Identification of Turmeric Rhizomes Using Image Processing and Machine Learning †
Abstract
:1. Introduction
2. Related Work
3. Methodology
3.1. Image Acquisition
3.2. Image Preprocessing
3.3. Segmentation
3.4. Feature Extraction
3.4.1. Color
3.4.2. Morphology
- Area: the number of pixels in the region of the rhizome.
- Width: width of a minimum rectangle enclosing the rhizome.
- Height: height of a minimum rectangle enclosing the rhizome.
- Perimeter: total number of boundary pixels.
- Circularity: 4pi(Area)/(Perimeter^2)
- Aspect ratio: Width * Length/Area
3.4.3. Texture
4. Classification
5. Results
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Sahidullah Md Nayan, N.M.; Morshed Md, S.; Hossain, M.M.; Islam, M.U. Date Fruit Classification with Machine Learning and Explainable Artificial Intelligence. Int. J. Comput. Appl. 2023, 184, 1–5. [Google Scholar] [CrossRef]
- Kanase, V.; Khan, F. An Overview of Medicinal Value of Curcuma Species. Asian J. Pharm. Clin. Res. 2018, 11, 40. [Google Scholar] [CrossRef]
- Aarthi, S.; Suresh, J.; Prasath, D. Morphological characterization of Indian turmeric (Curcuma longa L.) Genotypes using DUS descriptor. J. Plant. Crops 2018, 46, 173–179. [Google Scholar]
- Kaur, A.; Saini, N.; Kaur, R.; Das, A. Automatic classification of turmeric rhizomes using the external morphological characteristics. In Proceedings of the 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI), Jaipur, India, 21–24 September 2016; pp. 1507–1510. [Google Scholar] [CrossRef]
- Wan Nurazwin Syazwani, R.; Muhammad Asraf, H.; Megat Syahirul Amin, M.A.; Nur Dalila, K.A. Automated image identification, detection and fruit counting of top-view pineapple crown using machine learning. Alex. Eng. J. 2022, 61, 1265–1276. [Google Scholar] [CrossRef]
- Hassan NM, H.; Ramadan Gamal Elshoky, B.; MMabrouk, A.M. Quality of performance evaluation of ten machine learning algorithms in classifying thirteen types of apple fruits. Indones. J. Electr. Eng. Comput. Sci. 2023, 30, 102. [Google Scholar] [CrossRef]
- Behera, S.K.; Rath, A.K.; Mahapatra, A.; Sethy, P.K. Identification, classification & grading of fruits using machine learning & computer intelligence: A review. J. Ambient Intell. Humaniz. Comput. 2020, 11, 1–11. [Google Scholar] [CrossRef]
- Venkateswaran, D.T.D. Fruits Recognition System Based on Color, Shape, Principal Component and Region Features. Int. J. Res. Anal. Rev. 2019, 6, 226–231. [Google Scholar]
- Saxena, P.; Priya, K.; Goel, S.; Aggarwal, P.K.; Sinha, A.; Jain, P. Rice Varieties Classification using Machine Learning Algorithms. J. Pharm. Negat. Results 2022, 13, 3762–3772. [Google Scholar]
- Yu, F.; Lu, T.; Xue, C. Deep Learning-Based Intelligent Apple Variety Classification System and Model Interpretability Analysis. Foods 2023, 12, 885. [Google Scholar] [CrossRef] [PubMed]
- Aznan, A.; Gonzalez Viejo, C.; Pang, A.; Fuentes, S. Computer Vision and Machine Learning Analysis of Commercial Rice Grains: A Potential Digital Approach for Consumer Perception Studies. Sensors 2021, 21, 6354. [Google Scholar] [CrossRef] [PubMed]
- Mawaddah, S.; Mufid, M.R.; Basofi, A.; Fiyanto, A.; Aditama, D.; Nurlaila, N. Rhizome Image Classification Using Support Vector Machine. In Proceedings of the International Conference on Applied Science and Technology on Social Science 2021 (iCAST-SS 2021), Samarinda, Indonesia, 21–23 October 2022. [Google Scholar] [CrossRef]
- Sarode, K.; Savdekar, R.; Chaudhari, T. Texture feature Analysis of an image using Gray Level Co- Occurrence Matrix. Int. J. Nov. Res. Dev. 2022, 7, 5. [Google Scholar]
- Mohd Ali, M.; Hashim, N.; Abd Aziz, S.; Lasekan, O. Characterisation of Pineapple Cultivars under Different Storage Conditions Using Infrared Thermal Imaging Coupled with Machine Learning Algorithms. Agriculture 2022, 12, 1013. [Google Scholar] [CrossRef]
- Ropelewska, E.; Cai, X.; Zhang, Z.; Sabanci, K.; Aslan, M.F. Benchmarking Machine Learning Approaches to Evaluate the Cultivar Differentiation of Plum (Prunus domestica L.) Kernels. Agriculture 2022, 12, 285. [Google Scholar] [CrossRef]
- Komal, K.; Sonia, D. GLCM Algorithm and SVM Classification Method for Orange Fruit Quality Assessment. Int. J. Eng. Res. 2019, 8, 7. [Google Scholar]
- Jitanan, S.; Chimlek, P. Quality grading of soybean seeds using image analysis. Int. J. Electr. Comput. Eng. IJECE 2019, 9, 3495. [Google Scholar] [CrossRef]
- Arwatchananukul, S.; Saengrayap, R.; Chaiwong, S.; Aunsri, N. Fast and Efficient Cavendish Banana Grade Classification using Random Forest Classifier with Synthetic Minority Oversampling Technique. IAENG Int. J. Comput. Sci. 2022, 49, 46–54. [Google Scholar]
- Kurade, C.; Meenu, M.; Kalra, S.; Miglani, A.; Neelapu, B.C.; Yu, Y.; Ramaswamy, H.S. An Automated Image Processing Module for Quality Evaluation of Milled Rice. Foods 2023, 12, 1273. [Google Scholar] [CrossRef] [PubMed]
- Panigrahi, J.; Pattnaik, P.; Dash, B.B.; Ranjan Dash, S. Rice Quality Prediction using Computer Vision. In Proceedings of the 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA), Gunupur, India, 13–14 March 2020; pp. 1–5. [Google Scholar] [CrossRef]
- Ghazal, S.; Qureshi, W.S.; Khan, U.S.; Iqbal, J.; Rashid, N.; Tiwana, M.I. Analysis of visual features and classifiers for Fruit classification problem. Comput. Electron. Agric. 2021, 187, 106267. [Google Scholar] [CrossRef]
- Garcia JA, A.; Arboleda, E.R.; Galas, E.M. Identification of Visually Similar Vegetable Seeds Using Image Processing and Fuzzy Logic. Int. J. Sci. Technol. Res. 2020, 9, 5. [Google Scholar]
Classifier | Accuracy | ||||
---|---|---|---|---|---|
RGB | LAB | HSI | Texture | Morph | |
KNN | 74.41 | 65.11 | 39.53 | 42.63 | 47.61 |
SVM | 92.24 | 86.82 | 75.19 | 59.68 | 65.07 |
Naïve Bayes | 65.89 | 72.86 | 42.63 | 65.11 | 65.07 |
Random Forest | 70.54 | 62.01 | 53.48 | 57.36 | 57.93 |
LDA | 90.5 | 89.01 | 88.4 | 81.1 | 67.7 |
(a) | |||||
Classifier | Accuracy | ||||
KNN | 77.51 | ||||
SVM | 94.57 | ||||
Naïve Bayes | 79.06 | ||||
Random Forest | 65.89 | ||||
LDA | 94.1 | ||||
(b) |
Classifier | Accuracy for Pitambar and R-Sonia | Accuracy for Phule Swarupa and Kullu Local |
---|---|---|
KNN | 90.32 | 93.16 |
SVM | 96.77 | 93.18 |
NB | 91.93 | 95.45 |
RF | 96.77 | 97.72 |
LDA | 96.9 | 95.9 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Patil, S.; Patil, G. Identification of Turmeric Rhizomes Using Image Processing and Machine Learning. Eng. Proc. 2023, 59, 34. https://doi.org/10.3390/engproc2023059034
Patil S, Patil G. Identification of Turmeric Rhizomes Using Image Processing and Machine Learning. Engineering Proceedings. 2023; 59(1):34. https://doi.org/10.3390/engproc2023059034
Chicago/Turabian StylePatil, Shubhangi, and Gouri Patil. 2023. "Identification of Turmeric Rhizomes Using Image Processing and Machine Learning" Engineering Proceedings 59, no. 1: 34. https://doi.org/10.3390/engproc2023059034