Fruit Type Recognition Using Hybrid Method with Principal Component Analysis (PCA)
DOI:
https://doi.org/10.29103/micoms.v3i.211Keywords:
Deep learning, K-nearest neighbor (k-NN), Gaussian filter, Principal component analysisAbstract
This research concentrated on fruit image recognition. Fruit recognition in this study can be used to estimate the number of fruits that exist. Data testing is used to classify a fruit image that has been trained to recognize a variety of labels (fruit types). Until the classification process, several processes and methods are used in this research, one of which is the Gaussian filter to improve the quality of fruit image recognition. In addition, the Gabor filter is used in the feature extraction process, and the PCA technique is used in feature selection to select the best features. To classify the chosen feature, deep learning and the k-nearest neighbor (k-NN) method will be used. Furthermore, the processes used improved the accuracy of 92%, a root mean squared error (RMSE) of 0.323, a mean squared error (MSE) of 0.6278.
References
W. Zhang et al., “Deep-learning-based in-field citrus fruit detection and tracking,” Hortic. Res., vol. 9, no. March 2021, 2022, doi: 10.1093/hr/uhac003.
D. N. V. S. L. S. Indira, J. Goddu, B. Indraja, V. M. L. Challa, and B. Manasa, “A review on fruit recognition and feature evaluation using CNN,” Mater. Today Proc., no. xxxx, pp. 1–6, 2021, doi: 10.1016/j.matpr.2021.07.267.
J. Lu and N. Sang, “Detecting citrus fruits and occlusion recovery under natural illumination conditions,” Comput. Electron. Agric., vol. 110, pp. 121–130, 2015, doi: 10.1016/j.compag.2014.10.016.
A. Koirala, K. B. Walsh, Z. Wang, and C. McCarthy, “Deep learning – Method overview and review of use for fruit detection and yield estimation,” Comput. Electron. Agric., vol. 162, no. January, pp. 219–234, 2019, doi: 10.1016/j.compag.2019.04.017.
W. Widyawati and R. Febriani, “Real-time detection of fruit ripeness using the YOLOv4 algorithm,” Tek. J. Sains dan Teknol., vol. 17, no. 2, p. 205, 2021, doi: 10.36055/tjst.v17i2.12254.
M. Omid, M. Khojastehnazhand, and A. Tabatabaeefar, “Estimating volume and mass of citrus fruits by image processing technique,” J. Food Eng., vol. 100, no. 2, pp. 315–321, 2010, doi: 10.1016/j.jfoodeng.2010.04.015.
D. M. Bulanon, T. F. Burks, and V. Alchanatis, “Study on temporal variation in citrus canopy using thermal imaging for citrus fruit detection,” Biosyst. Eng., vol. 101, no. 2, pp. 161–171, 2008, doi: 10.1016/j.biosystemseng.2008.08.002.
H. Kuang, C. Liu, L. L. H. Chan, and H. Yan, “Multi-class fruit detection based on image region selection and improved object proposals,” Neurocomputing, vol. 283, pp. 241–255, 2018, doi: 10.1016/j.neucom.2017.12.057.
A. D. M. Africa, “Ripe Fruit Detection and Classification using Machine Learning,” Int. J. Emerg. Trends Eng. Res., vol. 8, no. 5, pp. 1845–1849, 2020, doi: 10.30534/ijeter/2020/60852020.
R. M. Alonso-Salces, C. Herrero, A. Barranco, L. A. Berrueta, B. Gallo, and F. Vicente, “Classification of apple fruits according to their maturity state by the pattern recognition analysis of their polyphenolic compositions,” Food Chem., vol. 93, no. 1, pp. 113–123, 2005, doi: 10.1016/j.foodchem.2004.10.013.
J. Blasco, N. Aleixos, J. Gómez-Sanchís, and E. Moltó, “Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features,” Biosyst. Eng., vol. 103, no. 2, pp. 137–145, 2009, doi: 10.1016/j.biosystemseng.2009.03.009.
S. I. Saedi and H. Khosravi, “A deep neural network approach towards real-time on-branch fruit recognition for precision horticulture,” Expert Syst. Appl., vol. 159, p. 113594, 2020, doi: 10.1016/j.eswa.2020.113594.
J. Jhawar, “Orange Sorting by Applying Pattern Recognition on Colour Image,” Phys. Procedia, vol. 78, no. December 2015, pp. 691–697, 2016, doi: 10.1016/j.procs.2016.02.118.
P. Wan, A. Toudeshki, H. Tan, and R. Ehsani, “A methodology for fresh tomato maturity detection using computer vision,” Comput. Electron. Agric., vol. 146, no. January, pp. 43–50, 2018, doi: 10.1016/j.compag.2018.01.011.
H. Mureşan and M. Oltean, “Fruit recognition from images using deep learning,” Acta Univ. Sapientiae, Inform., vol. 10, no. 1, pp. 26–42, 2018, doi: 10.2478/ausi-2018-0002.
M. Haghighat, S. Zonouz, and M. Abdel-Mottaleb, “CloudID: Trustworthy cloud-based and cross-enterprise biometric identification,” Expert Syst. Appl., vol. 42, no. 21, pp. 7905–7916, 2015, doi: 10.1016/j.eswa.2015.06.025.
R. S. Choras, “Image Feature Extraction Techniques and Their Applications for CBIR and Biometrics Systems,” Int. J. Biol. Biomed. Eng., vol. 1, no. 1, pp. 6–15, 2007.
T. Li et al., “An improved binocular localization method for apple based on fruit detection using deep learning,” Inf. Process. Agric., no. xxxx, 2021, doi: 10.1016/j.inpa.2021.12.003.
M. B. Sulthan, I. Wahyudi, and L. Suhartini, “Analisis Sentimen Pada Bencana Alam Menggunakan Deep Neural Network dan Information Gain,” J. Apl. Teknol. Inf. dan Manaj., vol. 2, no. 2, pp. 65–71, 2021, doi: 10.31102/jatim.v2i2.1273.
A. Tripathy, A. Agrawal, and S. K. Rath, “Classification of Sentimental Reviews Using Machine Learning Techniques,” Procedia Comput. Sci., vol. 57, pp. 821–829, 2015, doi: 10.1016/j.procs.2015.07.523.
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