{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,30]],"date-time":"2026-01-30T09:59:22Z","timestamp":1769767162625,"version":"3.49.0"},"reference-count":20,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T00:00:00Z","timestamp":1769644800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"\n Background<\/jats:title>\n The recognition and prevention of plant diseases is very important to the growth process. At present, neural networks have achieved good results in plant disease identification, but the development of convolutional neural networks has brought a large number of network parameters and long recognition time, which greatly limits its application on devices that lack computing resources.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n To solve this problem, We introduce a novel approach, dubbed instance-relation-matrix based knowledge distillation (IRMKD), that transfers mutual relations of data examples. For concrete realizations of IRMKD, we combine the correlation of the samples with the relationship between the characteristics of the instances and introducing multiple loss functions.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n Experimental results show that the proposed method improves educated student models with a significant margin. In particular, for traditional neural networks, our method significantly reduces memory usageand recognition time by an average of 92% and at the same time ensure that the recognition accuracy rate is above 93%, provides a new plant disease recognition method for devices with limited memory and computing resources.<\/jats:p>\n <\/jats:sec>\n \n Conclusion<\/jats:title>\n IRMKD can significantly reduce the volume of the model and improve the recognition speed of the model on the premise of slightly reducing the accuracy of the verification set.<\/jats:p>\n <\/jats:sec>","DOI":"10.3389\/fbinf.2026.1761574","type":"journal-article","created":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T07:24:13Z","timestamp":1769671453000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["IRMKD: an application of instance relation matrix in plant disease recognition"],"prefix":"10.3389","volume":"6","author":[{"given":"Jinqing","family":"Huang","sequence":"first","affiliation":[{"name":"School of Computer and Electronic Information, Guangxi University","place":["Nanning, China"]}]},{"given":"Jian","family":"Su","sequence":"additional","affiliation":[{"name":"Guangxi Science and Technology Evaluation Center","place":["Nanning, China"]}]},{"given":"Tengfei","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer and Electronic Information, Guangxi University","place":["Nanning, China"]}]}],"member":"1965","published-online":{"date-parts":[[2026,1,29]]},"reference":[{"key":"B1","first-page":"200","article-title":"Knowledge distillation with feature maps for image classification","volume-title":"Asian conference on computer vision","author":"Chen","year":"2018"},{"key":"B2","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1145\/3307650.3322258","article-title":"Tie: energy-efficient tensor train-based inference engine for deep neural network","volume-title":"Proceedings of the 46th international symposium on computer architecture","author":"Deng","year":"2019"},{"key":"B3","doi-asserted-by":"publisher","first-page":"485","DOI":"10.1109\/jproc.2020.2976475","article-title":"Model compression and hardware acceleration for neural networks: a comprehensive survey","volume":"108","author":"Deng","year":"2020","journal-title":"Proc. IEEE"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1789","DOI":"10.1007\/s11263-021-01453-z","article-title":"Knowledge distillation: a survey","volume":"129","author":"Gou","year":"2021","journal-title":"Int. J. Comput. Vis."},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1503.02531","article-title":"Distilling the knowledge in a neural network","author":"Hinton","year":"2015","journal-title":"arXiv Preprint arXiv:1503.02531"},{"key":"B6","article-title":"An open access repository of images on plant health to enable the development of mobile disease diagnostics","author":"Hughes","year":"2015","journal-title":"arXiv Preprint arXiv:1511.08060"},{"key":"B7","first-page":"07947","article-title":"Sequence-level knowledge distillation","author":"Kim","year":"2016"},{"key":"B8","doi-asserted-by":"crossref","first-page":"709","DOI":"10.23919\/SICE48898.2020.9240458","article-title":"Insect pest detection and identification method based on deep learning for realizing a pest control system","volume-title":"2020 59th annual conference of the Society of instrument and control engineers of Japan (SICE)","author":"Kuzuhara","year":"2020"},{"key":"B9","article-title":"Graph-based knowledge distillation by multi-head attention network","author":"Lee","year":"2019","journal-title":"arXiv Preprint arXiv:1907.02226"},{"key":"B10","first-page":"6445","article-title":"Conditional teacher-student learning","volume-title":"ICASSP 2019-2019 IEEE international conference on acoustics, speech and signal processing (ICASSP)","author":"Meng","year":"2019"},{"key":"B11","first-page":"2339","article-title":"Heterogeneous knowledge distillation using information flow modeling","volume-title":"Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","author":"Passalis","year":"2020"},{"key":"B12","first-page":"5007","article-title":"Correlation congruence for knowledge distillation","volume-title":"Proceedings of the IEEE\/CVF international conference on computer vision","author":"Peng","year":"2019"},{"key":"B13","doi-asserted-by":"publisher","first-page":"4780","DOI":"10.1609\/aaai.v33i01.33014780","article-title":"Regularized evolution for image classifier architecture search","volume":"33","author":"Real","year":"2019","journal-title":"Proc. AAAI Conf. Artif. Intell."},{"key":"B14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on image data augmentation for deep learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"B15","first-page":"2159","article-title":"Application of neural networks to image recognition of plant diseases","volume-title":"2012 international conference on systems and informatics (ICSAI2012)","author":"Wang","year":""},{"key":"B16","first-page":"246","article-title":"Image recognition of plant diseases based on principal component analysis and neural networks","volume-title":"2012 8th international conference on natural computation","author":"Wang","year":""},{"key":"B17","first-page":"894","article-title":"Image recognition of plant diseases based on backpropagation networks","volume-title":"2012 5th international congress on image and signal processing","author":"Wang","year":""},{"key":"B18","first-page":"108","article-title":"A secure perceptual hash algorithm for image content authentication","volume-title":"IFIP international conference on communications and multimedia security","author":"Weng","year":"2011"},{"key":"B19","first-page":"365","article-title":"Lq-nets: learned quantization for highly accurate and compact deep neural networks","volume-title":"Proceedings of the European conference on computer vision","author":"Zhang","year":"2018"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.5555\/3495724.3496961","article-title":"Task-oriented feature distillation","volume":"33","author":"Zhang","year":"2020","journal-title":"Adv. Neural Inf. Process. Syst."}],"container-title":["Frontiers in Bioinformatics"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbinf.2026.1761574\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T07:24:15Z","timestamp":1769671455000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fbinf.2026.1761574\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,1,29]]},"references-count":20,"alternative-id":["10.3389\/fbinf.2026.1761574"],"URL":"https:\/\/doi.org\/10.3389\/fbinf.2026.1761574","relation":{},"ISSN":["2673-7647"],"issn-type":[{"value":"2673-7647","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,1,29]]},"article-number":"1761574"}}