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Predictions of the Apple Bruise Volume on the Basis of Impact Energy or Maximum Contact Force Using Adaptive Neuro-Fuzzy Inference System (ANFIS)


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ABEDI, G. – AHMADI E. 2013. Design and evaluation a pendulum device to study postharvest mechanical damage in fruits: Bruise modeling of red delicious apple. In Australian Jourdan of Crop Science, vol. 7, no. 7, pp. 962–968.Search in Google Scholar

AHMADI, E. – BARIKLOO, H. – KASHFI, M. 2016. Viscoelastic finite element analysis of the dynamic behavior of apple under impact loading with regard to its different layers. In Computers and Electronics in Agriculture, vol. 121, pp. 1–11.10.1016/j.compag.2015.11.017Search in Google Scholar

AHMADI, E. – GHASSEMZADEH, H. R. – SADEGHI, M. – MOGHADDAM, M. – ZARIFNESHAT, S. 2010. The effect of impact and fruit properties on the bruising of peach. In Journal of Food Engineering, vol. 97, pp. 110–117.10.1016/j.jfoodeng.2009.09.024Search in Google Scholar

AMIRYOUSEFI, M. R. – MOHEBBI, M. – KHODAIYAN, F. – ASADI, S. 2011. An empowered adaptive neuro-fuzzy inference system using self-organizing map clustering to predict mass transfer kinetics in deep-fat frying of ostrich meat plates. In Computers and Electronics in Agriculture, vol. 76, pp. 89–95.10.1016/j.compag.2011.01.008Search in Google Scholar

AY, M. – KISI, O. 2014. Modelling of chemical oxygen demand by using ANNs, ANFIS and k-means clustering techniques. In Journal of Hydrology, vol. 511, pp. 279–289.10.1016/j.jhydrol.2014.01.054Search in Google Scholar

ALTUG, S. – CHOW, M. Y. – TRUSSELL, H. J. 1999. Fuzzy inference systems implemented on neural architectures for motor fault detection and diagnosis. In IEEE Transactions on Industrial Electronics, vol. 46, no. 6, pp. 1069–1079.10.1109/41.807988Search in Google Scholar

BARREIRO, P. – STEINMETZ, V. – RUIZ-ALTISENT, M. 1997. Neural bruise prediction models for fruit handling and machinery evaluation. In Computers and Electronics in Agriculture, vol. 8, pp. 91–103.10.1016/S0168-1699(97)00022-7Search in Google Scholar

CHEN, P. – SUN, Z. 1981. Impact parameters related to postharvest bruising of apples. ASAE Paper No. 81, pp. 3041, St. Joseph, Michigan.Search in Google Scholar

DIEZMA, B. – VALERO, C. – GARCIA-RAMOS, F. J. – RUIZ-ALTISENT, M. 2006. Monitoring of firmness evolution of peaches during storage by combining acoustic and impact methods. In Journal of Food Engineering, vol. 77, pp. 926–935.10.1016/j.jfoodeng.2005.08.021Search in Google Scholar

DU, C. J. – SUN, D. W. 2006. Learning techniques used in computer vision for food quality evaluation: a review. In Journal of Food Engineering, vol. 72, no. 1, pp. 39–55.10.1016/j.jfoodeng.2004.11.017Search in Google Scholar

EFFENDI, Z. – RAMLI, R. – GHANI, J. A. 2010. A back propagation neural networks for grading Jatropha curcas fruits maturity. In American Journal of Applied Sciences, vol. 7, no. 3, pp. 390–394.10.3844/ajassp.2010.390.394Search in Google Scholar

JANG, J. S. R. – SUN, C. T. 1997. Neuro-fuzzy modeling and control. In Proceedings of IEEE, vol. 83, no. 3, pp. 378–405.10.1109/5.364486Search in Google Scholar

JHIN, C. – HWANG, K. T. 2014. Prediction of radical scavenging activities of anthocyanins applying adaptive neuro-fuzzy inference system (ANFIS) with quantum chemical descriptors. In International Journal of Molecular Sciences, vol. 15, pp. 14715–14727.10.3390/ijms150814715Search in Google Scholar

KARAMAN, S. – KAYACIER, A. 2011. Effect of temperature on rheological characteristics of molasses: Modeling of apparent viscosity using Adaptive Neuro-Fuzzy Inference System (ANFIS). In LWT–Food Sciences, vol. 44, pp. 1717–1725.10.1016/j.lwt.2011.03.004Search in Google Scholar

KHOSHHAL, A. – ALIZADEH, A. – ETEMADI, A. – ZERESHKI, S. 2010. Artificial neural network modeling of apple drying process. In Journal of Food Process Engineering, vol. 33, pp. 298–313.10.1111/j.1745-4530.2009.00435.xSearch in Google Scholar

KHOSHNEVISAN, B. – RAFIEE, S. – OMID, M. – MOUSAZADEH, H. 2014. Prediction of potato yield based on energy inputs using multilayer adaptive neuro-fuzzy inference system. In Measurement, vol. 47, pp. 521–530.10.1016/j.measurement.2013.09.020Search in Google Scholar

KRUEGER, E. – PRIOR, S. A. – KURTENER, D. – ROGERS, H. H. – RUNION, G. B. 2011. Characterizing root distribution with adaptive neuro-fuzzy analysis. In International Agrophysics, vol. 25, pp. 93–96.Search in Google Scholar

LEWIS, R. – YOXALL, A. – MARSHALL, M. B. – CANTY, L. A. 2008. Characterising pressure and bruising in apple fruit. In Wear, vol. 264, pp. 37–46.10.1016/j.wear.2007.01.038Search in Google Scholar

MENESATTI, P. – PAGLIA, G. 2001. Development of a drop damage index of fruit resistance to damage. In Journal of Agricultural Engineering Research, vol. 80, pp. 53–64.10.1006/jaer.2000.0669Search in Google Scholar

MOHSENIN, N. N. 1986. Physical Properties of Plant and Animal Materials. Gordon and Breach Science Publishers, New York.Search in Google Scholar

MOINFAR, A. – SHAHGHOLI, G. 2019. The effect of tractor driving system type on its slip and rolling resistance and its modelling using ANFIS. In Acta Technologica Agriculturae, vol. 4, pp. 116–122.10.2478/ata-2019-0021Search in Google Scholar

ROTH, E. – KOVACS, E. – HERTOG, M. – VANSTREELS, E. – NICOLAI, B. 2005. Relationship between physical and biochemical parameters in apple softening. In Proceedings of the 5th International Postharvest Symposium (PS’05), ISHS, pp. 573–578.10.17660/ActaHortic.2005.682.72Search in Google Scholar

TAGHAVIFAR, H. – MARDANI, A. 2014. On the modeling of energy efficiency indices of agricultural tractor driving wheels applying adaptive neuro-fuzzy inference system. In Journal of Terramechanics, vol. 56, no.1, pp. 37–47.10.1016/j.jterra.2014.08.002Search in Google Scholar

VAN LINDEN, V. – SCHEERLINCK, N. – DESMET, M. – DE BAERDEMAEKER, J. 2006. Factors that affect tomato bruise development as a result of mechanical impact. In Postharvest Biology and Technology, vol. 42, pp. 260–270.10.1016/j.postharvbio.2006.07.001Search in Google Scholar

VAN ZEEBROECK, M. – VAN LINDEN, V. – DARIUS, P. – DE KETELAERE, B. – RAMON, H. – TIJSKENS, E. 2007. The effect of fruit factors on the bruise susceptibility of apples. In Postharvest Biology and Technology, vol. 46, pp.10–19.10.1016/j.postharvbio.2007.03.017Search in Google Scholar

ZHENG, H. – FANG, S. S. – LOU, H. Q. – CHEN, Y. – JIANG, L. L. – LU, H. F. 2011. Neural network prediction of ascorbic acid degradation in green asparagus during thermal treatments. In Expert Systems with Applications, vol. 38, pp. 5591–5602.10.1016/j.eswa.2010.10.076Search in Google Scholar

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