PERTANIKA JOURNAL OF TROPICAL AGRICULTURAL SCIENCE

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• 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