{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T08:20:54Z","timestamp":1775118054562,"version":"3.50.1"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T00:00:00Z","timestamp":1599177600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T00:00:00Z","timestamp":1599177600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"published-print":{"date-parts":[[2021,1]]},"DOI":"10.1007\/s11042-020-09567-1","type":"journal-article","created":{"date-parts":[[2020,9,4]],"date-time":"2020-09-04T13:02:46Z","timestamp":1599224566000},"page":"753-771","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":116,"title":["Tea leaf disease detection using multi-objective image segmentation"],"prefix":"10.1007","volume":"80","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4532-6245","authenticated-orcid":false,"given":"Somnath","family":"Mukhopadhyay","sequence":"first","affiliation":[]},{"given":"Munti","family":"Paul","sequence":"additional","affiliation":[]},{"given":"Ramen","family":"Pal","sequence":"additional","affiliation":[]},{"given":"Debashis","family":"De","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,9,4]]},"reference":[{"key":"9567_CR1","unstructured":"Anthony G, Greg H, Tshilidzi M (2007) Classification of images using support vector machines. arXiv preprint arXiv:0709.3967"},{"issue":"1","key":"9567_CR2","first-page":"211","volume":"15","author":"S Arivazhagan","year":"2013","unstructured":"Arivazhagan S, Shebiah R N, Ananthi S, Varthini S V (2013) Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features. Agricultural Engineering International: CIGR Journal 15 (1):211\u2013217","journal-title":"Agricultural Engineering International: CIGR Journal"},{"issue":"2","key":"9567_CR3","doi-asserted-by":"publisher","first-page":"646","DOI":"10.1137\/S0895479893249757","volume":"16","author":"M Bakonyi","year":"1995","unstructured":"Bakonyi M, Johnson C R (1995) The euclidian distance matrix completion problem. SIAM Journal on Matrix Analysis and Applications 16(2):646\u2013654","journal-title":"SIAM Journal on Matrix Analysis and Applications"},{"key":"9567_CR4","doi-asserted-by":"crossref","unstructured":"Boser B E, Guyon I M, Vapnik V N (1992) A training algorithm for optimal margin classifiers. In: Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory. ACM Press, pp 144\u2013152","DOI":"10.1145\/130385.130401"},{"key":"9567_CR5","doi-asserted-by":"crossref","unstructured":"Chen J, Liu Q, Gao L (2019) Visual tea leaf disease recognition using a convolutional neural network model. Symmetry, 11(3). https:\/\/www.mdpi.com\/2073-8994\/11\/3\/343","DOI":"10.3390\/sym11030343"},{"issue":"21","key":"9567_CR6","doi-asserted-by":"publisher","first-page":"28483","DOI":"10.1007\/s11042-018-6005-6","volume":"77","author":"SS Chouhan","year":"2018","unstructured":"Chouhan S S, Kaul A, Singh U P (2018) Soft computing approaches for image segmentation: a survey. Multimedia Tools and Applications 77 (21):28483\u201328537. https:\/\/doi.org\/10.1007\/s11042-018-6005-6","journal-title":"Multimedia Tools and Applications"},{"issue":"3","key":"9567_CR7","first-page":"41","volume":"2","author":"JQ Dae Gwan Kim","year":"2009","unstructured":"Dae Gwan Kim J Q, Bulanon D M (2009) Classification of grapefruit peel diseases using color texture feature analysis. International Journal of Agricultural & Biological Engineering 2(3):41\u201350","journal-title":"International Journal of Agricultural & Biological Engineering"},{"issue":"2","key":"9567_CR8","doi-asserted-by":"publisher","first-page":"182","DOI":"10.1109\/4235.996017","volume":"6","author":"K Deb","year":"2002","unstructured":"Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: Nsga-ii. IEEE Trans Evol Comput 6 (2):182\u2013197. https:\/\/doi.org\/10.1109\/4235.996017","journal-title":"IEEE Trans Evol Comput"},{"key":"9567_CR9","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1016\/j.procs.2016.05.262","volume":"85","author":"AK Dey","year":"2016","unstructured":"Dey A K, Sharma M, Meshram MR (2016) Image processing based leaf rot disease, detection of betel vine (piper betlel.) Procedia Computer Science 85:748\u2013754. https:\/\/doi.org\/10.1016\/j.procs.2016.05.262. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S1877050916306123. International Conference on Computational Modelling and Security (CMS 2016)","journal-title":"Procedia Computer Science"},{"issue":"2","key":"9567_CR10","doi-asserted-by":"publisher","first-page":"267","DOI":"10.3923\/itj.2011.267.275","volume":"10","author":"MB Dheeb Al Bashish","year":"2011","unstructured":"Dheeb Al Bashish M B, Bani-Ahmad S (2011) Detection and classification of leaf diseases using k-means-based segmentation and neural-networks-based classification. Inf Technol J 10(2):267\u2013 275","journal-title":"Inf Technol J"},{"issue":"15","key":"9567_CR11","doi-asserted-by":"publisher","first-page":"19951","DOI":"10.1007\/s11042-017-5445-8","volume":"77","author":"G Dhingra","year":"2018","unstructured":"Dhingra G, Kumar V, Joshi H D (2018) Study of digital image processing techniques for leaf disease detection and classification. Multimedia Tools and Applications 77(15):19951\u201320000. https:\/\/doi.org\/10.1007\/s11042-017-5445-8","journal-title":"Multimedia Tools and Applications"},{"issue":"7","key":"9567_CR12","doi-asserted-by":"publisher","first-page":"3516","DOI":"10.1109\/TIP.2019.2898567","volume":"28","author":"X Dong","year":"2019","unstructured":"Dong X, Shen J, Wu D, Guo K, Jin X, Porikli F (2019) Quadruplet network with one-shot learning for fast visual object tracking. IEEE Trans Image Process 28(7):3516\u20133527","journal-title":"IEEE Trans Image Process"},{"key":"9567_CR13","doi-asserted-by":"crossref","unstructured":"Duan K-B, Keerthi S S (2005) Which is the best multiclass svm method? an empirical study. In: Oza N C, Polikar R, Kittler J, Roli F (eds) Multiple Classifier Systems. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 278\u2013285","DOI":"10.1007\/11494683_28"},{"key":"9567_CR14","unstructured":"El-Helly M, Rafea A A, El-Gammal S (2003) An integrated image processing system for leaf disease detection and diagnosis. In: Proceedings of the 1st Indian International Conference on Artificial Intelligence, IICAI 2003, Hyderabad, India, December 18-20, 2003, pp 1182\u20131195"},{"issue":"102","key":"9567_CR15","first-page":"36","volume":"1989","author":"DE Golberg","year":"1989","unstructured":"Golberg D E (1989) Genetic algorithms in search, optimization, and machine learning. Addion wesley 1989(102):36","journal-title":"Addion wesley"},{"key":"9567_CR16","doi-asserted-by":"crossref","unstructured":"Goldberg D E, Deb K (1991) A comparative analysis of selection schemes used in genetic algorithms, Foundations of Genetic Algorithms, vol 1. Elsevier. http:\/\/www.sciencedirect.com\/science\/article\/pii\/B9780080506845500082","DOI":"10.1016\/B978-0-08-050684-5.50008-2"},{"issue":"3","key":"9567_CR17","doi-asserted-by":"publisher","first-page":"354","DOI":"10.1016\/j.inpa.2018.05.002","volume":"5","author":"K Golhani","year":"2018","unstructured":"Golhani K, Balasundram S K, Vadamalai G, Pradhan B (2018) A review of neural networks in plant disease detection using hyperspectral data. Information Processing in Agriculture 5 (3):354\u2013371. https:\/\/doi.org\/10.1016\/j.inpa.2018.05.002. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S2214317317301774","journal-title":"Information Processing in Agriculture"},{"key":"9567_CR18","doi-asserted-by":"crossref","unstructured":"Hossain M S, Mou R M, Hasan M M, Chakraborty S, Razzak M A (2018) Recognition and detection of tea leaf\u2019s diseases using support vector machine. In: 2018 IEEE 14th International Colloquium on Signal Processing & Its Applications (CSPA), IEEE, pp 150\u2013154","DOI":"10.1109\/CSPA.2018.8368703"},{"issue":"7","key":"9567_CR19","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1037\/h0071325","volume":"24","author":"H Hotelling","year":"1933","unstructured":"Hotelling H (1933) Analysis of a complex of statistical variables into principal components. J Educ Psychol 24(7):417\u2013441","journal-title":"J Educ Psychol"},{"key":"9567_CR20","doi-asserted-by":"publisher","unstructured":"Kalaivani S, Shantharajah S P, Padma T (2019) Agricultural leaf blight disease segmentation using indices based histogram intensity segmentation approach. Multimedia Tools and Applications. https:\/\/doi.org\/10.1007\/s11042-018-7126-7","DOI":"10.1007\/s11042-018-7126-7"},{"key":"9567_CR21","doi-asserted-by":"crossref","unstructured":"Kanungo T, Mount D M, Netanyahu N S, Piatko C D, Silverman R, Wu A Y (2002) An efficient k-means clustering algorithm: analysis and implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, pp 881\u2013892","DOI":"10.1109\/TPAMI.2002.1017616"},{"key":"9567_CR22","doi-asserted-by":"crossref","unstructured":"Karmokar B C, Ullah M S, Siddiquee M K, Alam K M R (2015) Tea leaf diseases recognition using neural network ensemble. International Journal of Computer Applications, 114(17)","DOI":"10.5120\/20071-1993"},{"key":"9567_CR23","doi-asserted-by":"crossref","unstructured":"Keller J M, Gray M R, Givens J A (1985) A fuzzy k-nearest neighbor algorithm. IEEE transactions on systems, man, and cybernetics, (4), pp 580\u2013585","DOI":"10.1109\/TSMC.1985.6313426"},{"key":"9567_CR24","doi-asserted-by":"crossref","unstructured":"Khirade S D, Patil AB (2015) Plant disease detection using image processing. In: 2015 International conference on computing communication control and automation, IEEE, pp 768\u2013771","DOI":"10.1109\/ICCUBEA.2015.153"},{"key":"9567_CR25","unstructured":"Knowles J, Corne D (1999) The pareto archived evolution strategy: a new baseline algorithm for pareto multiobjective optimisation. In: Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), vol 1, pp 98\u2013105 Vol. 1"},{"issue":"2","key":"9567_CR26","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1109\/TEVC.2008.925798","volume":"13","author":"H Li","year":"2009","unstructured":"Li H, Zhang Q (2009) Multiobjective optimization problems with complicated pareto sets, moea\/d and nsga-ii. IEEE transactions on evolutionary computation 13(2):284\u2013302","journal-title":"IEEE transactions on evolutionary computation"},{"key":"9567_CR27","doi-asserted-by":"publisher","first-page":"3351","DOI":"10.1109\/TIP.2019.2959256","volume":"29","author":"Z Liang","year":"2020","unstructured":"Liang Z, Shen J (2020) Local semantic siamese networks for fast tracking. IEEE Trans Image Process 29:3351\u20133364","journal-title":"IEEE Trans Image Process"},{"issue":"5","key":"9567_CR28","doi-asserted-by":"publisher","first-page":"10491","DOI":"10.4249\/scholarpedia.10491","volume":"7","author":"T Lindeberg","year":"2012","unstructured":"Lindeberg T (2012) Scale Invariant Feature Transform. Scholarpedia 7(5):10491. https:\/\/doi.org\/10.4249\/scholarpedia.10491 revision#153939","journal-title":"Scholarpedia"},{"key":"9567_CR29","doi-asserted-by":"crossref","unstructured":"Liu Y, Li Z, Xiong H, Gao X, Wu J (2010) Understanding of internal clustering validation measures. In: 2010 IEEE International Conference on Data Mining, pp 911\u2013916","DOI":"10.1109\/ICDM.2010.35"},{"issue":"2","key":"9567_CR30","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1109\/TIT.1982.1056489","volume":"28","author":"S Lloyd","year":"1982","unstructured":"Lloyd S (1982) Least squares quantization in pcm. IEEE Trans Inf Theory 28(2):129\u2013137","journal-title":"IEEE Trans Inf Theory"},{"key":"9567_CR31","doi-asserted-by":"crossref","unstructured":"Lu X, Wang W, Ma C, Shen J, Shao L, Porikli F (2019) See more, know more: Unsupervised video object segmentation with co-attention siamese networks. In: 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp 3618\u20133627","DOI":"10.1109\/CVPR.2019.00374"},{"key":"9567_CR32","doi-asserted-by":"crossref","unstructured":"Lu X, Wang W, Shen J, Tai Y-W, Crandall D J, Hoi S C H (2020) Learning video object segmentation from unlabeled videos. ArXiv abs\/2003.05020","DOI":"10.1109\/CVPR42600.2020.00898"},{"key":"9567_CR33","unstructured":"MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics. https:\/\/projecteuclid.org\/euclid.bsmsp\/1200512992. University of California Press, Berkeley, Calif., pp 281\u2013297"},{"key":"9567_CR34","unstructured":"Madzarov G, Gjorgjevikj D, Chorbev I (2009) A multi-class svm classifier utilizing binary decision tree. Informatica, 33(2)"},{"issue":"1","key":"9567_CR35","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.compag.2011.03.008","volume":"77","author":"SK Mathanker","year":"2011","unstructured":"Mathanker SK, Weckler PR, Bowser TJ, Wang N, Maness NO (2011) Adaboost classifiers for pecan defect classification. Comput Electron Agric 77(1):60\u201368. https:\/\/doi.org\/10.1016\/j.compag.2011.03.008. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S016816991100069X","journal-title":"Comput Electron Agric"},{"key":"9567_CR36","doi-asserted-by":"crossref","unstructured":"Mohan K J, Balasubramanian M, Palanivel S (2016) Detection and recognition of diseases from paddy plant leaf images. International Journal of Computer Applications, 144(12)","DOI":"10.5120\/ijca2016910505"},{"key":"9567_CR37","unstructured":"Mukhopadhyay S, Mandal J K (2013) Adaptive median filtering based on unsupervised classification of pixels. IGI Global, 701 E. Chocolate Ave., Hershey, PA 17033, USA"},{"issue":"4","key":"9567_CR38","first-page":"158","volume":"3","author":"S Mukhopadhyay","year":"2013","unstructured":"Mukhopadhyay S, Mandal J K (2013) Denoising of digital images through pso based pixel classification. Central European Journal of Computer Science, Springer Vienna 3(4):158\u2013172","journal-title":"Central European Journal of Computer Science, Springer Vienna"},{"issue":"4","key":"9567_CR39","doi-asserted-by":"publisher","first-page":"1132","DOI":"10.1109\/TGRS.2008.2008182","volume":"47","author":"A Mukhopadhyay","year":"2009","unstructured":"Mukhopadhyay A, Maulik U (2009) Unsupervised pixel classification in satellite imagery using multiobjective fuzzy clustering combined with svm classifier. IEEE Trans Geosci Remote Sens 47(4):1132\u20131138. https:\/\/doi.org\/10.1109\/TGRS.2008.2008182","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"9567_CR40","doi-asserted-by":"crossref","unstructured":"Na S, Xumin L, Yong G (2010) Research on k-means clustering algorithm: An improved k-means clustering algorithm. In: 2010 Third International Symposium on Intelligent Information Technology and Security Informatics, pp 63\u201367","DOI":"10.1109\/IITSI.2010.74"},{"key":"9567_CR41","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1142\/S0218001405004083","volume":"19","author":"M Omran","year":"2005","unstructured":"Omran M, Enge;brecht A, Salman A (2005) Particle swarm optimization method for image clustering. Int J Pattern Recognit Artif Intell 19:297\u2013322","journal-title":"Int J Pattern Recognit Artif Intell"},{"key":"9567_CR42","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1016\/j.measurement.2014.05.033","volume":"55","author":"E Omrani","year":"2014","unstructured":"Omrani E, Khoshnevisan B, Shamshirband S, Saboohi H, Anuar N B, Nasir M H N M (2014) Potential of radial basis function-based support vector regression for apple disease detection. Measurement 55:512\u2013519. https:\/\/doi.org\/10.1016\/j.measurement.2014.05.033. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0263224114002541","journal-title":"Measurement"},{"key":"9567_CR43","doi-asserted-by":"crossref","unstructured":"Padol P B, Yadav A A (2016) Svm classifier based grape leaf disease detection. In: 2016 Conference on advances in signal processing (CASP), IEEE, pp 175\u2013179","DOI":"10.1109\/CASP.2016.7746160"},{"issue":"9","key":"9567_CR44","doi-asserted-by":"publisher","first-page":"1277","DOI":"10.1016\/0031-3203(93)90135-J","volume":"26","author":"NR Pal","year":"1993","unstructured":"Pal N R, Pal S K (1993) A review on image segmentation techniques. Pattern recognition 26(9):1277\u20131294","journal-title":"Pattern recognition"},{"issue":"7","key":"9567_CR45","doi-asserted-by":"publisher","first-page":"1616","DOI":"10.1109\/TCYB.2015.2453091","volume":"46","author":"J Peng","year":"2016","unstructured":"Peng J, Shen J, Li X (2016) High-order energies for stereo segmentation. IEEE Transactions on Cybernetics 46(7):1616\u20131627","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"3","key":"9567_CR46","first-page":"460","volume":"2","author":"S Phadikar","year":"2012","unstructured":"Phadikar S, Sil J, Das A K (2012) Classification of rice leaf diseases based on morphological changes. International Journal of Information and Electronics Engineering 2(3):460\u2013463","journal-title":"International Journal of Information and Electronics Engineering"},{"issue":"1","key":"9567_CR47","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.compag.2010.06.009","volume":"74","author":"T Rumpf","year":"2010","unstructured":"Rumpf T, Mahlein A-K, Steiner U, Oerke E-C, Dehne H-W, Pl\u00fcmer L (2010) Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and electronics in agriculture 74(1):91\u201399","journal-title":"Computers and electronics in agriculture"},{"key":"9567_CR48","unstructured":"Salehi M, Haniyeh R (2019) A fuzzy multi-objective model for allocating orders to suppliers under shortfall and price-quantity discounts: An mpso and nsga-ii with tuned parameters. International Journal of Industiral Engineering & Production Research, 30(2). http:\/\/ijiepr.iust.ac.ir\/article-1-731-en.html"},{"issue":"9","key":"9567_CR49","doi-asserted-by":"publisher","first-page":"2637","DOI":"10.1109\/TNNLS.2018.2885591","volume":"30","author":"J Shen","year":"2019","unstructured":"Shen J, Dong X, Peng J, Jin X, Shao L, Porikli F (2019) Submodular function optimization for motion clustering and image segmentation. IEEE Transactions on Neural Networks and Learning Systems 30(9):2637\u20132649","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"7","key":"9567_CR50","doi-asserted-by":"publisher","first-page":"1088","DOI":"10.1109\/TCSVT.2014.2302545","volume":"24","author":"J Shen","year":"2014","unstructured":"Shen J, Du Y, Li X (2014) Interactive segmentation using constrained laplacian optimization. IEEE Transactions on Circuits and Systems for Video Technology 24(7):1088\u20131100","journal-title":"IEEE Transactions on Circuits and Systems for Video Technology"},{"issue":"10","key":"9567_CR51","doi-asserted-by":"publisher","first-page":"4911","DOI":"10.1109\/TIP.2017.2722691","volume":"26","author":"J Shen","year":"2017","unstructured":"Shen J, Peng J, Dong X, Shao L, Porikli F (2017) Higher order energies for image segmentation. IEEE Trans Image Process 26(10):4911\u20134922","journal-title":"IEEE Trans Image Process"},{"issue":"6","key":"9567_CR52","doi-asserted-by":"publisher","first-page":"2688","DOI":"10.1109\/TIP.2018.2795740","volume":"27","author":"J Shen","year":"2018","unstructured":"Shen J, Peng J, Shao L (2018) Submodular trajectories for better motion segmentation in videos. IEEE Trans Image Process 27(6):2688\u20132700","journal-title":"IEEE Trans Image Process"},{"issue":"24","key":"9567_CR53","doi-asserted-by":"publisher","first-page":"26647","DOI":"10.1007\/s11042-016-4191-7","volume":"76","author":"S Shrivastava","year":"2017","unstructured":"Shrivastava S, Singh S K, Hooda D S (2017) Soybean plant foliar disease detection using image retrieval approaches. Multimedia Tools and Applications 76(24):26647\u201326674. https:\/\/doi.org\/10.1007\/s11042-016-4191-7","journal-title":"Multimedia Tools and Applications"},{"issue":"1","key":"9567_CR54","doi-asserted-by":"publisher","first-page":"41","DOI":"10.1016\/j.inpa.2016.10.005","volume":"4","author":"V Singh","year":"2017","unstructured":"Singh V, Misra AK (2017) Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture 4(1):41\u201349. https:\/\/doi.org\/10.1016\/j.inpa.2016.10.005. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S2214317316300154","journal-title":"Information Processing in Agriculture"},{"key":"9567_CR55","doi-asserted-by":"crossref","unstructured":"Sun X, Mu S, Xu Y, Cao Z, Su T (2019) Image recognition of tea leaf diseases based on convolutional neural network. ArXiv abs\/1901.02694","DOI":"10.1109\/SPAC46244.2018.8965555"},{"key":"9567_CR56","doi-asserted-by":"crossref","unstructured":"Sun X, Mu S, Xu Y, Cao Z, Su T (2019) Image recognition of tea leaf diseases based on convolutional neural network. arXiv preprint arXiv:1901.02694","DOI":"10.1109\/SPAC46244.2018.8965555"},{"key":"9567_CR57","unstructured":"Vapnik V (1998) Statistical learning theory. Hoboken, NJ: Wiley"},{"issue":"7","key":"9567_CR58","doi-asserted-by":"publisher","first-page":"1531","DOI":"10.1109\/TPAMI.2018.2840724","volume":"41","author":"W Wang","year":"2019","unstructured":"Wang W, Shen J, Ling H (2019) A deep network solution for attention and aesthetics aware photo cropping. IEEE Transactions on Pattern Analysis and Machine Intelligence 41(7):1531\u20131544","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"issue":"1","key":"9567_CR59","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1016\/0169-7439(87)80084-9","volume":"2","author":"S Wold","year":"1987","unstructured":"Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemometr Intell Lab Syst 2 (1):37\u201352. https:\/\/doi.org\/10.1016\/0169-7439(87)80084-9. http:\/\/www.sciencedirect.com\/science\/article\/pii\/0169743987800849. Proceedings of the Multivariate Statistical Workshop for Geologists and Geochemists","journal-title":"Chemometr Intell Lab Syst"},{"key":"9567_CR60","doi-asserted-by":"crossref","unstructured":"Wong M T, He X, Yeh W-C (2011June) Image clustering using particle swarm optimization Evolutionary Computation (CEC), 2011 IEEE Congress on, pp 262\u2013268","DOI":"10.1109\/CEC.2011.5949627"},{"key":"9567_CR61","doi-asserted-by":"crossref","unstructured":"Yao Q, Guan Z, Zhou Y, Tang J, Hu Y, Yang B (2009) Application of support vector machine for detecting rice diseases using shape and color texture features. In: 2009 international conference on engineering computation, IEEE, pp 79\u201383","DOI":"10.1109\/ICEC.2009.73"},{"key":"9567_CR62","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1016\/j.compag.2017.01.014","volume":"134","author":"S Zhang","year":"2017","unstructured":"Zhang S, Wu X, You Z, Zhang L (2017) Leaf image based cucumber disease recognition using sparse representation classification. Comput Electron Agric 134:135\u2013141. https:\/\/doi.org\/10.1016\/j.compag.2017.01.014. http:\/\/www.sciencedirect.com\/science\/article\/pii\/S0168169917300820","journal-title":"Comput Electron Agric"},{"key":"9567_CR63","doi-asserted-by":"publisher","unstructured":"Zhu J, Wu A, Wang X, Zhang H (2019) Identification of grape diseases using image analysis and bp neural networks. Multimedia Tools and Applications. https:\/\/doi.org\/10.1007\/s11042-018-7092-0","DOI":"10.1007\/s11042-018-7092-0"},{"key":"9567_CR64","unstructured":"Zitzler E, Laumanns M, Thiele L (2001) Spea2: Improving the strength pareto evolutionary algorithm"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09567-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-020-09567-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-020-09567-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,4]],"date-time":"2021-09-04T02:17:37Z","timestamp":1630721857000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-020-09567-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,4]]},"references-count":64,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,1]]}},"alternative-id":["9567"],"URL":"https:\/\/doi.org\/10.1007\/s11042-020-09567-1","relation":{},"ISSN":["1380-7501","1573-7721"],"issn-type":[{"value":"1380-7501","type":"print"},{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,4]]},"assertion":[{"value":"10 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 July 2020","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 August 2020","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 September 2020","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}