A COMPARATIVE ANALYSIS OF COLOR SPACES FOR TOMATO RIPENESS CLASSIFICATION USING MACHINE LEARNING AND DEEP LEARNING APPROACHES
Abstract
The classification of tomato ripeness is crucial for post-harvest processing, quality assurance, and agricultural automation, as manual evaluation is often subjective, inconsistent, and time-consuming. This research investigated the impact of color space selection and hyperparameter optimization on tomato ripeness classification using machine learning (SVM, Random Forest, K-NN, GNB) and deep learning (CNN) approaches. Evaluation results indicated that YCbCr was the best-performing color space for classical models, with SVM achieving the highest accuracy (91.24%) and RF following closely (89.54%), whereas HSV yielded optimal performance for CNN (90.46%), highlighting differences in feature extraction mechanisms. Confusion matrix and ROC curve analyses demonstrated that models capturing nonlinear and interdependent color features, such as SVMs and CNNs, achieved superior class separability, particularly for the Ripe and Unripe classes. Dominant channel analysis revealed that chrominance channels, Cb in YCbCr and H in HSV, played a critical role in ripeness discrimination. These findings emphasized the importance of preprocessing for feature selection and provided guidance on selecting appropriate models and color spaces to improve the accuracy and reliability of automated tomato ripeness classification.
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References
T. Khatun, A. Razzak, and S. Islam, “AN EXTENSIVE REAL-WORLD IN FIELD TOMATO IMAGE DATASET INVOLVING MATURITY CLASSIFICATION AND RECOGNITION OF FRESH AND DEFECT TOMATOES,” Data Br., vol. 51, p. 109688, 2023, doi: https://doi.org/10.1016/j.dib.2023.109688
R. Shinoda, H. Kataoka, K. Hara, and R. Noguchi, “SMART AGRICULTURAL TECHNOLOGY TRANSFORMER-BASED RIPENESS SEGMENTATION FOR TOMATOES,” Smart Agric. Technol., vol. 4, no. February, p. 100196, 2023, doi: https://doi.org/10.1016/j.atech.2023.100196 .
M. Islam, S. Bijjahalli, T. Fahey, and A. Gardi, DESTRUCTIVE AND NON ‑ DESTRUCTIVE MEASUREMENT APPROACHES AND THE APPLICATION OF AI MODELS IN PRECISION AGRICULTURE : a review, vol. 25, no. 3. Springer US, 2024.doi: https://doi.org/10.1007/s11119-024-10112-5
M. Yazdani, D. Bao, J. Zhou, A. Wang, and R. D. van Klinken, “SINGLE-WAVELENGTH NEAR-INFRARED IMAGING AND MACHINE LEARNING FOR DETECTING QUEENSLAND FRUIT FLY DAMAGE IN CHERRIES,” Smart Agric. Technol., vol. 12, p. 101090, 2025, doi: https://doi.org/10.1016/j.atech.2025.101090.
R. Khodabakhshian and M. H. Abbaspour-Fard, “PATTERN RECOGNITION-BASED RAMAN SPECTROSCOPY FOR NON-DESTRUCTIVE DETECTION OF POMEGRANATES DURING MATURITY,” Spectrochim. Acta Part A Mol. Biomol. Spectrosc., vol. 231, p. 118127, 2020, doi: https://doi.org/10.1016/j.saa.2020.118127.
Q. Wang et al., “IN SITU NONDESTRUCTIVE IDENTIFICATION OF CITRUS FRUIT RIPENESS VIA HYPERSPECTRAL IMAGING TECHNOLOGY,” Plant Methods, 2025, doi: https://doi.org/10.1186/s13007-025-01354-z
S. Sikder, M. S. Islam, M. Islam, and S. Reza, “MACHINE LEARNING WITH APPLICATIONS IMPROVING MANGO RIPENESS GRADING ACCURACY : A COMPREHENSIVE ANALYSIS OF DEEP LEARNING , TRADITIONAL MACHINE LEARNING , AND TRANSFER LEARNING TECHNIQUES,” Mach. Learn. with Appl., vol. 19, no. May 2024, p. 100619, 2025, doi: https://doi.org/10.1016/j.mlwa.2025.100619
O.-R. Rusu et al., “InteRACTION EFFECTS OF CULTIVARS AND NUTRITION ON QUALITY AND YIELD OF TOMATO,” Horticulturae, vol. 9, no. 5, 2023, doi: https://doi.org/10.3390/horticulturae9050541 .
H. S. Mputu, A. Abdel-mawgood, and A. Shimada, “TOMATO QUALITY CLASSIFICATION BASED ON TRANSFER LEARNING FEATURE EXTRACTION AND MACHINE LEARNING ALGORITHM CLASSIFIERS,” IEEE Access, vol. 12, no. December 2023, pp. 8283–8295, 2024, doi:. https://doi.org/10.1109/ACCESS.2024.3352745
R. Qasrawi, M. Amro, R. Zaghal, M. Sawafteh, and S. V. Polo, “MACHINE LEARNING TECHNIQUES FOR TOMATO PLANT DISEASES CLUSTERING, PREDICTION AND CLASSIFICATION,” in 2021 International Conference on Promising Electronic Technologies (ICPET), 2021, pp. 40–45, doi: https://doi.org/10.1109/ICPET53277.2021.00014.
A. Sanga and V. Zsófia, “EVALUATION OF MACHINE LEARNING ALGORITHM’S SELECTION CRITERIA FOR SATELLITE IMAGE CLASSIFICATION, LEVERAGING ACCURACY AND EXECUTION TIMES,” Egypt. J. Remote Sens. Sp. Sci., vol. 28, no. 3, pp. 534–541, 2025, doi: https://doi.org/10.1016/j.ejrs.2025.08.001
H. Azarmdel, A. Jahanbakhshi, S. S. Mohtasebi, and A. R. Muñoz, “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),” Postharvest Biol. Technol., vol. 166, p. 111201, 2020, doi: https://doi.org/10.1016/j.postharvbio.2020.111201
A. A. Adam and R. Alfred, “OPTIMIZING THE CLASSIFICATION PERFORMANCE BY FINE-TUNING THE MACHINE LEARNING HYPERPARAMETERS AND UTILIZING PCA AND RFE FEATURE SELECTION METHODS,” in Proceedings of the 4th International Conference on Advances in Computational Science and Engineering, 2024, pp. 435–454.doi: https://doi.org/10.1007/978-981-97-2977-7_27
J. Chen et al., “DETECTING RIPE FRUITS UNDER NATURAL OCCLUSION AND ILLUMINATION CONDITIONS,” Comput. Electron. Agric., vol. 190, p. 106450, 2021, doi: https://doi.org/10.1016/j.compag.2021.106450
M. Knott, F. Perez-Cruz, and T. Defraeye, “FACILITATED MACHINE LEARNING FOR IMAGE-BASED FRUIT QUALITY ASSESSMENT.” 2022, doi: https://doi.org/10.1016/j.jfoodeng.2022.111401
G. C. Wakchaure et al., “MATURITY STAGES DETECTION PROTOTYPE DEVICE FOR CLASSIFYING CUSTARD APPLE (ANNONA SQUAMOSA L) FRUIT USING IMAGE PROCESSING APPROACH,” Smart Agric. Technol., vol. 7, p. 100394, 2024, doi: https://doi.org/10.1016/j.atech.2023.100394.
C. Pestana, N. Akhtar, W. Liu, D. Glance, and A. Mian, “ADVERSARIAL ATTACKS AND DEFENSE ON DEEP LEARNING CLASSIFICATION MODELS USING YCBCR COLOR IMAGES,” in 2021 International Joint Conference on Neural Networks (IJCNN), 2021, pp. 1–9, doi: https://doi.org/10.1109/IJCNN52387.2021.9533495.
D. Worasawate, P. Sakunasinha, and S. Chiangga, “AUTOMATIC CLASSIFICATION OF THE RIPENESS STAGE OF MANGO FRUIT USING A MACHINE LEARNING APPROACH,” AgriEngineering, vol. 4, no. 1, pp. 32–47, 2022, doi: https://doi.org/10.3390/agriengineering4010003
A. Raghavendra, D. S. Guru, M. K. Rao, and R. Sumithra, “ARTIFICIAL INTELLIGENCE IN AGRICULTURE HIERARCHICAL APPROACH FOR RIPENESS GRADING OF MANGOES,” Artif. Intell. Agric., vol. 4, pp. 243–252, 2020, doi:. https://doi.org/10.1016/j.aiia.2020.10.003
K. Chawgien and S. Kiattisin, “MACHINE LEARNING TECHNIQUES FOR CLASSIFYING THE SWEETNESS OF WATERMELON USING ACOUSTIC SIGNAL AND IMAGE PROCESSING,” Comput. Electron. Agric., vol. 181, p. 105938, 2021, doi: https://doi.org/10.1016/j.compag.2020.105938
C. Huang et al., “STRUCTURED HYPERSPECTRAL IMAGING AND MACHINE LEARNING FOR NON-DESTRUCTIVE KIWIFRUIT FIRMNESS PREDICTION, CLASSIFICATION, AND INTELLIGENT POST-HARVEST MANAGEMENT,” J. Food Compos. Anal., vol. 147, p. 108026, 2025, doi: https://doi.org/10.1016/j.jfca.2025.108026
P. Tipauksorn, P. Luekhong, M. Okada, J. Thongpron, C. Nadee, and K. Yingkayun, “OPTIMIZED ENSEMBLE LEARNING FOR NON-DESTRUCTIVE AVOCADO RIPENESS CLASSIFICATION,” Smart Agric. Technol., vol. 12, p. 101114, 2025, doi: https://doi.org/10.1016/j.atech.2025.101114.
A. N. De Oliveira et al., “TOMATO CLASSIFICATION USING MASS SPECTROMETRY-MACHINE LEARNING TECHNIQUE : A FOOD SAFETY-ENHANCING PLATFORM,” Food Chem., vol. 398, no. August 2022, p. 133870, 2023, doi: https://doi.org/10.1016/j.foodchem.2022.133870.
S. Alharthi and A. Gutub, “ADJUSTING IMAGE STEGO PRACTICALITY VIA YCBCR COLOR SPACE FORMATION,” J. Eng. Res., no. January, 2025, doi: https://doi.org/10.1016/j.jer.2025.07.008.
R. C. Gonzalez and R. E. Woods, DIGITAL IMAGE PROCESSING. Pearson, 2018.
R. Lukac and K.N. Plataniotis, COLOR IMAGE PROCESSING: METHODS AND APPLICATIONS (1st ed.). CRC Press, 2006, doi: https://doi.org/10.1201/9781420009781
E. Xhaferra, E. Cina, and L. Toti, “ClaSSIFICATION OF STANDARD FASHION MNIST DATASET USING DEEP LEARNING BASED CNN ALGORITHMS,” in 2022 International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT), 2022, pp. 494–498, doi: https://doi.org/10.1109/ISMSIT56059.2022.9932737
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