{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,28]],"date-time":"2026-03-28T19:39:58Z","timestamp":1774726798438,"version":"3.50.1"},"reference-count":26,"publisher":"Tech Science Press","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.32604\/iasc.2023.026564","type":"journal-article","created":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T05:57:26Z","timestamp":1654495046000},"page":"1261-1275","source":"Crossref","is-referenced-by-count":16,"title":["Enhanced Disease Identification Model for Tea Plant Using Deep Learning"],"prefix":"10.32604","volume":"35","author":[{"given":"Santhana","family":"Krishnan Jayapal","sequence":"first","affiliation":[]},{"given":"Sivakumar","family":"Poruran","sequence":"additional","affiliation":[]}],"member":"17807","reference":[{"key":"ref1","series-title":"Proc. of Int. Conf. on Artificial Intelligence (IJCAI\u201916)","first-page":"1711","article-title":"Feature learning based deep supervised hashing with pairwise labels","author":"Li","year":"2016"},{"key":"ref2","doi-asserted-by":"crossref","first-page":"950","DOI":"10.1109\/TGRS.2017.2756911","article-title":"Large-scale remote sensing image retrieval by deep hashing neural networks","volume":"56","author":"Li","year":"2018","journal-title":"IEEE Transactions on Geoscience and Remote Sensing"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1080\/2150704X.2015.1074756","article-title":"High resolution remote-sensing imagery retrieval using sparse features by auto-encoder","volume":"6","author":"Zhou","year":"2015","journal-title":"Remote Sensing Letters"},{"key":"ref4","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1016\/j.neucom.2016.05.061","article-title":"Local structure learning in high resolution remote sensing image retrieval","volume":"207","author":"Du","year":"2016","journal-title":"Neurocomputing"},{"key":"ref5","doi-asserted-by":"crossref","first-page":"489","DOI":"10.3390\/rs9050489","article-title":"Learning low dimensional convolutional neural networks for high resolution remote sensing image retrieval","volume":"9","author":"Zhou","year":"2017","journal-title":"Remote Sensing"},{"key":"ref6","first-page":"975","article-title":"Tea leaf diseases recognition using neural network ensemble","volume":"114","author":"Karmokar","year":"2015","journal-title":"International Journal of Computer Applications"},{"key":"ref7","series-title":"Proc. of Int. Conf. on Signal Processing & Its Applications (CSPA)","first-page":"150","article-title":"Recognition and detection of tea leaf\u2019s diseases using support vector machine","author":"Hossain","year":"2018"},{"key":"ref8","series-title":"Proc. of Int. Conf. on Geoscience and Remote Sensing Symp. (IGARSS)","first-page":"469","article-title":"Active learning based autoencoder for hyperspectral imagery classification","author":"Sun","year":"2016"},{"key":"ref9","series-title":"Proc. of Int. Conf. on Innovations in Information, Embedded and Communication Systems (ICIIECS)","first-page":"1","article-title":"Detection of leaf diseases and classification using digital image processing","author":"Meena","year":"2017"},{"key":"ref10","series-title":"Int. Conf. on Computer Communication and Informatics (ICCCI)","first-page":"1","article-title":"A modern approach for plant leaf disease classification which depends on leaf image processing","author":"Dhaware","year":"2017"},{"key":"ref11","series-title":"Proc. of Int. Conf. on Computing Communication Control and Automation","first-page":"768","article-title":"Plant disease detection using image processing","author":"Khirade","year":"2015"},{"key":"ref12","series-title":"Proc. of Int. Conf. on Security, Pattern Analysis, and Cybernetics (SPAC)","first-page":"304","article-title":"Image recognition of Tea leaf diseases based on convolutional neural network","author":"Sun","year":"2018"},{"key":"ref13","series-title":"Proc. of Int. Conf. on Orange Technologies (ICOT)","first-page":"64","article-title":"Unsupervised change detection for remote sensing images based on object-based mrf and stacked autoencoders","author":"Li","year":"2016"},{"key":"ref14","series-title":"Proc. of Int. Conf. on Industrial Informatics-Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII)","first-page":"145","article-title":"Land-use classification with remote sensing image based on stacked autoencoder","author":"Ding","year":"2016"},{"key":"ref15","series-title":"Proc. of Symp. on Neural Networks and Applications (NEUREL)","first-page":"1","article-title":"Analysis of color space quantization in split-brain autoencoder for remote sensing image classification","author":"Stojnic","year":"2018"},{"key":"ref16","series-title":"Proc. of Int. Conf. on Computer Vision and Pattern Recognition Workshops","first-page":"496","article-title":"Generalized autoencoder: A neural network framework for dimensionality reduction","author":"Wang","year":"2014"},{"key":"ref17","series-title":"Proc. of Int. Conf. on Machine Learning and Applications (ICMLA)","first-page":"773","article-title":"Denoising auto-encoder with recurrent skip connections and residual regression for music source separation","author":"Liu","year":"2016"},{"key":"ref18","series-title":"Proc. of Int. Conf. on Artificial Intelligence and Big Data (ICAIBD)","first-page":"205","article-title":"A hybrid deep learning model for consumer credit scoring","author":"Zhu","year":"2018"},{"key":"ref19","series-title":"Int. Conf. on Business, Engineering and Industrial Applications","first-page":"203","article-title":"Comparative analysis of PCA and LDA","author":"Borade","year":"2011"},{"key":"ref20","series-title":"Proc. of Int. Conf. on Data Mining Workshops (ICDMW)","first-page":"1080","article-title":"Dimension reduction on open data using variational autoencoder","author":"Lee","year":"2018"},{"key":"ref21","series-title":"Proc. of Second Int. Conf. on Artificial Intelligence and Knowledge Engineering (AIKE)","first-page":"211","article-title":"Empirical comparison between autoencoders and traditional dimensionality reduction methods","author":"Fournier","year":"2019"},{"key":"ref22","series-title":"Proc. of Conf. on Computer Vision and Pattern Recognition","first-page":"557","article-title":"Hashing with binary autoencoders","author":"Carreira-Perpin\u00e1n","year":"2015"},{"key":"ref23","series-title":"Proc. of AAAI Conf. on Artificial Intelligence","article-title":"Deep hashing network for efficient similarity retrieval","author":"Zhu","year":"2016"},{"key":"ref24","series-title":"Proc. of Int. Conf. on Pattern Recognition (ICPR)","first-page":"2935","article-title":"Skip-connected deep convolutional autoencoder for restoration of document images","author":"Zhao","year":"2018"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1007\/s11263-019-01174-4","article-title":"Deep supervised hashing for fast image retrieval","volume":"127","author":"Liu","year":"2019","journal-title":"International Journal Computer Vision"},{"key":"ref26","series-title":"Proc. of Int. Conf. on Computer Vision and Pattern Recognition (CVPR)","first-page":"37","article-title":"Supervised discrete hashing","author":"Shen","year":"2015"}],"container-title":["Intelligent Automation &amp; Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.techscience.com\/ueditor\/files\/iasc\/TSP_IASC-35-1\/TSP_IASC_26564\/TSP_IASC_26564.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T21:28:39Z","timestamp":1733520519000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/iasc\/v35n1\/48143"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":26,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.32604\/iasc.2023.026564","relation":{},"ISSN":["1079-8587"],"issn-type":[{"value":"1079-8587","type":"print"}],"subject":[],"published":{"date-parts":[[2023]]}}}