Determining the Carrot Volume via Radius and Length Using ANN

Mustafa Nevzat Örnek, Humar Kahramanli
  • Mustafa Nevzat Örnek
    Selcuk University, Turkey


In this study a total of 464 carrots were taken from Kaşınhanı, where the most carrots are produces in Turkey. The length and radiuses with an interval of 5 cm and volume were measured and recorded. Three different Artificial Neural Network models: BP, LM and PUNN were designed for predicting the carrot volume. To assess the success of the system, statistical measures such as Root Mean Squared Error, Mean Absolute Error and R2 were used. The results were showed that all three methods are successful in this problem, while LM and PUNN seems bit.


Carrot; carrots physical properties; ANN; PUNN; BP; LM

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Submitted: 2018-04-24 21:27:08
Published: 2018-06-29 14:38:56
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Z.-G. Chen, X.-Y. Guo, T. Wu, “A novel dehydration technique for carrot slices implementing ultrasound and vacuum drying methods”, Ultrason. Sonochem., 30 (2016), pp. 28-34

K. Górnicki, A. Kaleta “Drying curve modelling of blanched carrot cubes under natural convection condition”, J. Food Eng., 82 (2007), pp. 160-170

J. Frias, E. Penas, M. Ullate, C. Vidal-Valverde, “Influence of drying by convective air dryer or power ultrasound on the vitamin C and beta-carotene content of carrots”, J. Agric. Food Chem., 58 (2010), pp. 10539-10544

B. Bao, K.C. Chang, “Carrot juice color, carotenoids, and nonstarchy polysaccharides as affected by processing conditions” , J. Food Sci., 59 (1995), pp. 1155-1158

J.L. Bureau, R.J. Bushway, “HPLC determination of carotenoids in fruits and vegetables in the United States”, J. Food Sci., 51 (1986), pp. 128-130

M. Soltani, R. Alimardani and M. Omid, “Modeling the Main Physical Properties of Banana Fruit Based on Geometrical Attributes”, International Journal of Multidisciplinary Sciences and Engineering, Vol. 2, No. 2, May 2011.

K. Vursavus, H. Kelebek and S. Selli, “A study on some chemical and physico-mechanic properties of three sweet cherry varieties (Prunus avium L.) in Turkey”, Journal of Food Engineering 74 (2006) 568–575.

J. D. Bustos-Vanegas, P. C. Corrêa, M. A. Martins, F. M. Baptestini, R. C. Campos, G. H. Horta de Oliveira, E. H. Martins Nunes, “Developing predictive models for determining physical properties of coffee beans during the roasting process”, Industrial Crops and Products, doi:

S. Munder, D. Argyropoulos and J. Muller, “Class-based physical properties of air-classified sunflower seeds and kernels”, Biosystems Engineering Volume 164, December 2017, Pages 124-134

M. Radunić, M. Jupic Špika, S. Goreta Ban, J. Gadže, J. C. Díaz-Pérez, D. MacLean, “Physical and chemical properties of pomegranate fruit accessions from Croatia”, Food Chemistry, Volume 177, 15 June 2015, Pages 53-60

R. Przybylski and R. C. Zambiazi, “Predicting Oxidative Stability of Vegetable Oils Using Neural Network System and Endogenous Oil Components”, JAOCS, Vol. 77, no. 9 (2000)

K. Movagharnejad and M. Nikzad, “Modeling of tomato drying using artificial neural network”, Computers and Electronics in Agriculture 59 (2007) 78–85.

Ş. Taşdemir, B. Yanıktepe and A. B. Guher, “Determination of Wind Potential of a Specific Region using Artificial Neural Networks”, International Journal of Intelligent Systems and Applications in Engineering, 2017, 5(3), 158-162

A. Kayabasi, “MLP and KNN Algorithm Model Applications for Determining the Operating Frequency of A-Shaped Patch Antennas”, International Journal of Intelligent Systems and Applications in Engineering, 2017, 5(3), 154-157.

M. Mahmudul Alam Mia, S. Kumar Biswas, M. Chowdhury Urmi and A. Siddique, “An Algorithm For Training Multilayer Perceptron (MLP) For Image Reconstruction Using Neural Network Without Overfitting”, International Journal of Scientific & Technology Research Volume 4, Issue 02, 2015

M. Gori and A. Tesi, “On the Problem of Local Minima in Backpropagation”, IEEE Transactions on Pattern Analysis and Machine Intelligence 14 (1), 76-86.

R. Durbin and D. E. Rumelhart, “Product Units: A Computationally Powerful and Biologically Plausible Extension to Backpropagation Networks”, Neural Computation, Volume 1 | Issue 1 | Spring 1989 p.133-142

A. Ismail and A.P. Engelbrecht, “Training product units in feedforward neural networks using particle swarm optimization”, Proceedings of the International Conference on Artificial Intelligence, Durban, South Africa, 1999, pp 36-40.

Y. Shi and R. C. Eberhart, “Empirical Study of Particle Swarm Optimization,” in Proceedings of the Congress on Evolutionary Computation, (Washington D.C, USA), pp. 1945–1949, July 1999.

F. van den Bergh, A.P. Engelbrecht, “Training Product Unit Networks using Cooperative Particle Swarm Optimizers”, International Joint Conference on Neural Networks, 2001.

H. Kahramanli, “Training Product-Unit Neural Networks with Cuckoo Optimization Algorithm for Classification”, International Journal of Intelligent Systems and Applications in Engineering, 2017, 5(4), 252-255.

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