{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T11:10:18Z","timestamp":1783077018257,"version":"3.54.6"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"7","license":[{"start":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T00:00:00Z","timestamp":1618790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022,7,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>The diseases in plants pose a devastating impact on initiating safety in the production of food and they can lead to a reduction in the quantity and quality of agricultural products. In most cases, plant diseases lead to no grain harvest. Thus, an automatic diagnosis of plant disease is highly recommended for determining agricultural information. Several techniques are devised for plant disease detection wherein deep learning is preferred due to its effective performance. Novel deep learning is presented to spot disease from rice crop images. Here, the rice plant image undergoes pre-processing to remove noise and artifacts contained in the image. Then, the segmentation is performed with Segmentation Network (SegNet) to produce segments. The segments are further adapted for extracting statistical features, convolution neural network (CNN) features and texture features. These features are employed for plant disease detection wherein the deep recurrent neural network (Deep RNN) is utilized. The Deep RNN is trained with the proposed RideSpider Water Wave (RSW) algorithm. The proposed RSW is devised by integrating RWW in Spider monkey optimization. The proposed RWS-based Deep RNN provides superior performance with the highest accuracy of 90.5%, maximal sensitivity of 84.9% and maximal specificity of 95.2%.<\/jats:p>","DOI":"10.1093\/comjnl\/bxab022","type":"journal-article","created":{"date-parts":[[2021,3,10]],"date-time":"2021-03-10T12:11:27Z","timestamp":1615378287000},"page":"1812-1825","source":"Crossref","is-referenced-by-count":72,"title":["Deep Neural Network for Disease Detection in Rice Plant Using the Texture and Deep Features"],"prefix":"10.1093","volume":"65","author":[{"given":"T","family":"Daniya","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering , , Chennai, India"},{"name":"Sathyabama Institute of Science and Technology , , Chennai, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S","family":"Vigneshwari","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering , , Chennai, India"},{"name":"Sathyabama Institute of Science and Technology , , Chennai, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,4,19]]},"reference":[{"key":"2022071813385448800_ref1","first-page":"768","volume-title":"Proc. Int. Conf. Computing Communication Control and Automation","author":"Khirade","year":"2015"},{"key":"2022071813385448800_ref2","first-page":"1","volume-title":"Proc. IEEE International Conference on Current Trends in Advanced Computing (ICCTAC)","author":"Shah","year":"2016"},{"key":"2022071813385448800_ref3","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13593-014-0246-1","article-title":"Advanced methods of plant disease detection. A review","volume":"35","author":"Martinelli","year":"2015","journal-title":"Agron. Sustain. Dev."},{"key":"2022071813385448800_ref4","first-page":"114","article-title":"An overview on detection and classification of plant diseases in image processing","volume":"3","author":"Rishi","year":"2015","journal-title":"Int. J. Sci. Eng. Res."},{"key":"2022071813385448800_ref5","first-page":"80090","volume-title":"Proc. Third International Conference on Digital Image Processing (ICDIP 2011)","author":"Pugoy","year":"2011"},{"key":"2022071813385448800_ref6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.compag.2010.02.007","article-title":"A review of advanced techniques for detecting plant diseases","volume":"72","author":"Sankaran","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"2022071813385448800_ref7","volume-title":"International Rice Research Institute","author":"Rice Doctor"},{"key":"2022071813385448800_ref8","doi-asserted-by":"crossref","DOI":"10.13031\/2013.13703","volume-title":"Prototype system of automatic identification of cotton insect pest and intelligent decision based on machine vision","author":"Zhigang","year":"2003"},{"key":"2022071813385448800_ref9","first-page":"2215","article-title":"Remote sensing analysis of rice disease tresses for farm pest management using wide-band airborne data","author":"Qin","year":"2003"},{"key":"2022071813385448800_ref10","first-page":"85","volume-title":"2007 IEEE 10th Int. Conf. Information Technology","author":"Sanyal","year":"2007"},{"key":"2022071813385448800_ref11","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1146\/annurev.py.24.090186.001405","article-title":"Remote sensing of biotic and abiotic plant stress","volume":"24","author":"Jackson","year":"1986","journal-title":"Annu. Rev. Phytopathol."},{"key":"2022071813385448800_ref12","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1094\/PHYTO.2001.91.3.316","article-title":"Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners","volume":"91","author":"Kobayashi","year":"2001","journal-title":"Phytopathology"},{"key":"2022071813385448800_ref13","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.compag.2020.105280","article-title":"Predicting the hydrological response of a forest after wildfire and soil treatments using an artificial neural network","volume":"170","author":"Zema","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"2022071813385448800_ref14","first-page":"635","article-title":"LOOP descriptor: Encoding repeated local patterns for fine-grained visual identification of lepidoptera","volume":"25","author":"Chakraborti","year":"2018","journal-title":"arXiv"},{"key":"2022071813385448800_ref15","first-page":"1","article-title":"Applications of deep learning for dense scenes analysis in agriculture: a review","volume":"20","author":"Zhang","year":"2020","journal-title":"Sensors"},{"key":"2022071813385448800_ref16","doi-asserted-by":"crossref","DOI":"10.1016\/j.compag.2020.105393","article-title":"Using deep transfer learning for image-based plant disease identification","volume":"173","author":"Chen","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"2022071813385448800_ref17","article-title":"Rice grain disease identification using dual phase convolutional neural network-based system aimed at small dataset","author":"Ahmed","year":"2020","journal-title":"arXiv preprint arXiv"},{"key":"2022071813385448800_ref18","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.biosystemseng.2020.03.020","article-title":"Identification and recognition of rice diseases and pests using convolutional neural networks","volume":"194","author":"Rahman","year":"2020","journal-title":"Biosyst. Eng."},{"key":"2022071813385448800_ref19","doi-asserted-by":"crossref","first-page":"3246","DOI":"10.1002\/jsfa.10365","article-title":"Detection of rice plant diseases based on deep transfer learning","volume":"100","author":"Chen","year":"2020","journal-title":"J. Sci. Food Agric."},{"key":"2022071813385448800_ref20","doi-asserted-by":"crossref","first-page":"578","DOI":"10.3390\/s20030578","article-title":"A recognition method for rice plant diseases and pests video detection based on deep convolutional neural network","volume":"20","author":"Li","year":"2020","journal-title":"Sensors"},{"key":"2022071813385448800_ref21","first-page":"1","article-title":"Rice leaf blast disease detection using multi-level colour image thresholding","volume":"10","author":"Bakar","year":"2018","journal-title":"J. Telecommun. Electron. Comput. Eng."},{"key":"2022071813385448800_ref22","first-page":"67","article-title":"A multiclass deep convolutional neural network classifier for detection of common rice plant anomalies","volume":"9","author":"Atole","year":"2018","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"2022071813385448800_ref23","first-page":"31","article-title":"Application of machine learning in detection of blast disease in south Indian rice crops","author":"Ramesh","year":"2019","journal-title":"J. Phytology"},{"key":"2022071813385448800_ref24","first-page":"249","article-title":"Recognition and classification of paddy leaf diseases using optimized deep neural network with Jaya algorithm","volume":"7","author":"Ramesh","year":"2020","journal-title":"Inf. Process. Agric."},{"key":"2022071813385448800_ref25","article-title":"Rice-net: an efficient artificial fish swarm optimization applied deep convolutional neural network model for identifying the Oryza sativa diseases","author":"Raja Reddy","year":"2020","journal-title":"Neural Comput. Applic."},{"key":"2022071813385448800_ref26","first-page":"4111","volume-title":"AAAI\u201917: Proc. Thirty-First AAAI Conference on Artificial Intelligence","author":"Kwak","year":"2017"},{"key":"2022071813385448800_ref27","first-page":"924","volume-title":"Proc. ACM Symposium on Applied Computing","author":"Largeron","year":"2011"},{"key":"2022071813385448800_ref28","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/s12293-013-0128-0","article-title":"Spider monkey optimization algorithm for numerical optimization","volume":"6","author":"Bansal","year":"2014","journal-title":"Memet. Comput."},{"key":"2022071813385448800_ref29","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s10015-017-0422-x","article-title":"Deep recurrent neural network for mobile human activity recognition with high throughput","volume":"23","author":"Inoue","year":"2018","journal-title":"Artif. Life Robot."},{"key":"2022071813385448800_ref30","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/TIM.2018.2836058","article-title":"A new rider optimization algorithm-based neural network for fault diagnosis in analog circuits","volume":"68","author":"Binu","year":"2018","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"2022071813385448800_ref31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cor.2014.10.008","article-title":"Water wave optimization: a new nature-inspired metaheuristic","volume":"55","author":"Zheng","year":"2015","journal-title":"Comput. Oper. Res."},{"key":"2022071813385448800_ref32","author":"Rice disease dataset"},{"key":"2022071813385448800_ref33","doi-asserted-by":"crossref","first-page":"61","DOI":"10.3126\/jsce.v7i0.26794","article-title":"Rice plant disease detection using twin support vector machine (TSVM)","volume":"7","author":"Chawal","year":"2019","journal-title":"J. Sci. Eng."}],"container-title":["The Computer Journal"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/65\/7\/1812\/44921636\/bxab022.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/comjnl\/article-pdf\/65\/7\/1812\/44921636\/bxab022.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,7,18]],"date-time":"2022-07-18T13:40:15Z","timestamp":1658151615000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/comjnl\/article\/65\/7\/1812\/6236074"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,19]]},"references-count":33,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4,19]]},"published-print":{"date-parts":[[2022,7,15]]}},"URL":"https:\/\/doi.org\/10.1093\/comjnl\/bxab022","relation":{},"ISSN":["0010-4620","1460-2067"],"issn-type":[{"value":"0010-4620","type":"print"},{"value":"1460-2067","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2022,7,15]]},"published":{"date-parts":[[2021,4,19]]}}}