Excessive feature dimensions impact the effectiveness of machine learning, computationally expensive and the analysis of feature correlations in the engineering area. This paper uses the colour descriptor to get the most optimal feature to improve time consumption and efficiency. This study investigated Ficus carica L. (figs) with three classification stages. The ripening classification of fig was examined using colour features descriptor with two different colour models, RGB and HSV. In addition, the machine learning classification model based on Artificial Neural Network (ANN) that utilised the Feed-Forward Neural Network (FFNN) model to classify the ripeness of fig is considered in this characterisation. Five different numbers of binning were characterised for RGB and HSV. Both colour feature descriptors were compared in terms of accuracy, sensitivity, precision, and time consumption to identify the dimension of the optimal feature. Based on the result, reducing the size of images will improve the time consumption with comparable accuracy. Moreover, the reduction of features dimension cannot be too small or too big due to inequitable enough to differentiate the ripeness stages and lead to a false error state. The optimal features dimension in binning for RGB was 8 (R/G/B) bins with 96.7% accuracy. Meanwhile, 96.7% accuracy for HSV at 15, 5, and 5 (H, S, V) bins as optimal colour features.

• Ali, M. M., Hashim, N., & Hamid, A. S. A. (2020). Combination of laser-light backscattering imaging and computer vision for rapid determination of oil palm fresh fruit bunches maturity. Computers and Electronics in Agriculture, 169, Article 105235. https://doi.org/10.1016/j.compag.2020.105235

• Bahar, A., & Lichter, A. (2018). Effect of controlled atmosphere on the storage potential of Ottomanit fig fruit. Scientia Horticulturae, 227, 196-201. https://doi.org/10.1016/j.scienta.2017.09.036

• Baigvand, M., Banakar, A., Minaei, S., Khodaei, J., & Behroozi-khazaei, N. (2015). Machine vision system for grading of dried figs. Computers and Electronics in Agriculture, 119, 158-165. https://doi.org/10.1016/j.compag.2015.10.019

• Bargshady, G., Zhou, X., Deo, R. C., Soar, J., & Whittaker, F. (2020). The modeling of human facial pain intensity based on Temporal Convolutional Networks trained with video frames in HSV color space. Applied Soft Computing, 97, Article 106805. https://doi.org/10.1016/j.asoc.2020.106805

• Behera, S. K., Rath, A. K., & Sethy, P. K. (2020). Maturity status classification of papaya fruits based on machine learning and transfer learning approach. Information Processing in Agriculture, 8(2), 244-250. https://doi.org/10.1016/j.inpa.2020.05.003

• Bhargava, A., & Bansal, A. (2021). Fruits and vegetables quality evaluation using computer vision: A review. Journal of King Saud University - Computer and Information Sciences, 33(3), 243-257. https://doi.org/10.1016/j.jksuci.2018.06.002

• Bhosale, A. A. (2017). Detection of sugar content in citrus fruits by capacitance method. Procedia Engineering, 181, 466-471. https://doi.org/10.1016/j.proeng.2017.02.417

• Bratu, A. M., Popa, C., Bojan, M., Logofatu, P. C., & Petrus, M. (2021). Non-destructive methods for fruit quality evaluation. Scientific Reports, 11(1), 1-15. https://doi.org/10.1038/s41598-021-87530-2

• Cavallo, D. P., Cefola, M., Pace, B., Logrieco, A. F., & Attolico, G. (2019). Non-destructive and contactless quality evaluation of table grapes by a computer vision system. Computers and Electronics in Agriculture, 156, 558-564. https://doi.org/10.1016/j.compag.2018.12.019

• Cho, B. H., & Koseki, S. (2021). Determination of banana quality indices during the ripening process at different temperatures using smartphone images and an artificial neural network. Scientia Horticulturae, 288, Article 110382. https://doi.org/10.1016/j.scienta.2021.110382

• El Abbadi, N., & San, K. M. (2013). Face detection using a hybrid approach that combines HSV and RGB Face detection using a hybrid approach that combines HSV and RGB. International Journal of Computer Science and Mobile Computing, 2(3), 127-136.

• Fatima, S., & Seshashayee, M. (2022). Feature fusion of fruit image categorization using machine learning. International Journal of Nonlinear Analysis and Applications, 13, 2008-6822. http://dx.doi.org/10.22075/ijnaa.2022.6332

• Fermo, I. R., Cavali, T. S., Bonfim-Rocha, L., Srutkoske, C. L., Flores, F. C., & Andrade, C. M. G. (2021). Development of a low-cost digital image processing system for oranges selection using hopfield networks. Food and Bioproducts Processing, 125, 181-192. https://doi.org/10.1016/j.fbp.2020.11.012

• Freiman, Z. E., Rosianskey, Y., Dasmohapatra, R., Kamara, I., & Flaishman, M. A. (2015). The ambiguous ripening nature of the fig (Ficus carica L.) fruit: A gene-expression study of potential ripening regulators and ethylene-related genes. Journal of Experimental Botany, 66(11), 3309-3324. https://doi.org/10.1093/jxb/erv140

• Hamdani, H., Septiarini, A., Sunyoto, A., & Suyanto, S. (2021). Detection of oil palm leaf disease based on color histogram and supervised classifier. Optik, 245, Article 167753. https://doi.org/10.1016/j.ijleo.2021.167753

• Hamuda, E., Ginley, B. M., Glavin, M., & Jones, E. (2017). Automatic crop detection under field conditions using the HSV colour space and morphological operations. Computers and electronics in agriculture, 133, 97-107. https://doi.org/10.1016/j.compag.2016.11.021

• Hssaini, L., Hanine, H., Razouk, R., Ennahli, S., Mekaoui, A., & Charafi, J. (2019). Characterization of local fig clones (Ficus carica L.) collected in Northern Morocco. Fruits, The International Journal of Tropical and Subtropical Horticulture, 74(2), 55-64. https://doi.org/10.17660/th2019/74.2.1

• Ikmal, M., Maruzuki, F., Shahrin, A. S., Setumin, S., Ramli, R. A., & Fithry, S. (2021). A Multilayer perceptron approach for Ficus carica (fig) ripening classification. ESTEEM Academic Journal, 17, 56-66.

• Kangune, K., VKulkarni, V., & Kosamkar, P. (2019). Automated estimation of grape ripeness. Asian Journal of Convergence in Technology, 5(1), 1-6.

• Khalid, N. S., Abdullah, A. H., Shukor, S. A. A., Syahir, A. S. F., Mansor, H., & Dalila, N. D. N. (2018). Non-destructive technique based on specific gravity for post-harvest Mangifera Indica L. cultivar maturity. In Asia Modelling Symposium 2017 and 11th International Conference on Mathematical Modelling and Computer Simulation (pp. 113-117). IEEE Publishing. https://doi.org/10.1109/AMS.2017.26

• Li, J., Huang, W., Tian, X., Wang, C., Fan, S., & Zhao, C. (2016). Fast detection and visualization of early decay in citrus using Vis-NIR hyperspectral imaging. Computers and Electronics in Agriculture, 127, 582-592. https://doi.org/10.1016/j.compag.2016.07.016

• Magabilin, M. C. V., Fajardo, A. C., & Medina, R. P. (2022). Optimal Ripeness Classification of the Philippine Guyabano Fruit using Deep Learning. In 2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T) (pp. 1-5). IEEE Publishing. https://doi.org/10.1109/ICPC2T53885.2022.9777014

• Manthou, E., Lago, S. L., Dagres, E., Lianou, A., Tsakanikas, P., Panagou, E. Z., Anastasiadi, M., Mohareb, F., & Nychas, G. J. E. (2020). Application of spectroscopic and multispectral imaging technologies on the assessment of ready-to-eat pineapple quality: A performance evaluation study of machine learning models generated from two commercial data analytics tools. Computers and Electronics in Agriculture, 175, Article 105529. https://doi.org/10.1016/j.compag.2020.105529

• Marei, N., & Crane, J. C. (1971). Growth and respiratory response of fig (Ficus carica L. cv. Mission) fruits to ethylene. Plant Physiology, 48(3), 249-254. https://doi.org/10.1104/pp.48.3.249

• Minas, I. S., Blanco-Cipollone, F., & Sterle, D. (2021). Accurate non-destructive prediction of peach fruit internal quality and physiological maturity with a single scan using near infrared spectroscopy. Food Chemistry, 335, Article 127626. https://doi.org/10.1016/j.foodchem.2020.127626

• Mohd, M., Hashim, N., Khairunniza, S., & Shamsudin, R. (2017). Postharvest biology and technology quality evaluation of watermelon using laser-induced backscattering imaging during storage. Postharvest Biology and Technology, 123, 51-59. https://doi.org/10.1016/j.postharvbio.2016.08.010

• Munera, S., Amigo, J. M., Aleixos, N., Talens, P., Cubero, S., & Blasco, J. (2018). Potential of VIS-NIR hyperspectral imaging and chemometric methods to identify similar cultivars of nectarine. Food Control, 86, 1-10. https://doi.org/10.1016/j.foodcont.2017.10.037

• Nguyen-Do-Trong, N., Dusabumuremyi, J. C., & Saeys, W. (2018). Cross-polarized VNIR hyperspectral reflectance imaging for non-destructive quality evaluation of dried banana slices, drying process monitoring and control. Journal of Food Engineering, 238, 85-94. https://doi.org/10.1016/j.jfoodeng.2018.06.013

• Nugroho, C. S., Ainuri, M., & Falah, M. A. F. (2021). Physical quality determination of fresh strawberry (Fragaria x ananassa var. Osogrande) fruit in tropical environment using image processing approach. IOP Conference Series: Earth and Environmental Science, 759, 1-6. https://doi.org/10.1088/1755-1315/759/1/012020

• Ortac, G., Bilgi, A. S., Gorgulu, Y. E., Gunes, A., Kalkan, H., & Tasdemir, K. (2016). Classification of black mold contaminated figs by hyperspectral imaging. In 2015 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2015 (pp. 227-230). IEEE Publishing. https://doi.org/10.1109/ISSPIT.2015.7394332

• Pérez-Rodríguez, F., & Gómez-García, E. (2019). Codelplant: Regression-based processing of RGB images for colour models in plant image segmentation. Computers and Electronics in Agriculture, 163, Article 104880. https://doi.org/10.1016/j.compag.2019.104880

• Popov, V., Ostarek, M., & Tenison, C. (2018). Practices and pitfalls in inferring neural representations. NeuroImage, 174, 340-351. https://doi.org/10.1016/j.neuroimage.2018.03.041

• Pu, Y. Y., Sun, D. W., Buccheri, M., Grassi, M., Cattaneo, T. M. P., & Gowen, A. (2019). Ripeness classification of bananito fruit (Musa acuminata, AA): A comparison study of visible spectroscopy and hyperspectral imaging. Food Analytical Methods, 12(8), 1693-1704. https://doi.org/10.1007/s12161-019-01506-7

• Rady, A., Ekramirad, N., Adedeji, A. A., Li, M., & Alimardani, R. (2017). Hyperspectral imaging for detection of codling moth infestation in GoldRush apples. Postharvest Biology and Technology, 129, 37-44. https://doi.org/10.1016/j.postharvbio.2017.03.007

• Sanchez, P. D. C., Hashim, N., Shamsudin, R., & Nor, M. Z. M. (2020). Quality evaluation of sweet potatoes (Ipomoea batatas L.) of different varieties using laser light backscattering imaging technique. Scientia Horticulturae, 260, Article 108861. https://doi.org/10.1016/j.scienta.2019.108861

• Septiarini, A., Sunyoto, A., Hamdani, H., Kasim, A. A., Utaminingrum, F., & Hatta, H. R. (2021). Machine vision for the maturity classification of oil palm fresh fruit bunches based on color and texture features. Scientia Horticulturae, 286, Article 110245. https://doi.org/10.1016/j.scienta.2021.110245

• Skolik, P., Morais, C. L. M., Martin, F. L., & McAinsh, M. R. (2019). Determination of developmental and ripening stages of whole tomato fruit using portable infrared spectroscopy and Chemometrics. BMC Plant Biology, 19(1), 1-15. https://doi.org/10.1186/s12870-019-1852-5

• Song, W., Jiang, N., Wang, H., & Guo, G. (2020). Evaluation of machine learning methods for organic apple authentication based on diffraction grating and image processing. Journal of Food Composition and Analysis, 88, Article 103437. https://doi.org/10.1016/j.jfca.2020.103437

• Taghizadeh, M., Gowen, A. A., & O’Donnell, C. P. (2011). Comparison of hyperspectral imaging with conventional RGB imaging for quality evaluation of Agaricus bisporus mushrooms. Biosystems Engineering, 108(2), 191-194. https://doi.org/10.1016/j.biosystemseng.2010.10.005

• Tang, C., He, H., Li, E., & Li, H. (2018). Multispectral imaging for predicting sugar content of ‘Fuji’ apples. Optics and Laser Technology, 106, 280-285. https://doi.org/10.1016/j.optlastec.2018.04.017

• Teerachaichayut, S., & Ho, H. T. (2017). Postharvest biology and technology non-destructive prediction of total soluble solids, titratable acidity and maturity index of limes by near infrared hyperspectral imaging. Postharvest Biology and Technology, 133, 20-25. https://doi.org/10.1016/j.postharvbio.2017.07.005

• Worasawate, D., Sakunasinha, P., & Chiangga, S. (2022). Automatic classification of the ripeness stage of mango fruit using a machine learning approach. AgriEngineering, 4(1), 32-47. https://doi.org/10.3390/agriengineering4010003

• Wu, G., Li, B., Zhu, Q., Huang, M., & Guo, Y. (2020). Using color and 3D geometry features to segment fruit point cloud and improve fruit recognition accuracy. Computers and Electronics in Agriculture, 174, Article 105475. https://doi.org/10.1016/j.compag.2020.105475

• Yang, B., Gao, Y., Yan, Q., Qi, L., Zhu, Y., & Wang, B. (2020). Estimation Method of Soluble Solid Content in Peach Based on Deep Features of Hyperspectral Imagery. Sensors, 20(18), Article 5021. https://doi.org/10.3390/s20185021

• Yijing, W., Yi, Y., Xue-fen, W., Jian, C., & Xinyun, L. (2021). Fig fruit recognition method based on YOLO v4 deep learning. In 2021 18th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) (pp. 303-306). IEEE Publishing. https://doi.org/10.1109/ECTI-CON51831.2021.9454904

• Zulkifli, N., Hashim, N., Abdan, K., & Hanafi, M. (2019). Application of laser-induced backscattering imaging for predicting and classifying ripening stages of “Berangan” bananas. Computers and Electronics in Agriculture, 160, 100-107. https://doi.org/10.1016/j.compag.2019.02.031