Fuzzy classification of pre-harvest tomatoes for ripeness estimation - An approach based on automatic rule learning using decision tree

Applied Soft Computing

Volumn 36, Issue , 2015, Pages 45-56

  Goel, Nidhi ^a   Sehgal, Priti ^b  

^a UNIVERSITY OF DELHI   (India)

^b UNIVERSITY OF DELHI   (India)

Author keywords

Automatic rule learning; Decision trees; Fuzzy rule based classification; Image retrieval; Machine vision; Tomato ripeness

Indexed keywords

AGRICULTURAL ROBOTS; CLASSIFICATION (OF INFORMATION); COLOR; COLORIMETRY; COMPUTER VISION; DECISION TREES; FRUITS; FUZZY RULES; IMAGE RETRIEVAL; RANDOM FORESTS; ROBOTS; STATISTICAL TESTS;

AUTOMATICALLY GENERATED; CLASSIFICATION ACCURACY; FUZZY RULE BASED; FUZZY RULE BASED CLASSIFICATION SYSTEMS; MAMDANI FUZZY INFERENCES; RECOGNITION OF OBJECTS; RULE LEARNING; TOMATO RIPENESS;

FUZZY INFERENCE;

EID: 84938634597     PISSN: 15684946     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.asoc.2015.07.009     Document Type: Article

References (48)

1. H. Ji, and J. Yuan The application study of apple color grading by particle swarm optimization neural networks The Sixth World Congress on Intelligent Control and Automation, WCICA, IEEE 2006 2651 2654
2. K. Tu, K. Ren, L. Pan, and H. Li A study of broccoli grading system based on machine vision and neural networks International Conference on Mechatronics and Automation, ICMA 2007, IEEE 2007 2332 2336
3. Y. Chherawala, R. Lepage, and G. Doyon Food grading/sorting based on color appearance trough machine vision: the case of fresh cranberries Information and Communication Technologies 2006, ICTTA'06, 2nd, IEEE 2006 1540 1545
4. W.M. Syahrir, A. Suryanti, and C. Connsynn Color grading in tomato maturity estimator using image processing technique 2nd IEEE International Conference on Computer Science and Information Technology, 2009, ICCSIT, IEEE 2009 276 280
5. F.Y.A. Rahman, S.R.M.S. Baki, A. Yassin, N.M. Tahir, and W.I.W. Ishak Monitoring of watermelon ripeness based on fuzzy logic WRI World Congress on Computer Science and Information Engineering, IEEE 2009 67 70
6. N.B.A. Mustafa, N.A. Fuad, S.K. Ahmed, A.A.Z. Abidin, Z. Ali, W.B. Yit, and Z.A.M. Sharrif Image processing of an agriculture produce: determination of size and ripeness of a banana International Symposium on Information Technology, ITSim, IEEE 2008 1 7
7. D.-J. Lee, J.K. Archibald, Y.-C. Chang, and C.R. Greco Robust color space conversion and color distribution analysis techniques for date maturity evaluation J. Food Eng. 88 2008 364 372
8. M.S. Reid Maturation and maturity indices. Postharvest technology of horticultural crops vol. 3 2002 55 62
9. K. Choi, G. Lee, Y. Han, and J. Bunn Tomato maturity evaluation using color image analysis Trans. ASAE 38 1995 171 176
10. U.D.o. Agriculture United States standards for grades of fresh tomatoes 1997
11. N. Kondo, and S. Endo Studies on fruit recognizing visual sensor J. Jpn. Soc. Agric. Mach. 49 1987 476 482
12. N. Kondo, and Y. Shibano Studies on image recognition of fruit vegetables (Part 1) J. Soc. Agric. Mach. Jpn. 53 1991 52 58
13. M. Monta, N. Kondo, and K. Ting End-effectors for tomato harvesting robot Artificial Intelligence for Biology and Agriculture 1998 Springer 1 25
14. Y. Wang, X. Zhu, and C. Ji Machine vision based cotton recognition for cotton harvesting robot Computer And Computing Technologies In Agriculture vol. II 2008 Springer 1421 1425
15. A. Gastélum-Barrios, R.A. Bórquez-López, E. Rico-García, M. Toledano-Ayala, and G.M. Soto-Zarazúa Tomato quality evaluation with image processing Afr. J. Agri. Res. 6 2011 3333 3339
16. X. Liming, and Z. Yanchao Automated strawberry grading system based on image processing Comput. Electron. Agric. 71 2010 S32 S39
17. Y. Zhang, X. Yin, T. Xu, and J. Zhao On-line sorting maturity of cherry tomato bymachine vision Computer and Computing Technologies in Agriculture II vol. 3 2009 Springer 2223 2229
18. H. Gu, Y. Lu, J. Lou, and W. Zhang Recognition and location of fruit objects based on machine vision Z. Pan, A. Cheok, M. Haller, R.H. Lau, H. Saito, R. Liang, (Eds.), Advances in Artificial Reality and Tele-Existence 2006 Springer Berlin, Heidelberg 785 795
19. S. Hayashi, K. Ganno, Y. Ishii, and I. Tanaka Robotic harvesting system for eggplants Jpn Agric. Res. Q. 36 2002 163 168
20. R. Xiang, Y. Ying, and H. Jiang Research on image segmentation methods of tomato in natural conditions 4th International Congress on Image and Signal Processing (CISP), IEEE 2011 1268 1272
21. J. Yin, H. Mao, Y. Hu, X. Wang, and S. Chen An automatic segmentation method for multi-tomatoes image under complicated natural background Asia-Pacific Remote Sensing Symposium, International Society for Optics and Photonics 2006 641118
22. H. Yin, Y. Chai, S.X. Yang, and G.S. Mitta Ripe tomato extraction for a harvesting robotic system Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics, IEEE Press San Antonio, TX, USA 2009 2984 2989
23. X. Wang, H. Mao, X. Han, and J. Yin Vision-based judgment of tomato maturity under growth conditions Afr. J. Biotechnol. 10 2013 3616 3623
24. A. Arefi, and A.M. Motlagh Development of an expert system based on wavelet transform and artificial neural networks for the ripe tomato harvesting robot Aust. J. Crop Sci. 7 2013 699
25. N. Goel, and P. Sehgal Adaptive segmentation technique for recognizing tomatoes at different maturity stages International Conference on Computer, Control, Informatics and its Applications (IC3INA 2014), IEEE Indonesia Section Indonesia 2014
26. H. Stokman, and T. Gevers Selection and fusion of color models for image feature detection IEEE Transactions on Pattern Analysis and Machine Intelligence 29 2007 371 381
27. D.-J. Lee, J.K. Archibald, and G. Xiong Rapid color grading for fruit quality evaluation using direct color mapping IEEE Transactions on Automation Science and Engineering 8 2011 292 302
28. Y. Gejima, H. Zhang, and M. Nagata Judgment on level of maturity for tomato quality using L∗a∗b∗color image processing Proceedings. 2003 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM, IEEE 2003 1355 1359
29. M.S. Iraji, and A. Tosinia Classification tomatoes on machine vision with fuzzy the mamdani inference, adaptive neuro fuzzy inference system based (Anfis-Sugeno) J. Appl. Sci. Res. 7 2011
30. Y. Takahashi, J. Ogawa, and K. Saeki Automatic tomato picking robot system with human interface using image processing IECON'01, Industrial Electronics Society, The 27th Annual Conference of the IEEE 2001 433 438
31. Y. JianJun, M. HanPing, W. XinZhong, and S. BaoGuo Judgment model on maturity of harvesting-tomato for robot under natural conditions Nongye Jixie Xuebao = Transactions of the Chinese Society for Agricultural Machinery 40 2009 146 150 168
32. C.-J. Du, and D.-W. Sun Learning techniques used in computer vision for food quality evaluation: a review J. Food Eng. 72 2006 39 55
33. M. Howarth, and S. Searcy Comparison of Bayesian and neural network classifiers for grading carrots Paper-American Society of Agricultural Engineers USA 1991
34. Y.-R. Chen, K. Chao, and M.S. Kim Machine vision technology for agricultural applications Comput. Electron. Agric. 36 2002 173 191
35. E. Elhariri, N. El-Bendary, M.M.M. Fouad, J. Platoš, A.E. Hassanien, and A.M. Hussein Multi-class SVM Based Classification Approach for Tomato Ripeness, Innovations in Bio-inspired Computing and Applications 2014 Springer 175 186
36. M. Shahin, E. Tollner, and R. McClendon AE - Automation and Emerging Technologies: artificial Intelligence Classifiers for sorting Apples based on Watercore J. Agric. Eng. Res. 79 2001 265 274
37. N. Alavi Quality determination of mozafati dates using mamdani fuzzy inference system J. Saudi Soc. Agric. Sci. 12 2013 137 142
38. A.R. Mansor, M. Othman, M.N.A. Bakar, K.A. Ahmad, and T.R. Razak Fuzzy ripening mango index using RGB colour sensor model Res. World 5 2014 1
39. G. Jahns, H.M. Nielsen, and W. Paul Measuring image analysis attributes and modelling fuzzy consumer aspects for tomato quality grading Comput. Electron. Agric. 31 2001 17 29
40. J.M. Mendel Computing with words and its relationships with fuzzistics Inf. Sci. 177 2007 988 1006
41. H. Ishibuchi Classification and Modeling with Linguistic Information Granules: Advanced Approaches to Linguistic Data Mining 2009
42. L. Rokach Data mining with decision trees: theory and applications World Sci. 2008
43. W. Yi, M. Lu, and Z. Liu Multi-valued attribute and multi-labeled data decision tree algorithm Int. J. Mach. Learn. Cyber. 2 2011 67 74
44. E.H. Mamdani, and S. Assilian An experiment in linguistic synthesis with a fuzzy logic controller Int. J. Man-Mach. Stud. 7 1975 1 13
45. F. Ba-Alwi Knowledge Acquisition Tool for Learning Membership Function and Fuzzy Classification Rules from Numerical Data 2013
46. J.R. Quinlan C4. 5: Programs for Machine Learning 1993
47. I.H. Witten, E. Frank, and M.A. Hall Data Mining: Practical Machine Learning Tools and Techniques 2011 Morgan Kaufmann San Francisco (retrieved)
48. M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I.H. Witten The WEKA data mining software: an update ACM SIGKDD Explorations Newsletter 11 2009 10 18