TEKNOLOGI NON DESTRUKTIF DAN MACHINE LEARNING UNTUK PREDIKSI KUALITAS BUAH: TINJAUAN LITERATUR 2015-2020

Ali Khumaidi

Abstract


Accuracy in the prediction of fruit quality is very important to provide the best products to consumers and increase economic value. To produce accurate prediction of good fruit quality, it is needed the right technological instruments and data processing techniques. In this literature review systematically summarizes and analyzes non-destructive technology and machine learning for the prediction of fruit quality over the past 5 years and its challenges and explores future opportunities and prospects for forming the latest references for researchers. Based on the results of the analysis that for accuracy and speed in the examination of fruit quality for internal and external attributes required different technological approaches, methods and algorithms according to their characteristics. Development of technology and algorithms continues to be achieved to achieve the goal that is the presence of fruit quality detection devices that are fast, reliable, portable, and cost-effective.


Keywords


Fruit Quality, Machine Learning, Non Destructive, Prediction, Technology

References


Anurekha D, Sankaran RA. 2020. Efficient classification and grading of MANGOES with GANFIS for improved performance. Multimed. Tools Appl. 79(5–6):4169–4184.doi:10.1007/s11042-019-07784-x.

Arora M, Dutta MK, Travieso CM, Burget R. 2018. Image Processing Based Classification of Enzymatic Browning in Chopped Apples. Di dalam: 2018 IEEE International Work Conference on Bioinspired Intelligence (IWOBI). IEEE. hlm. 1–8.

Azarmdel H, Jahanbakhshi A, Mohtasebi SS, Muñoz AR. 2020. 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. 166:111201.doi:10.1016/j.postharvbio.2020.111201.

Bae H, Seo Y-W, Kim D-Y, Lohumi S, Park E, Cho B-K. 2016. Development of Non-Destructive Sorting Technique for Viability of Watermelon Seed by Using Hyperspectral Image Processing. J. Korean Soc. Nondestruct. Test. 36(1):35–44.doi:10.7779/JKSNT.2016.36.1.35.

Bai Y, Xiong Y, Huang J, Zhou J, Zhang B. 2019. Accurate prediction of soluble solid content of apples from multiple geographical regions by combining deep learning with spectral fingerprint features. Postharvest Biol. Technol. 156:110943.doi:10.1016/j.postharvbio.2019.110943.

Behera SK, Rath AK, Sethy PK. 2020. Maturity Status Classification of Papaya Fruits based on Machine Learning and Transfer Learning Approach. Inf. Process. Agric..doi:10.1016/j.inpa.2020.05.003.

Berardinelli A, Benelli A, Tartagni M, Ragni L. 2019. Kiwifruit flesh firmness determination by a NIR sensitive device and image multivariate data analyses. Sensors Actuators A Phys. 296:265–271.doi:10.1016/j.sna.2019.07.027.

Bexiga F, Rodrigues D, Guerra R, Brázio A, Balegas T, Cavaco AM, Antunes MD, Valente de Oliveira J. 2017. A TSS classification study of ‘Rocha’ pear ( Pyrus communis L.) based on non-invasive visible/near infra-red reflectance spectra. Postharvest Biol. Technol. 132:23–30.doi:10.1016/j.postharvbio.2017.05.014.

Bhargava A, Bansal A. 2020. Quality evaluation of Mono & bi-Colored Apples with computer vision and multispectral imaging. Multimed. Tools Appl. 79(11–12):7857–7874.doi:10.1007/s11042-019-08564-3.

Buyukcan MB, Kavdir I. 2017. Prediction of some internal quality parameters of apricot using FT-NIR spectroscopy. J. Food Meas. Charact. 11(2):651–659.doi:10.1007/s11694-016-9434-9.

Caladcad JA, Cabahug S, Catamco MR, Villaceran PE, Cosgafa L, Cabizares KN, Hermosilla M, Piedad EJ. 2020. Determining Philippine coconut maturity level using machine learning algorithms based on acoustic signal. Comput. Electron. Agric. 172(November 2019):105327.doi:10.1016/j.compag.2020.105327.

Castro W, Oblitas J, De-La-Torre M, Cotrina C, Bazan K, Avila-George H. 2019. Classification of Cape Gooseberry Fruit According to its Level of Ripeness Using Machine Learning Techniques and Different Color Spaces. IEEE Access. 7:27389–27400.doi:10.1109/ACCESS.2019.2898223.

Cavallo D Pietro, Cefola M, Pace B, Logrieco AF, Attolico G. 2019. Non-destructive and contactless quality evaluation of table grapes by a computer vision system. Comput. Electron. Agric. 156:558–564.doi:10.1016/j.compag.2018.12.019.

Cen H, Lu R, Zhu Q, Mendoza F. 2016. Nondestructive detection of chilling injury in cucumber fruit using hyperspectral imaging with feature selection and supervised classification. Postharvest Biol. Technol. 111:352–361.doi:10.1016/j.postharvbio.2015.09.027.

Che W, Sun L, Zhang Q, Tan W, Ye D, Zhang D, Liu Y. 2018. Pixel based bruise region extraction of apple using Vis-NIR hyperspectral imaging. Comput. Electron. Agric. 146:12–21.doi:10.1016/j.compag.2018.01.013.

Cortés V, Ortiz C, Aleixos N, Blasco J, Cubero S, Talens P. 2016a. A new internal quality index for mango and its prediction by external visible and near-infrared reflection spectroscopy. Postharvest Biol. Technol. 118:148–158.doi:10.1016/j.postharvbio.2016.04.011.

Cortés V, Ortiz C, Aleixos N, Blasco J, Cubero S, Talens P. 2016b. A new internal quality index for mango and its prediction by external visible and near-infrared reflection spectroscopy. Postharvest Biol. Technol. 118:148–158.doi:10.1016/j.postharvbio.2016.04.011.

Cortés V, Rodríguez A, Blasco J, Rey B, Besada C, Cubero S, Salvador A, Talens P, Aleixos N. 2017. Prediction of the level of astringency in persimmon using visible and near-infrared spectroscopy. J. Food Eng. 204:27–37.doi:10.1016/j.jfoodeng.2017.02.017.

Cui S, Ling P, Zhu H, Keener H. 2018. Plant Pest Detection Using an Artificial Nose System: A Review. Sensors. 18(2):378.doi:10.3390/s18020378.

Van Dael M, Herremans E, Verboven V, Opara UL, Nicolaï B, Lebotsa S. 2016. Automated online detection of granulation in oranges using X-ray radiographs. Acta Hortic.(1119):179–182.doi:10.17660/ActaHortic.2016.1119.24.

van Dael M, Lebotsa S, Herremans E, Verboven P, Sijbers J, Opara UL, Cronje PJ, Nicolaï BM. 2016. A segmentation and classification algorithm for online detection of internal disorders in citrus using X-ray radiographs. Postharvest Biol. Technol. 112:205–214.doi:10.1016/j.postharvbio.2015.09.020.

Deák K, Szigedi T, Pék Z, Baranowski P, Helyes L. 2015. Carotenoid determination in tomato juice using near infrared spectroscopy. Int. Agrophysics. 29(3):275–282.doi:10.1515/intag-2015-0032.

Dhakshina Kumar S, Esakkirajan S, Bama S, Keerthiveena B. 2020. A microcontroller based machine vision approach for tomato grading and sorting using SVM classifier. Microprocess. Microsyst. 76:103090.doi:10.1016/j.micpro.2020.103090.

Dong J, Guo W. 2015. Nondestructive Determination of Apple Internal Qualities Using Near-Infrared Hyperspectral Reflectance Imaging. Food Anal. Methods. 8(10):2635–2646.doi:10.1007/s12161-015-0169-8.

Dong J, Guo W, Wang Z, Liu D, Zhao F. 2016. Nondestructive Determination of Soluble Solids Content of ‘Fuji’ Apples Produced in Different Areas and Bagged with Different Materials During Ripening. Food Anal. Methods. 9(5):1087–1095.doi:10.1007/s12161-015-0278-4.

Dong Q, Du L, Zhuang L, Li R, Liu Q, Wang P. 2013. A novel bioelectronic nose based on brain–machine interface using implanted electrode recording in vivo in olfactory bulb. Biosens. Bioelectron. 49:263–269.doi:10.1016/j.bios.2013.05.035.

Du X, Li X, Liu Y, Zhou W, Li J. 2019. Genetic algorithm optimized non-destructive prediction on property of mechanically injured peaches during postharvest storage by portable visible/shortwave near-infrared spectroscopy. Sci. Hortic. (Amsterdam). 249:240–249.doi:10.1016/j.scienta.2019.01.057.

Fan S, Li C, Huang W, Chen L. 2017. Detection of blueberry internal bruising over time using NIR hyperspectral reflectance imaging with optimum wavelengths. Postharvest Biol. Technol. 134:55–66.doi:10.1016/j.postharvbio.2017.08.012.

Fan S, Zhang B, Li J, Liu C, Huang W, Tian X. 2016. Prediction of soluble solids content of apple using the combination of spectra and textural features of hyperspectral reflectance imaging data. Postharvest Biol. Technol. 121:51–61.doi:10.1016/j.postharvbio.2016.07.007.

Fashi M, Naderloo L, Javadikia H. 2019. The relationship between the appearance of pomegranate fruit and color and size of arils based on image processing. Postharvest Biol. Technol. 154:52–57.doi:10.1016/j.postharvbio.2019.04.017.

Feng L, Zhang M, Bhandari B, Guo Z. 2018. A novel method using MOS electronic nose and ELM for predicting postharvest quality of cherry tomato fruit treated with high pressure argon. Comput. Electron. Agric. 154:411–419.doi:10.1016/j.compag.2018.09.032.

Ferrari C, Foca G, Calvini R, Ulrici A. 2015. Fast exploration and classification of large hyperspectral image datasets for early bruise detection on apples. Chemom. Intell. Lab. Syst. 146:108–119.doi:10.1016/j.chemolab.2015.05.016.

FISHER RA. 1936. THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS. Ann. Eugen. 7(2):179–188.doi:10.1111/j.1469-1809.1936.tb02137.x.

Geladi P, Burger J, Lestander T. 2004. Hyperspectral imaging: calibration problems and solutions. Chemom. Intell. Lab. Syst. 72(2):209–217.doi:https://doi.org/10.1016/j.chemolab.2004.01.023.

Ghasemi-Varnamkhasti M, Mohammad-Razdari A, Yoosefian SH, Izadi Z, Rabiei G. 2019. Selection of an optimized metal oxide semiconductor sensor (MOS) array for freshness characterization of strawberry in polymer packages using response surface method (RSM). Postharvest Biol. Technol. 151:53–60.doi:10.1016/j.postharvbio.2019.01.016.

Gomes VM, Fernandes AM, Faia A, Melo-Pinto P. 2017. Comparison of different approaches for the prediction of sugar content in new vintages of whole Port wine grape berries using hyperspectral imaging. Comput. Electron. Agric. 140:244–254.doi:10.1016/j.compag.2017.06.009.

Gómez-Sanchis J, Gómez-Chova L, Aleixos N, Camps-Valls G, Montesinos-Herrero C, Moltó E, Blasco J. 2008. Hyperspectral system for early detection of rottenness caused by Penicillium digitatum in mandarins. J. Food Eng. 89(1):80–86.doi:https://doi.org/10.1016/j.jfoodeng.2008.04.009.

Guo W, Fang L, Liu D, Wang Z. 2015. Determination of soluble solids content and firmness of pears during ripening by using dielectric spectroscopy. Comput. Electron. Agric. 117:226–233.doi:10.1016/j.compag.2015.08.012.

Guo W, Shang L, Zhu X, Nelson SO. 2015. Nondestructive Detection of Soluble Solids Content of Apples from Dielectric Spectra with ANN and Chemometric Methods. Food Bioprocess Technol. 8(5):1126–1138.doi:10.1007/s11947-015-1477-0.

Guo W, Zhao F, Dong J. 2016. Nondestructive Measurement of Soluble Solids Content of Kiwifruits Using Near-Infrared Hyperspectral Imaging. Food Anal. Methods. 9(1):38–47.doi:10.1007/s12161-015-0165-z.

Guo Y, Ni Y, Kokot S. 2016. Evaluation of chemical components and properties of the jujube fruit using near infrared spectroscopy and chemometrics. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 153:79–86.doi:10.1016/j.saa.2015.08.006.

Guo Z, Huang W, Peng Y, Chen Q, Ouyang Q, Zhao J. 2016. Color compensation and comparison of shortwave near infrared and long wave near infrared spectroscopy for determination of soluble solids content of ‘Fuji’ apple. Postharvest Biol. Technol. 115:81–90.doi:10.1016/j.postharvbio.2015.12.027.

Hashim N, Adebayo SE, Abdan K, Hanafi M. 2018. Comparative study of transform-based image texture analysis for the evaluation of banana quality using an optical backscattering system. Postharvest Biol. Technol. 135:38–50.doi:10.1016/j.postharvbio.2017.08.021.

Hu M-H, Dong Q-L, Liu B-L. 2016. Classification and characterization of blueberry mechanical damage with time evolution using reflectance, transmittance and interactance imaging spectroscopy. Comput. Electron. Agric. 122:19–28.doi:10.1016/j.compag.2016.01.015.

Hu M-H, Dong Q-L, Liu B-L, Opara UL. 2016. Prediction of mechanical properties of blueberry using hyperspectral interactance imaging. Postharvest Biol. Technol. 115:122–131.doi:10.1016/j.postharvbio.2015.11.021.

Hu M-H, Dong Q-L, Liu B-L, Opara UL, Chen L. 2015. Estimating blueberry mechanical properties based on random frog selected hyperspectral data. Postharvest Biol. Technol. 106:1–10.doi:10.1016/j.postharvbio.2015.03.014.

Hu M-H, Zhao Y, Zhai G-T. 2018. Active learning algorithm can establish classifier of blueberry damage with very small training dataset using hyperspectral transmittance data. Chemom. Intell. Lab. Syst. 172:52–57.doi:10.1016/j.chemolab.2017.11.012.

Hu M, Zhai G, Zhao Y, Wang Z. 2018. Uses of selection strategies in both spectral and sample spaces for classifying hard and soft blueberry using near infrared data. Sci. Rep. 8(1):6671.doi:10.1038/s41598-018-25055-x.

Huang W, Li J, Wang Q, Chen L. 2015. Development of a multispectral imaging system for online detection of bruises on apples. J. Food Eng. 146:62–71.doi:10.1016/j.jfoodeng.2014.09.002.

Iqbal A, Sun D-W, Allen P. 2014. An overview on principle, techniques and application of hyperspectral imaging with special reference to ham quality evaluation and control. Food Control. 46:242–254.doi:https://doi.org/10.1016/j.foodcont.2014.05.024.

Iraji MS. 2019. Comparison between soft computing methods for tomato quality grading using machine vision. J. Food Meas. Charact. 13(1):1–15.doi:10.1007/s11694-018-9913-2.

Ireri D, Belal E, Okinda C, Makange N, Ji C. 2019. A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing. Artif. Intell. Agric. 2:28–37.doi:10.1016/j.aiia.2019.06.001.

Jahanbakhshi A, Momeny M, Mahmoudi M, Zhang Y-D. 2020. Classification of sour lemons based on apparent defects using stochastic pooling mechanism in deep convolutional neural networks. Sci. Hortic. (Amsterdam). 263:109133.doi:10.1016/j.scienta.2019.109133.

Jarolmasjed S, Zúñiga Espinoza C, Sankaran S. 2017. Near infrared spectroscopy to predict bitter pit development in different varieties of apples. J. Food Meas. Charact. 11(3):987–993.doi:10.1007/s11694-017-9473-x.

June Anne Caladcad, Shiela Cabahug, Mary Rose Catamco, Paul Elyson Villaceran, Leizel Cosgafa, Karl Norbert Cabizares, Marfe Hermosilla, Eduardo Jr.Piedad. 2020. Determining Philippine coconut maturity level using machine learning algorithms based on acoustic signal. Comput. Elec0tronics Agric. . 172.

Keresztes JC, Goodarzi M, Saeys W. 2016. Real-time pixel based early apple bruise detection using short wave infrared hyperspectral imaging in combination with calibration and glare correction techniques. Food Control. 66:215–226.doi:10.1016/j.foodcont.2016.02.007.

Khatiwada BP, Subedi PP, Hayes C, Carlos LCC, Walsh KB. 2016. Assessment of internal flesh browning in intact apple using visible-short wave near infrared spectroscopy. Postharvest Biol. Technol. 120:103–111.doi:10.1016/j.postharvbio.2016.06.001.

Khodabakhshian R, Emadi B, Khojastehpour M, Golzarian MR, Sazgarnia A. 2017a. Non-destructive evaluation of maturity and quality parameters of pomegranate fruit by visible/near infrared spectroscopy. Int. J. Food Prop. 20(1):41–52.doi:10.1080/10942912.2015.1126725.

Khodabakhshian R, Emadi B, Khojastehpour M, Golzarian MR, Sazgarnia A. 2017b. Development of a multispectral imaging system for online quality assessment of pomegranate fruit. Int. J. Food Prop. 20(1):107–118.doi:10.1080/10942912.2016.1144200.

Khoje S, Bodhe SK, Adsul AD. 2013. Automated Skin Defect Identification System for Fruit Grading Based on Discrete Curvelet Transform.

Kuzy J, Jiang Y, Li C. 2018. Blueberry bruise detection by pulsed thermographic imaging. Postharvest Biol. Technol. 136:166–177.doi:10.1016/j.postharvbio.2017.10.011.

Lamb N, Chuah MC. 2018. A Strawberry Detection System Using Convolutional Neural Networks. Di dalam: 2018 IEEE International Conference on Big Data (Big Data). IEEE. hlm. 2515–2520.

Landahl S, Terry LA. 2020. Non-destructive discrimination of avocado fruit ripeness using laser Doppler vibrometry. Biosyst. Eng. 194:251–260.doi:10.1016/j.biosystemseng.2020.04.001.

Lashgari M, Imanmehr A, Tavakoli H. 2020. Fusion of acoustic sensing and deep learning techniques for apple mealiness detection. J. Food Sci. Technol. 57(6):2233–2240.doi:10.1007/s13197-020-04259-y.

Li B, Hou B, Zhang D, Zhou Y, Zhao M, Hong R, Huang Y. 2016. Pears characteristics (soluble solids content and firmness prediction, varieties) testing methods based on visible-near infrared hyperspectral imaging. Optik (Stuttg). 127(5):2624–2630.doi:10.1016/j.ijleo.2015.11.193.

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. Comput. Electron. Agric. 127:582–592.doi:10.1016/j.compag.2016.07.016.

Li J, Luo W, Wang Z, Fan S. 2019. Early detection of decay on apples using hyperspectral reflectance imaging combining both principal component analysis and improved watershed segmentation method. Postharvest Biol. Technol. 149:235–246.doi:10.1016/j.postharvbio.2018.12.007.

Li J, Zhang H, Zhan B, Wang Z, Jiang Y. 2019. Determination of SSC in pears by establishing the multi-cultivar models based on visible-NIR spectroscopy. Infrared Phys. Technol. 102:103066.doi:10.1016/j.infrared.2019.103066.

Li Jiangbo, Zhang H, Zhan B, Zhang Y, Li R, Li Jingbin. 2020. Nondestructive firmness measurement of the multiple cultivars of pears by Vis-NIR spectroscopy coupled with multivariate calibration analysis and MC-UVE-SPA method. Infrared Phys. Technol. 104:103154.doi:10.1016/j.infrared.2019.103154.

Li M, Pullanagari RR, Pranamornkith T, Yule IJ, East AR. 2017. Quantitative prediction of post storage ‘Hayward’ kiwifruit attributes using at harvest Vis-NIR spectroscopy. J. Food Eng. 202:46–55.doi:10.1016/j.jfoodeng.2017.01.002.

Li S, Luo H, Hu M, Zhang M, Feng J, Liu Y, Dong Q, Liu B. 2019. Optical non-destructive techniques for small berry fruits: A review. Artif. Intell. Agric. 2:85–98.doi:10.1016/j.aiia.2019.07.002.

Li X, Li J, Tang J. 2018. A deep learning method for recognizing elevated mature strawberries. Di dalam: 2018 33rd Youth Academic Annual Conference of Chinese Association of Automation (YAC). IEEE. hlm. 1072–1077.

Liu Changhong, Liu W, Chen W, Yang J, Zheng L. 2015. Feasibility in multispectral imaging for predicting the content of bioactive compounds in intact tomato fruit. Food Chem. 173:482–488.doi:10.1016/j.foodchem.2014.10.052.

Liu Cong, Yang SX, Deng L. 2015a. Determination of internal qualities of Newhall navel oranges based on NIR spectroscopy using machine learning. J. Food Eng. 161:16–23.doi:10.1016/j.jfoodeng.2015.03.022.

Liu Cong, Yang SX, Deng L. 2015b. A comparative study for least angle regression on NIR spectra analysis to determine internal qualities of navel oranges. Expert Syst. Appl. 42(22):8497–8503.doi:10.1016/j.eswa.2015.07.005.

Liu C, Yang SX, Li X, Xu L, Deng L. 2020. Noise level penalizing robust Gaussian process regression for NIR spectroscopy quantitative analysis. Chemom. Intell. Lab. Syst. 201:104014.doi:10.1016/j.chemolab.2020.104014.

Liu Q, Sun K, Peng J, Xing M, Pan L, Tu K. 2018. Identification of Bruise and Fungi Contamination in Strawberries Using Hyperspectral Imaging Technology and Multivariate Analysis. Food Anal. Methods. 11(5):1518–1527.doi:10.1007/s12161-017-1136-3.

Liu Q, Wei K, Xiao H, Tu S, Sun K, Sun Y, Pan L, Tu K. 2019. Near-Infrared Hyperspectral Imaging Rapidly Detects the Decay of Postharvest Strawberry Based on Water-Soluble Sugar Analysis. Food Anal. Methods. 12(4):936–946.doi:10.1007/s12161-018-01430-2.

Van De Looverbosch T, Rahman Bhuiyan MH, Verboven P, Dierick M, Van Loo D, De Beenbouwer J, Sijbers J, Nicolaï B. 2020. Nondestructive internal quality inspection of pear fruit by X-ray CT using machine learning. Food Control. 113.doi:10.1016/j.foodcont.2020.107170.

Lorente D, Zude M, Idler C, Gómez-Sanchis J, Blasco J. 2015. Laser-light backscattering imaging for early decay detection in citrus fruit using both a statistical and a physical model. J. Food Eng. 154:76–85.doi:10.1016/j.jfoodeng.2015.01.004.

Lu H, Wang F, Liu X, Wu Y. 2017. Rapid Assessment of Tomato Ripeness Using Visible/Near-Infrared Spectroscopy and Machine Vision. Food Anal. Methods. 10(6):1721–1726.doi:10.1007/s12161-016-0734-9.

Lu Y, Lu R. 2017. Development of a Multispectral Structured Illumination Reflectance Imaging (SIRI) System and Its Application to Bruise Detection of Apples. Trans. ASABE. 60(4):1379–1389.doi:10.13031/trans.12158.

Magwaza LS, Opara UL, Terry LA, Landahl S, Cronje PJR, Nieuwoudt HH, Hanssens A, Saeys W, Nicolaï BM. 2013. Evaluation of Fourier transform-NIR spectroscopy for integrated external and internal quality assessment of Valencia oranges. J. Food Compos. Anal. 31(1):144–154.doi:https://doi.org/10.1016/j.jfca.2013.05.007.

MARKOVIC I, MARKOVIC D, ILIC J, SIMONOVIC V, VEG E, ŠINIKOVIĆ G, GUBELJAK N. 2018. Application of Statistical Indicators for Digital Image Analysis and Segmentation in Sorting of Agriculture Products. Teh. Vjesn. - Tech. Gaz. 25(6).doi:10.17559/TV-20171129091703.

Mogollon MR, Jara AF, Contreras C, Zoffoli JP. 2020. Quantitative and qualitative VIS-NIR models for early determination of internal browning in ‘Cripps Pink’ apples during cold storage. Postharvest Biol. Technol. 161:111060.doi:10.1016/j.postharvbio.2019.111060.

Mollazade K, Omid M, Tab FA, Mohtasebi SS. 2012. Principles and Applications of Light Backscattering Imaging in Quality Evaluation of Agro-food Products: a Review. Food Bioprocess Technol. 5(5):1465–1485.doi:10.1007/s11947-012-0821-x.

Munera S, Besada C, Aleixos N, Talens P, Salvador A, Sun D-W, Cubero S, Blasco J. 2017. Non-destructive assessment of the internal quality of intact persimmon using colour and VIS/NIR hyperspectral imaging. LWT. 77:241–248.doi:10.1016/j.lwt.2016.11.063.

Munera S, Hernández F, Aleixos N, Cubero S, Blasco J. 2019. Maturity monitoring of intact fruit and arils of pomegranate cv. ‘Mollar de Elche’ using machine vision and chemometrics. Postharvest Biol. Technol. 156:110936.doi:10.1016/j.postharvbio.2019.110936.

Naik S, Patel B. 2017. Machine Vision based Fruit Classification and Grading - A Review. Int. J. Comput. Appl. 170(9):22–34.doi:10.5120/ijca2017914937.

Nasiri A, Taheri-Garavand A, Zhang Y-D. 2019. Image-based deep learning automated sorting of date fruit. Postharvest Biol. Technol. 153:133–141.doi:10.1016/j.postharvbio.2019.04.003.

Ncama K, Magwaza LS, Poblete-Echeverría CA, Nieuwoudt HH, Tesfay SZ, Mditshwa A. 2018. On-tree indexing of ‘Hass’’ avocado fruit by non-destructive assessment of pulp dry matter and oil content.’ Biosyst. Eng. 174:41–49.doi:10.1016/j.biosystemseng.2018.06.011.

Nicolaï BM, Beullens K, Bobelyn E, Peirs A, Saeys W, Theron KI, Lammertyn J. 2007. Nondestructive measurement of fruit and vegetable quality by means of NIR spectroscopy: A review. Postharvest Biol. Technol. 46(2):99–118.doi:https://doi.org/10.1016/j.postharvbio.2007.06.024.

Nogales-Bueno J, Baca-Bocanegra B, Rodríguez-Pulido FJ, Heredia FJ, Hernández-Hierro JM. 2015. Use of near infrared hyperspectral tools for the screening of extractable polyphenols in red grape skins. Food Chem. 172:559–564.doi:10.1016/j.foodchem.2014.09.112.

Nyalala I, Okinda C, Nyalala L, Makange N, Chao Q, Chao L, Yousaf K, Chen K. 2019. Tomato volume and mass estimation using computer vision and machine learning algorithms: Cherry tomato model. J. Food Eng. 263:288–298.doi:10.1016/j.jfoodeng.2019.07.012.

Olarewaju OO, Bertling I, Magwaza LS. 2016. Non-destructive evaluation of avocado fruit maturity using near infrared spectroscopy and PLS regression models. Sci. Hortic. (Amsterdam). 199:229–236.doi:10.1016/j.scienta.2015.12.047.

Oliveira] GA [de, Bureau S, Renard CM-GC, Pereira-Netto AB, Castilhos] F [de. 2014. Comparison of NIRS approach for prediction of internal quality traits in three fruit species. Food Chem. 143:223–230.doi:https://doi.org/10.1016/j.foodchem.2013.07.122.

Oo LM, Aung NZ. 2018. A simple and efficient method for automatic strawberry shape and size estimation and classification. Biosyst. Eng. 170:96–107.doi:10.1016/j.biosystemseng.2018.04.004.

Pan L, Zhang Q, Zhang W, Sun Y, Hu P, Tu K. 2016. Detection of cold injury in peaches by hyperspectral reflectance imaging and artificial neural network. Food Chem. 192:134–141.doi:10.1016/j.foodchem.2015.06.106.

Pearson K. 1901. LIII. On lines and planes of closest fit to systems of points in space. London, Edinburgh, Dublin Philos. Mag. J. Sci. 2(11):559–572.doi:10.1080/14786440109462720.

Penchaiya P, Tijskens LMM, Uthairatanakij A, Srilaong V, Tansakul A, Kanlayanarat S. 2020. Modelling quality and maturity of ‘Namdokmai Sithong’ mango and their variation during storage. Postharvest Biol. Technol. 159:111000.doi:10.1016/j.postharvbio.2019.111000.

Phate VR, Malmathanraj R, Palanisamy P. 2019. Classification and weighing of sweet lime (Citrus limetta) for packaging using computer vision system. J. Food Meas. Charact. 13(2):1451–1468.doi:10.1007/s11694-019-00061-3.

Plazzotta S, Manzocco L, Nicoli MC. 2017. Fruit and vegetable waste management and the challenge of fresh-cut salad. Trends Food Sci. Technol. 63:51–59.doi:10.1016/j.tifs.2017.02.013.

Sabzi S, Abbaspour-Gilandeh Y, García-Mateos G. 2018. A new approach for visual identification of orange varieties using neural networks and metaheuristic algorithms. Inf. Process. Agric. 5(1):162–172.doi:10.1016/j.inpa.2017.09.002.

Sabzi S, Javadikia H, Arribas JI. 2020. A three-variety automatic and non-intrusive computer vision system for the estimation of orange fruit pH value. Measurement. 152:107298.doi:10.1016/j.measurement.2019.107298.

Saldaña E, Siche R, Luján M, Quevedo R. 2013. Review: computer vision applied to the inspection and quality control of fruits and vegetables. Brazilian J. Food Technol. 16(4):254–272.doi:10.1590/S1981-67232013005000031.

Sanaeifar A, Mohtasebi SS, Ghasemi-Varnamkhasti M, Ahmadi H. 2016. Application of MOS based electronic nose for the prediction of banana quality properties. Measurement. 82:105–114.doi:10.1016/j.measurement.2015.12.041.

Santos Pereira LF, Barbon S, Valous NA, Barbin DF. 2018. Predicting the ripening of papaya fruit with digital imaging and random forests. Comput. Electron. Agric. 145:76–82.doi:10.1016/j.compag.2017.12.029.

Shen F, Zhang B, Cao C, Jiang X. 2018. On-line discrimination of storage shelf-life and prediction of post-harvest quality for strawberry fruit by visible and near infrared spectroscopy. J. Food Process Eng. 41(7):e12866.doi:10.1111/jfpe.12866.

Siedliska A, Baranowski P, Zubik M, Mazurek W, Sosnowska B. 2018. Detection of fungal infections in strawberry fruit by VNIR/SWIR hyperspectral imaging. Postharvest Biol. Technol. 139:115–126.doi:10.1016/j.postharvbio.2018.01.018.

Sivakumar D, Jiang Y, Yahia EM. 2011. Maintaining mango (Mangifera indica L.) fruit quality during the export chain. Food Res. Int. 44(5):1254–1263.doi:10.1016/j.foodres.2010.11.022.

Sofu MM, Er O, Kayacan MC, Cetişli B. 2016. Design of an automatic apple sorting system using machine vision. Comput. Electron. Agric. 127:395–405.doi:10.1016/j.compag.2016.06.030.

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. J. Food Compos. Anal. 88:103437.doi:10.1016/j.jfca.2020.103437.

Srivastava S, Sadisatp S. 2016. Development of a low cost optimized handheld embedded odor sensing system (HE-Nose) to assess ripeness of oranges. J. Food Meas. Charact. 10(1):1–15.doi:10.1007/s11694-015-9270-3.

Sun Y, Lu R, Lu Y, Tu K, Pan L. 2019. Detection of early decay in peaches by structured-illumination reflectance imaging. Postharvest Biol. Technol. 151:68–78.doi:10.1016/j.postharvbio.2019.01.011.

Tan K, Lee WS, Gan H, Wang S. 2018. Recognising blueberry fruit of different maturity using histogram oriented gradients and colour features in outdoor scenes. Biosyst. Eng. 176:59–72.doi:10.1016/j.biosystemseng.2018.08.011.

Tan W, Sun L, Yang F, Che W, Ye D, Zhang D, Zou B. 2018. Study on bruising degree classification of apples using hyperspectral imaging and GS-SVM. Optik (Stuttg). 154:581–592.doi:10.1016/j.ijleo.2017.10.090.

Tucker G, Yin X, Zhang A, Wang M, Zhu Q, Liu X, Xie X, Chen K, Grierson D. 2017. Ethylene. Food Qual. Saf. 1(4):253–267.doi:10.1093/fqsafe/fyx024.

Voss HGJ, Stevan SL, Ayub RA. 2019. Peach growth cycle monitoring using an electronic nose. Comput. Electron. Agric. 163:104858.doi:10.1016/j.compag.2019.104858.

Wang Jiahua, Wang Jun, Chen Z, Han D. 2017. Development of multi-cultivar models for predicting the soluble solid content and firmness of European pear ( Pyrus communis L.) using portable vis–NIR spectroscopy. Postharvest Biol. Technol. 129:143–151.doi:10.1016/j.postharvbio.2017.03.012.

Wang Z, Hu M, Zhai G. 2018. Application of Deep Learning Architectures for Accurate and Rapid Detection of Internal Mechanical Damage of Blueberry Using Hyperspectral Transmittance Data. Sensors. 18(4):1126.doi:10.3390/s18041126.

Wei X, Zhang Y, Wu D, Wei Z, Chen K. 2018. Rapid and Non-Destructive Detection of Decay in Peach Fruit at the Cold Environment Using a Self-Developed Handheld Electronic-Nose System. Food Anal. Methods. 11(11):2990–3004.doi:10.1007/s12161-018-1286-y.

Wu A, Zhu J, Ren T. 2020. Detection of apple defect using laser-induced light backscattering imaging and convolutional neural network. Comput. Electr. Eng. 81:106454.doi:10.1016/j.compeleceng.2019.106454.

Wu D, Meng L, Yang L, Wang J, Fu X, Du X, Li S, He Y, Huang L. 2019. Feasibility of Laser-Induced Breakdown Spectroscopy and Hyperspectral Imaging for Rapid Detection of Thiophanate-Methyl Residue on Mulberry Fruit. Int. J. Mol. Sci. 20(8):2017.doi:10.3390/ijms20082017.

Wu L, He J, Liu G, Wang S, He X. 2016. Detection of common defects on jujube using Vis-NIR and NIR hyperspectral imaging. Postharvest Biol. Technol. 112:134–142.doi:10.1016/j.postharvbio.2015.09.003.

Xia Y, Huang W, Fan S, Li J, Chen L. 2019. Effect of spectral measurement orientation on online prediction of soluble solids content of apple using Vis/NIR diffuse reflectance. Infrared Phys. Technol. 97:467–477.doi:10.1016/j.infrared.2019.01.012.

Xu X, Xu H, Xie L, Ying Y. 2019. Effect of measurement position on prediction of apple soluble solids content (SSC) by an on-line near-infrared (NIR) system. J. Food Meas. Charact. 13(1):506–512.doi:10.1007/s11694-018-9964-4.

Yang X, Zhang R, Zhai Z, Pang Y, Jin Z. 2019. Machine learning for cultivar classification of apricots (Prunus armeniaca L.) based on shape features. Sci. Hortic. (Amsterdam). 256:108524.doi:10.1016/j.scienta.2019.05.051.

Yang Y-C, Sun D-W, Wang N-N. 2015. Rapid detection of browning levels of lychee pericarp as affected by moisture contents using hyperspectral imaging. Comput. Electron. Agric. 113:203–212.doi:10.1016/j.compag.2015.02.008.

Yu X, Lu H, Wu D. 2018. Development of deep learning method for predicting firmness and soluble solid content of postharvest Korla fragrant pear using Vis/NIR hyperspectral reflectance imaging. Postharvest Biol. Technol. 141:39–49.doi:10.1016/j.postharvbio.2018.02.013.

Zhang B, Fan Shuxiang, Li J, Huang W, Zhao C, Qian M, Zheng L. 2015. Detection of Early Rottenness on Apples by Using Hyperspectral Imaging Combined with Spectral Analysis and Image Processing. Food Anal. Methods. 8(8):2075–2086.doi:10.1007/s12161-015-0097-7.

Zhang B, Huang W, Gong L, Li J, Zhao C, Liu C, Huang D. 2015. Computer vision detection of defective apples using automatic lightness correction and weighted RVM classifier. J. Food Eng. 146:143–151.doi:10.1016/j.jfoodeng.2014.08.024.

Zhang B, Huang W, Wang C, Gong L, Zhao C, Liu C, Huang D. 2015. Computer vision recognition of stem and calyx in apples using near-infrared linear-array structured light and 3D reconstruction. Biosyst. Eng. 139:25–34.doi:10.1016/j.biosystemseng.2015.07.011.

Zhang B, Li Jiangbo, Fan S, Huang W, Zhao C, Liu C, Huang D. 2015. Hyperspectral imaging combined with multivariate analysis and band math for detection of common defects on peaches (Prunus persica). Comput. Electron. Agric. 114:14–24.doi:10.1016/j.compag.2015.03.015.

Zhang C, Zhao C, Huang W, Wang Q, Liu S, Li J, Guo Z. 2017. Automatic detection of defective apples using NIR coded structured light and fast lightness correction. J. Food Eng. 203:69–82.doi:10.1016/j.jfoodeng.2017.02.008.

Zhang D, Xu L, Wang Q, Tian X, Li J. 2019. The Optimal Local Model Selection for Robust and Fast Evaluation of Soluble Solid Content in Melon with Thick Peel and Large Size by Vis-NIR Spectroscopy. Food Anal. Methods. 12(1):136–147.doi:10.1007/s12161-018-1346-3.

Zhang D, Xu Y, Huang W, Tian X, Xia Y, Xu L, Fan S. 2019. Nondestructive measurement of soluble solids content in apple using near infrared hyperspectral imaging coupled with wavelength selection algorithm. Infrared Phys. Technol. 98:297–304.doi:10.1016/j.infrared.2019.03.026.

Zhang S, Wu X, Zhang Shuhui, Cheng Q, Tan Z. 2017. An effective method to inspect and classify the bruising degree of apples based on the optical properties. Postharvest Biol. Technol. 127:44–52.doi:10.1016/j.postharvbio.2016.12.008.

Zhang Y, Lee WS, Li M, Zheng L, Ritenour MA. 2018. Non-destructive recognition and classification of citrus fruit blemishes based on ant colony optimized spectral information. Postharvest Biol. Technol. 143:119–128.doi:10.1016/j.postharvbio.2018.05.004.

Zhao Y, Zhang C, Zhu S, Li Y, He Y, Liu F. 2020. Shape induced reflectance correction for non-destructive determination and visualization of soluble solids content in winter jujubes using hyperspectral imaging in two different spectral ranges. Postharvest Biol. Technol. 161:111080.doi:10.1016/j.postharvbio.2019.111080.

ZHOU H, YE Z, YU Z, SU M, DU J. 2016. Application of Low-Field Nuclear Magnetic Resonance and Proton Magnetic Resonance Imaging in Evaluation of Jinxiu’ Yellow Peach’s Storage Suitability. Emirates J. Food Agric. 28(9):633.doi:10.9755/ejfa.2016-03-244.

Zhu X, Fang L, Gu J, Guo W. 2016. Feasibility Investigation on Determining Soluble Solids Content of Peaches Using Dielectric Spectra. Food Anal. Methods. 9(6):1789–1798.doi:10.1007/s12161-015-0348-7.

Zou L, Ming S, Zhang D. 2015. A New Method for Rapid Detection of the Volume and Quality of Watermelon Based on Processing of X-Ray Images. hlm. 731–738.




DOI: https://doi.org/10.21107/agrointek.v15i1.7810

Refbacks

  • There are currently no refbacks.




Copyright (c) 2021 Ali Khumaidi

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Indexed by

Crossref logo

Mailing Address:

Agrointek: Jurnal Teknologi Industri Pertanian

Department of Agroindustrial Technology, Faculty of Agriculture, University of Trunojoyo Madura
Raya Telang St. PO BOX 2, Kamal - Bangkalan - Jawa Timur
Phone: (031) 3013234, Email:agrointek@trunojoyo.ac.id

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License