Comparison of some of the data mining Algorithms in classifying charleston gray watermelon variety using morphological properties

Paper Details

Research Paper 01/11/2017
Views (237) Download (17)
current_issue_feature_image
publication_file

Comparison of some of the data mining Algorithms in classifying charleston gray watermelon variety using morphological properties

Amir Alipasandi, Asghar Mahmoudi, Hossein Behfar, Hossein Ghaffari
J. Bio. Env. Sci.11( 5), 258-267, November 2017.
Certificate: JBES 2017 [Generate Certificate]

Abstract

It is predicted that crops like watermelon will be decreased in coming years and price of this product in the market will increase. This highlights the necessity of measures to choose high-quality watermelons by the final consumer. Also according to the concept of virtual water, sorted and more desirable watermelons can be exported at higher prices in order to create more benefit. In general, the aim of this study is to provide a measure that is based on Charleston Gray watermelon morphological characteristics and evaluation of the classification ratio in unripe, ripped and overripe classes, by data mining algorithms. The results of the sensory evaluation showed that experts (human) were able to classify 52% of the samples correctly. The correct classification algorithm K Nearest Neighbor was significantly higher than the classification of LVQ Neural Networks and Discriminant Analysis but classification results of different distance metrics of this algorithm showed no significant difference using them. The highest correct classification with the amount of 67.3 percent belonged to Support Vector Machine algorithm with Gaussian kernel function. Although at first glance it may seem that, this amount is far from ideal but it should be noted that this amount is 15% higher than the classification made by humans. Incidentally, this classification was done based on morphological characteristics of samples which measuring them does not require sophisticated tools and methods.

VIEWS 10

Abbaszadeh R, Rajabipour A, Delshad M, Mahjub M, Ahmadi H, Lague C. 2011. Application of vibration response for the nondestructive ripeness evaluation of watermelons, Australian Journal of Crop Science 5, 920-925.

Abbaszadeh R, Rajabipour A, Labbafi R, Ahmadi H. 2012. Prediction of watermelon customer-friendly based on sensory evaluation data using expert fuzzy model. Proceedings of the 7th National Congress of Agricultural Engineering (Biosystems Mechanics) and Mechanization. Sep 4-6. Shiraz. Iran. (in Farsi) Antonucci, Francesca, Corrado Costa, Federico Pallottino, Graziella Paglia, Valentina Rimatori, Donato De Giorgio, and Paolo Menesatti. 2012. Quantitative method for shape description of almond cultivars (Prunus amygdalus Batsch), Food and Bioprocess Technology 5, 768-785.

Anon. 2013. FAO Food and Nutrition Series. Statistical database http://faostat. fao.org.

Blasco JN, Aleixos S, Cubero J, Gómez-Sanchís E, Moltó. 2009. Automatic sorting of satsuma (Citrus unshiu) segments using computer vision and morphological features, Computers and Electronics in Agriculture 66, 1-8.

Bourne, Malcolm. 2002. Food texture and viscosity: concept and measurement (Academic press).

Brewer, Marin Talbot, Jennifer B Moyseenko, Antonio J Monforte, Esther van der Knaap. 2007. Morphological variation in tomato: a comprehensive study of quantitative trait loci controlling fruit shape and development, Journal of Experimental Botany 58, 1339-1349.

Choudhary R, Jayas Paliwal, DS, Jayas. 2008. Classification of cereal grains using wavelet, morphological, colour, and textural features of non-touching kernel images, Biosystems Engineering 99, 330-337.

Currie AJ, Ganeshanandam S, Noiton DA, Garrick D, Shelbourne CJA, Oraguzie N. 2000. Quantitative evaluation of apple (Malus× domestica Borkh.) fruit shape by principal component analysis of Fourier descriptors, Euphytica 111, 221-227.

Diezma-Iglesias B, Ruiz-Altisent M, Barreiro P. 2004. Detection of Internal Quality in Seedless Watermelon by Acoustic Impulse Response, Biosystems Engineering 88, 221-30.

Ding, Wei, Hirohisa Nesumi, Yasushi Takano, Yasuo Ukai. 2000. Quantitative evaluation of the three-dimensional fruit shape and size of Citrus species based on spherical harmonic descriptors, Euphytica 114, 103-115.

Ghazanfari A, Irudayaraj J, Kusalik A, Romaniuk M. 1997. Machine vision grading of pistachio nuts using Fourier descriptors, Journal of agricultural engineering research 68, 247-252.

Han, Jiawei, Jian Pei, Micheline Kamber. 2011. Data mining: concepts and techniques (Elsevier). Menesatti, Paolo, Corrado Costa, Graziella Paglia, Federico Pallottino, Stefano D’Andrea, Valentina Rimatori, and Jacopo Aguzzi. 2008. Shape-based methodology for multivariate discrimination among Italian hazelnut cultivars, Biosystems Engineering 101, 417-424.

Koc, Ali Bulent. 2007. Determination of watermelon volume using ellipsoid approximation and image processing, Postharvest Biology and Technology 45, 366-371.

Morimoto T, Takeuchi T, Miyata H, Hashimoto Y. 2000. Pattern recognition of fruit shape based on the concept of chaos and neural networks, Computers and Electronics in Agriculture 26, 171-186.

Muramatsu, Noboru, Keiichi Tanaka, Toshikazu Asakura, Yuko Ishikawa-Takano, Naoki Sakurai, Naoki Wada, Ryoichi Yamamoto, and Donald J Nevins. 1997. Critical comparison of an accelerometer and a laser Doppler vibrometer for measuring fruit firmness, HortTechnology 7, 434-438.

Ngouajio, Mathieu, William Kirk, Ronald Goldy. 2003. A simple model for rapid and nondestructive estimation of bell pepper fruit volume, HortScience 38, 509-511.

Nourain, Jamal, Yibin B, Ying, Jianping Wang, Xiuqin Rao. 2004. Determination of acoustic vibration in watermelon by finite element modeling. In Optics East 213-223. International Society for Optics and Photonics.

Nunome, Tsukasa, Keizo Ishiguro, Tatemi Yoshida, Masashi Hirai. 2001. Mapping of fruit shape and color development traits in eggplant (Solanum melongena L.) based on RAPD and AFLP markers, Breeding science 51, 19-26.

Rabiei B, Valizadeh M, Ghareyazie B, Moghaddam M, Ali AJ. 2004. Identification of QTLs for rice grain size and shape of Iranian cultivars using SSR markers, Euphytica 137, 325-332.

Sadrnia, Hassan, Ali Rajabipour, Ali Jafary, Arzhang Javadi, Younes Mostofi. 2007. Classification and analysis of fruit shapes in long type watermelon using image processing, Int. J. Agric. Biol 1, 68-70.

Stone ML, Armstrong PR, Zhang X, Brusewitz GH, Chen DD. 1996. Watermelon maturity determination in the field using acoustic impulse impedance techniques, Transactions of the ASAE 39, 2325-2330.

Taniwaki, Mitsuru, Takanori Hanada, and Naoki Sakurai. 2009. Postharvest quality evaluation of “Fuyu” and “Taishuu” persimmons using a nondestructive vibrational method and an acoustic vibration technique, Postharvest Biology and Technology 51, 80-85.

Xiao, Han, Ning Jiang, Erin Schaffner, Eric J Stockinger, and Esther van der Knaap. 2008. A retrotransposon-mediated gene duplication underlies morphological variation of tomato fruit, science 319, 1527-1530.

Xiaobo, Zou, Zhao Jiewen, Li Yanxiao, Shi Jiyong, Yin Xiaoping. 2008. Apples shape grading by Fourier expansion and genetic program algorithm. In Natural computation, 2008. ICNC’08. Fourth International Conference on 85-90.

Yadav, BK, Jindal VK. 2001. Monitoring milling quality of rice by image analysis, Computers and Electronics in Agriculture 33, 19-33.

Yamamoto, Hiromichi, Mutsuo Iwamoto, and Shiko Haginuma. 1980. Acoustic impulse response method for measuring natural frequency of intact fruits and preliminary applications to internal quality evaluation of apples and watermelons, Journal of Texture Studies 11, 117-136.

Zheng TQ, Xu JL, Li ZK, Zhai HQ, Wan JM. 2007. Genomic regions associated with milling quality and grain shape identified in a set of random introgression lines of rice (Oryza sativa L.), Plant Breeding 126, 158-163.

Zygier S, Ben Chaim A, Efrati A, Kaluzky G, Borovsky Y, Paran I. 2005. QTLs mapping for fruit size and shape in chromosomes 2 and 4 in pepper and a comparison of the pepper QTL map with that of tomato, Theoretical and Applied Genetics 111, 437-445.