Classification and Diseases Identification of Mango Based on Artificial Intelligence: A Review
DOI:
https://doi.org/10.29304/jqcm.2022.14.4.1085Keywords:
Mango Classification, Mango Diseases Identification, Shape features, Texture Features, Color featuresAbstract
Mango is a drupe fruit which plays an active role in the economy of different countries. Classification process is a fundamental process in: diseases detection domain, sorting and grading. Previously, farmers can detect mango's diseases, identification ripe and unripe mango by their eyes, but it is inaccurate, waste of time and effort. AI technology helping farmers get high quality agricultural crops, the essential idea of AI in agriculture is its flexibility, reliability, speedy performance and applicability. AI technology improves enterprise performance and productivity by automating processes or tasks that once required human skill. AI can also understand data on a scale that no human can achieve, this ability can bring great advantages in the field of agriculture. In this paper, a review for application of artificial intelligence in mango classification and mango diseases identification have been presented.
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References
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[30] Philippe Borianne, Julien Sarron, Frédéric Borne, Émile Faye, Deep Mangoes: from fruit detection to cultivar identification in colour images of mango trees, DISP'19 International Conference on Digital Image and Signal Processing, (2019), pp. 1-8, DOI:
https://doi.org/10.48550/arXiv.1909.10939
[31] A. S. A. Mettleq, I. M. Dheir and A. A. Elsharif, Classification of Mango Using Deep Learning, International Journal of Academic Engineering Research (IJAER), (2019), vol. 3, pp.22-30.
[32] N. T. Thinh, N. D. Thong and H. T. Cong, Sorting and Classification of Mangoes based on Artificial Intelligence, International Journal of Machine Learning and Computing, (2020), vol.10, pp. 374-380, http://doi:10.18178/ijmlc.2020.10.2.945.
[33] V. Bhole and A. Kumar, Mango Quality Grading using Deep Learning Technique: Perspectives from Agriculture and Food Industry, Association of Computing Machinery SIGITE’20, (2020), pp. 180-186, https://doi.org/10.1145/3368308.3415370.
[34] S.Naik, B. Talati and S. Naik, Quality Inspection and Classification of Mangoes using Color and Size Features, International Journal of Computer Applications, (2014), vol. 98, pp.1-5, http://DOI: 10.5120/17144-7161.
[35] S. Girish, K J. Shetty, D. Krithi, M. K. Kalkur and A. Megha, Mango Classification for Agro-based Industries using Transfer Learning Technique, International Journal for Research in Applied Science & Engineering Technology (IJRASET), (2020), vol.8, pp.1042-1047.
[36] M. K. Tripathi and D. D. Maktedar, Optimized deep learning model for mango grading: Hybridizing lion plus firefly algorithm, IET Image Processing, (2021), vol. 15, pp.1940–1956, http://DOI: 10.1049/ipr2.12163.
[37] D. Worasawate, P. Sakunasinha, and S. Chiangga, "Automatic Classification of the Ripeness Stage of Mango Fruit Using a Machine Learning Approach", Agri Engineering, (2022), vol. 4, pp.32–47, https://doi.org/10.3390/agriengineering4010003.
[38] H. M. R. Iqbal and A.Hakim, "Classification and Grading of Harvested Mangoes Using Convolutional Neural Network", International Journal of Fruit Science, (2022), vol. 22, pp.95–109, https://doi.org/10.1080/15538362.2021.2023069.
[2] D. Yadav and S. Singh, Mango: History origin and distribution, Journal of Pharmacognosy and Phytochemistry, (2017), vol. 6, pp.1257-1262.
[3] W.M. Haggag, Mango diseases in Egypt, Agriculture and Biology Journal of North America, (2010), vol. 1, pp.285-289.
[4] N. Sutrodhor, M. R. Hussein, M. M. Firoz, P. Karmokar and T. Nur, Mango Leaf Ailment Detection using Neural Network Ensemble and Support Vector Machine, International Journal of Computer Applications, (2018), vol.181, pp.31– 36.
[5] M. S. Aboalarbe and A. Adl, Detection of mango leaf spots and mealybugs diseases using deep learning ConvNet” and LinearSVC, International Pre-Conference Workshop on Microbial Ecology & Eco Systems, Egypt. June 2018.
[6] S. R. Kumar and S. Sowrirajan, Automatic Leaf Disease Detection and Classification using Hybrid Features and Supervised Classifier, International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, (2016), vol. 5, pp.4556-4563.
[7] S. S. Veling, R. S. Kalelkar, L.V. Ajgaonkar, N. V. Mestry and N.N. Gawade, Mango Disease Detection by using Image Processing, International Journal for Research in Applied Science & Engineering Technology (IJRASET), (2019), vol.7, pp.3717-3726.
[8] M. K. Priyadharshini, R. Sivakami and M. Janani, Sooty Mould Mango Disease Identification Using Deep Learning, International Journal of Innovative Technology and Exploring Engineering (IJITEE), (2019), vol.8, pp. 11-16.
[9] S. Dhameliya, J. Kakadiya and R. Savant, Volume Estimation of Mango. International Journal of Computer Applications, (2016), vol. 143.
[10] B. Prakash and A. Yerpude, Identification of Mango Leaf Disease and Control Prediction using Image Processing and Neural Network, International Journal for Scientific Research & Development (IJSRD), (2015), vol. 3, pp.794-799.
[11] R. Elqassas and S. S. Abu-Naser, Expert System for the Diagnosis of Mango Diseases. International Journal of Academic Engineering Research (IJAER), (2018), vol. 2, pp.10-18.
[12] J. Sethupathy and S. Veni, OpenCV Based Disease Identification of Mango Leaves, International Journal of Engineering and Technology (IJET), (2016), vol. 8, pp.1990-1998.
[13] D. P. Hughes and M. Salathe, an open access repository of images on plant health to enable the development of mobile disease diagnostics, [Online]. Available: https://doi.org/10.48550/arXiv.1511.08060.
[14] S. B. Ullagaddi and S.V. Raju, An Enhanced Feature Extraction Technique for Diagnosis of Pathological Problems in Mango Crop, I.J. Image Graphics and Signal Processing, (2017), vol. 9, pp.28-39.
[15] S. Gulavnai and R. Patil. Deep Learning for Image Based Mango Leaf Disease Detection, International Journal of Recent Technology and Engineering (IJRTE), (2019), vol. 8, pp.54-56.
[16] S. B. Ullagaddi and S. V. Raju, Disease Recognition in Mango Crop Using Modified Rotational Kernel Transform Features, International Conference on Advanced Computing and Communication Systems (ICACCS), Coimbatore, INDIA, Jan. 06 – 07, 2017.
[17] A. S. Nadarajan and A. Thamizharasi, Detection of Bacterial Canker Disease in Mango Using Image Processing, IOSR Journal of Computer Engineering (IOSR-JCE), (2017), vol.1, pp.901-08.
[18] V. Ashok and D.S. Vinod, A Comparative Study of Feature Extraction Methods in Defect Classification of Mangoes using Neural Network, 2nd International Conference on Cognitive Computing and Information Processing (CCIP), India, 2016.
[19] I. Maqbool, S. Qadri, D. M. Khan and M. Fahad, Identification of Mango Leaves by Using Artificial Intelligence, International Journal of Natural and Engineering Sciences, (2015), vol. 9, pp.45-53.
[20] L. Dutta and T. K. Basu, Extraction and Optimization of Leaves Images of Mango Trees and Classification Using Ann, International Journal of Recent Advances in Engineering & Technology, (2013), vol. 1, pp.46-51.
[21] Q. Abbas, M. M. Iqbal, S. Niazi, M. Noureen, M. S. Ahmad, M. Nisa and M. K. Malik, Mango Classification Using Texture & Shape Features, IJCSNS International Journal of Computer Science and Network Security, (2018), vol.18, pp.132-138.
[22] S. K. Behera, S. Sangita, A. K. Rath and P. K. Sethy, Automatic Classification of Mango Using Statistical Feature and SVM, Advances in Computer Communication and Control, (2019), vol. 1, pp.469-475.
[23] E. H. Yossy, J. Pranata, T. Wijaya, H. Hermawan and W. Budiharto, Mango Fruit Sortation System using Neural Network and Computer Vision, 2nd International Conference on Computer Science and Computational Intelligence ICCSCI, Indonesia, 2017.
[24] M. Chhabra, A Gupta, P. Mehrotra and S. Reel, Automated Detection of Fully and Partially Riped Mango by Machine Vision, Proceedings of the International Conference on SocProS, 2012.
[25] H. Zheng and H. Lu, “A least-squares support vector machine (LS-SVM) based on fractal analysis and CIELab parameters for the detection of browning degree on mango (Mangifera indica L.)”, J. Comp. and Electr, (2012), vol. 83, pp.47-51.
[26] C. S. Nandi, B Tudu and C. Koley, A Machine Vision-Based Maturity Prediction System for Sorting of Harvested Mangoes, IEEE transactions on instrumentation and measurement, (2014), vol. 63, pp.1722-1730.
[27] T. U. G. Jr, Size Properties of Mangoes using Image Analysis, International Journal of Bio-Science and Bio-Technology, (2014), vol. 6, pp.31-42.
[28] L. Agilandeeswari, M. Prabukumar and S. Goel, Automatic Grading System for Mangoes Using Multiclass Svm Classifier, International Journal of Pure and Applied Mathematics, (2017), vol. 116, pp.515-523.
[29] A. B. Alejandro, J. P. Gonzales and J. P. C. Yap, Grading and sorting of Carabao mangoes using probabilistic neural network, AIP Conference Proceedings, (2018), vol. 2045, pp.1-6, https://doi.org/10.1063/1.5080878.
[30] Philippe Borianne, Julien Sarron, Frédéric Borne, Émile Faye, Deep Mangoes: from fruit detection to cultivar identification in colour images of mango trees, DISP'19 International Conference on Digital Image and Signal Processing, (2019), pp. 1-8, DOI:
https://doi.org/10.48550/arXiv.1909.10939
[31] A. S. A. Mettleq, I. M. Dheir and A. A. Elsharif, Classification of Mango Using Deep Learning, International Journal of Academic Engineering Research (IJAER), (2019), vol. 3, pp.22-30.
[32] N. T. Thinh, N. D. Thong and H. T. Cong, Sorting and Classification of Mangoes based on Artificial Intelligence, International Journal of Machine Learning and Computing, (2020), vol.10, pp. 374-380, http://doi:10.18178/ijmlc.2020.10.2.945.
[33] V. Bhole and A. Kumar, Mango Quality Grading using Deep Learning Technique: Perspectives from Agriculture and Food Industry, Association of Computing Machinery SIGITE’20, (2020), pp. 180-186, https://doi.org/10.1145/3368308.3415370.
[34] S.Naik, B. Talati and S. Naik, Quality Inspection and Classification of Mangoes using Color and Size Features, International Journal of Computer Applications, (2014), vol. 98, pp.1-5, http://DOI: 10.5120/17144-7161.
[35] S. Girish, K J. Shetty, D. Krithi, M. K. Kalkur and A. Megha, Mango Classification for Agro-based Industries using Transfer Learning Technique, International Journal for Research in Applied Science & Engineering Technology (IJRASET), (2020), vol.8, pp.1042-1047.
[36] M. K. Tripathi and D. D. Maktedar, Optimized deep learning model for mango grading: Hybridizing lion plus firefly algorithm, IET Image Processing, (2021), vol. 15, pp.1940–1956, http://DOI: 10.1049/ipr2.12163.
[37] D. Worasawate, P. Sakunasinha, and S. Chiangga, "Automatic Classification of the Ripeness Stage of Mango Fruit Using a Machine Learning Approach", Agri Engineering, (2022), vol. 4, pp.32–47, https://doi.org/10.3390/agriengineering4010003.
[38] H. M. R. Iqbal and A.Hakim, "Classification and Grading of Harvested Mangoes Using Convolutional Neural Network", International Journal of Fruit Science, (2022), vol. 22, pp.95–109, https://doi.org/10.1080/15538362.2021.2023069.
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Published
2022-12-02
How to Cite
Hassoon, I. M. (2022). Classification and Diseases Identification of Mango Based on Artificial Intelligence: A Review. Journal of Al-Qadisiyah for Computer Science and Mathematics, 14(4), Comp Page 39–52. https://doi.org/10.29304/jqcm.2022.14.4.1085
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