sreeemathy, and R. V. Bakare, An Efficient Approach to Fruit Classification and Grading using Deep Convolutional Neural Network, IEEE 5th International Conference for Convergence in Technology (I2CT), pp. 1-7, 2019.
[15]Ucat R C, Cruz J C, Postharvest Grading Classification of Cavendish Banana Using Deep Learning and Tensorflow, International Symposium on Multimedia and Communication Technology (ISMAC), pp. 1-6, 2019.
[16]Nithya R, Santhi B, Manikandan R, Rahimi M, Gandomi A H, Computer vision system for mango fruit defect detection using deep convolutional neural network, Foods, 11(21):3483, 2022.
[17]Supekar A D, Wakode M. Multi-parameter based mango grading using image processing and machine learning techniques. INFOCOMP Journal of Computer Science, 19, pp. 175-187, no. 2, 2020.
[18]Preface-mango, Post-Harvest Profile of Mango, Government of India, Ministry of Agriculture, (Department of Agriculture & Cooperation), Directorate of Marketing & Inspection, Branch head office, Nagpur, 2013.
[19]Patel K K, Khan M A, Kumar Y, Yadav A K, Novel techniques in post-harvest management of mango—an overview, South Asian J Food Technol Environ 5, pp. 821-835, no. 2, 2019.
[20]Ashok V, Vinod DS, Combining Discriminant Analysis and Neural Networks for Detection of Internal Defects in Mangoes using X-Ray Imaging Technique, International Journal of Innovative Technology and Exploring Engineering, 9(2S), pp. 188-194, 2019.
[21]Bhargavi K, Jyothi S, A survey on threshold based segmentation technique in image processing, International Journal of Innovative Research and Development, 3(12), pp. 234-9, 2014.
[22]Denis D J, SPSS data analysis for univariate, bivariate, and multivariate statistics, John Wiley & Sons, 2018.
[23]Mery D, Lillo I, Loebel H, Riffo V, Soto A, Cipriano A, Aguilera JM, “Automated fish bone detection using X-ray imaging”, Journal of Food Engineering, 105(3), pp. 485-92, 2011.
[24]Humeau-Heurtier A. Texture feature extraction methods: A survey. IEEE access 7, pp. 8975-9000, 2019.
[25]Haralick RM, Shanmugam K, Dinstein IH, Textural features for image classification, IEEE Transactions on systems, man, and cybernetics, pp. 610-21, (6) 1973.
[26]Chellappa R, Bagdazian R, Fourier coding of image boundaries, IEEE Transactions on Pattern Analysis and Machine Intelligence, (1), pp. 102-5, 1984.
[27]Vapnik V, The nature of statistical learning theory, Springer science & business media, 2013.
[28]Mery D, Soto A, Features: the more the better, Proceedings of the 8th conference on signal processing, computational geometry and artificial vision, pp. 46-51, 2008.
[29]Kingma DP, Ba J, Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980, 2014.