Tomato Ripeness Detection Using Linear Discriminant Analysis Algorithm with CIELAB and HSV Color Spaces
Abstract
Tomatoes have a relatively short ripening period, making it essential to identify their ripeness level before distribution. The ripeness level of tomatoes can be detected based on their color. Therefore, the color of tomatoes serves as a crucial indicator in determining whether they are ripe and of good quality. However, classifying tomato ripeness levels manually has several drawbacks, namely requiring a long process, a low level of accuracy, and being inconsistent. The research aimed at developing a detection model for the ripeness level of tomatoes using the LDA algorithm based on color feature extraction, namely CIELAB (L*a*b) and HSV. The L*a*b and HSV color spaces are applied to obtain information about the color of the object being detected. Furthermore, the information obtained from feature extraction is then grouped by class using the LDA algorithm, which separates information for each class and limits the spread between classes through linear projection searches to maximize the covariance matrix between classes so that members within the class can be identified. This research produces a model that can detect the level of ripeness of tomatoes with an accuracy of 88.194%.
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References
E. R. Dougherty, Digital Image Processing Methods. New York: CRC Press, 2020.
V. C. S. Rao, S. Venkratamulu, and P. Sammulal, Digital Image Processing and Applications. Singapore: Horizon Books (A Division of Ignited Minds Edutech P Ltd), 2021.
R. I. Borman, F. Rossi, Y. Jusman, A. A. A. Rahni, S. D. Putra, and A. Herdiansah, “Identification of Herbal Leaf Types Based on Their Image Using First Order Feature Extraction and Multiclass SVM Algorithm,” in International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), 2021, pp. 12–17.
P. Skolik, C. L. M. Morais, F. L. Martin, and M. R. Mcainsh, “Determination of developmental and ripening stages of whole tomato fruit using portable infrared spectroscopy and Chemometrics,” BMC Plant Biol., vol. 19, no. 236, pp. 1–15, 2019.
D. Ayuningtyas, E. Suryani, and W. Wiharto, “Identification of Tomato Maturity Based on HIS Color Space Using The K-Nearest Neighbour Method,” in International Conference on Artificial Intelligence and Computer Science Technology (ICAICST), 2021, pp. 73–78. doi: 10.1109/ICAICST53116.2021.9497843.
M. C. Untoro, M. Praseptiawan, M. Widianingsih, I. F. Ashari, A. Afriansyah, and O. Oktafianto, “Evaluation of Decision Tree, K-NN, Naive Bayes and SVM with MWMOTE on UCI Dataset,” in ICComSET, 2019, pp. 1–8. doi: 10.1088/1742-6596/1477/3/032005.
M. B. Garcia, S. Ambat, and R. T. Adao, “Tomayto , Tomahto: A Machine Learning Approach for Tomato Ripening Stage Identification Using Pixel-Based Color Image Classification,” 2019.
S. Y. Chaganti, I. Nanda, K. R. Pandi, T. G. N. R. S. N. Prudhvith, and N. Kumar, “Image Classification using SVM and CNN,” in International Conference on Computer Science, Engineering and Applications (ICCSEA), 2020, pp. 1–5. doi: 10.1109/ICCSEA49143.2020.9132851.
S. Sanjaya, “Application of Learning Vector Quantization in Classifying Levels of Ripeness of Tomatoes Based on Fruit Color,” CoreIT, vol. 5, no. 2, pp. 49–55, 2019.
C. Deepika, R. P. Gnanamalar, K. Thangaraj, N. Revathy, and A. Karthikeyan, “Linear discriminant analysis of grain quality traits in rice (Oryza sativa L.) using the digital imaging technique,” J. Cereal Sci., vol. 109, p. 103609, 2023, doi: 10.1016/j.jcs.2022.103609.
S. Li, H. Zhang, R. Ma, J. Zhou, J. Wen, and B. Zhang, “Linear discriminant analysis with generalized kernel constraint for robust image classification,” Pattern Recognit., vol. 136, p. 109196, 2023, doi: 10.1016/j.patcog.2022.109196.
R. Nuraini, “Identification of Freshwater Fish Types Using Linear Discriminant Analysis (LDA) Algorithm,” IJICS (International J. Informatics Comput. Sci., vol. 6, no. 3, pp. 147–154, 2022, doi: 10.30865/ijics.v6i3.5565.
R. I. Borman, Y. Fernando, and Y. E. P. Yudoutomo, “Identification of Vehicle Types Using Learning Vector Quantization Algorithm with Morphological Features,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 158, pp. 339–345, 2022.
J. Michael, “Six Ripening Stages Of Tomatoes By The Fruit Surface Color,” FAGS: Organic Farming and Gardening School, 2020.
A. Mulyanto, E. Susanti, F. Rossi, W. Wajiran, and R. I. Borman, “Penerapan Convolutional Neural Network (CNN) pada Pengenalan Aksara Lampung Berbasis Optical Character Recognition (OCR),” JEPIN (Jurnal Edukasi dan Penelit. Inform., vol. 7, no. 1, pp. 52–57, 2021.
A. Mulyanto, W. Jatmiko, P. Mursanto, P. Prasetyawan, and R. I. Borman, “A New Indonesian Traffic Obstacle Dataset and Performance Evaluation of YOLOv4 for ADAS,” J. ICT Res. Appl., vol. 14, no. 3, pp. 286–298, 2021.
A. Mulyanto, R. I. Borman, P. Prasetyawana, and A. Sumarudin, “2d lidar and camera fusion for object detection and object distance measurement of ADAS using robotic operating system (ROS),” Int. J. Informatics Vis., vol. 4, no. 4, pp. 231–236, 2020, doi: 10.30630/joiv.4.4.466.
A. Gholamy, V. Kreinovich, and O. Kosheleva, “Why 70/30 or 80/20 Relation Between Training and Testing Sets : A Pedagogical Explanation,” Sch. UTEP, vol. 2, pp. 1–6, 2018.
J. Pardede, M. G. Husada, A. N. Hermana, and S. A. Rumapea, “Fruit Ripeness Based on RGB , HSV, HSL, L*a*b* Color Feature Using SVM,” in International Conference of Computer Science and Information Technology (ICoSNIKOM), 2019.
I. Ahmad, Y. Rahmanto, R. I. Borman, F. Rossi, Y. Jusman, and A. D. Alexander, “Identification of Pineapple Disease Based on Image Using Neural Network Self-Organizing Map (SOM) Model,” in International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), 2022.
H. Mayatopani, R. I. Borman, W. T. Atmojo, and A. Arisantoso, “Classification of Vehicle Types Using Backpropagation Neural Networks with Metric and Ecentricity Parameters,” J. Ris. Inform., vol. 4, no. 1, pp. 65–70, 2021, doi: 10.34288/jri.v4i1.293.
R. I. Borman, R. Napianto, N. Nugroho, D. Pasha, Y. Rahmanto, and Y. E. P. Yudoutomo, “Implementation of PCA and KNN Algorithms in the Classification of Indonesian Medicinal Plants,” in ICOMITEE 2021, 2021, pp. 46–50.
U. Erkut, F. Bostancıoğlu, M. Erten, A. M. Özbayoğlu, and E. Solak, “HSV Color Histogram Based Image Retrieval with Background Elimination,” in International Informatics and Software Engineering Conference (UBMYK), 2019.
A. Nguyen et al., “A computationally efficient crack detection approach based on deep learning assisted by stockwell transform and linear discriminant analysis,” Structures, vol. 45, pp. 1962–1970, 2022, doi: 10.1016/j.istruc.2022.09.107.
F. Hariadi and R. R. H. Enda, “Face Detection Using Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) Methods,” JOINCS (Journal Informatics, Network, Comput. Sci., vol. 2, no. 1, pp. 1–4, 2019.
Z. Abidin, R. I. Borman, F. B. Ananda, P. Prasetyawan, F. Rossi, and Y. Jusman, “Classification of Indonesian Traditional Snacks Based on Image Using Convolutional Neural Network (CNN) Algorithm,” in International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), 2022, pp. 18–23.
R. I. Borman, F. Rossi, D. Alamsyah, R. Nuraini, and Y. Jusman, “Classification of Medicinal Wild Plants Using Radial Basis Function Neural Network with Least Mean Square,” in International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), 2022.
R. I. Borman, Y. Fernando, and Y. Egi Pratama Yudoutomo, “Identification of Vehicle Types Using Learning Vector Quantization Algorithm with Morphological Features,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 6, no. 2, pp. 339–345, 2022, doi: 10.29207/resti.v6i2.3954.
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