{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T22:12:53Z","timestamp":1773439973731,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100009570","name":"Srinakharinwirot University","doi-asserted-by":"publisher","award":["370\/2567"],"award-info":[{"award-number":["370\/2567"]}],"id":[{"id":"10.13039\/100009570","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-025-04607-9","type":"journal-article","created":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T07:12:48Z","timestamp":1765609968000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Lightweight Convolutional Neural Network Model Based on Modified MobileNetV3 for Plant Disease Classification"],"prefix":"10.1007","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2755-4786","authenticated-orcid":false,"given":"Banphatree","family":"Khomkham","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-6455-4590","authenticated-orcid":false,"given":"Yanapatt","family":"Pankaseam","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,12,13]]},"reference":[{"issue":"12","key":"4607_CR1","doi-asserted-by":"publisher","first-page":"1732","DOI":"10.3390\/biology11121732","volume":"11","author":"\u0130 Ya\u011f","year":"2022","unstructured":"Ya\u011f \u0130, Altan A. Artificial intelligence-based robust hybrid algorithm design and implementation for real-time detection of plant diseases in agricultural environments. Biology. 2022;11(12):1732. https:\/\/doi.org\/10.3390\/biology11121732.","journal-title":"Biology"},{"issue":"21","key":"4607_CR2","doi-asserted-by":"publisher","first-page":"4486","DOI":"10.3390\/rs13214486","volume":"13","author":"I Rakhmatulin","year":"2021","unstructured":"Rakhmatulin I, Kamilaris A, Andreasen C. Deep neural networks to detect weeds from crops in agricultural environments in real-time: a review. Remote Sens. 2021;13(21):4486. https:\/\/doi.org\/10.3390\/rs13214486.","journal-title":"Remote Sens"},{"issue":"8","key":"4607_CR3","doi-asserted-by":"publisher","first-page":"707","DOI":"10.3390\/agriculture11080707","volume":"11","author":"J Lu","year":"2021","unstructured":"Lu J, Tan L, Jiang H. Review on convolutional neural network (CNN) applied to plant leaf disease classification. Agriculture. 2021;11(8):707. https:\/\/doi.org\/10.3390\/agriculture11080707.","journal-title":"Agriculture"},{"issue":"3","key":"4607_CR4","doi-asserted-by":"publisher","first-page":"312","DOI":"10.1017\/S0021859618000436","volume":"156","author":"A Kamilaris","year":"2018","unstructured":"Kamilaris A, Prenafeta-Bold\u00fa FX. A review of the use of convolutional neural networks in agriculture. J Agric Sci. 2018;156(3):312\u201322. https:\/\/doi.org\/10.1017\/S0021859618000436.","journal-title":"J Agric Sci"},{"key":"4607_CR5","doi-asserted-by":"publisher","unstructured":"Shrestha G, Das M, Dey N. Plant disease detection using CNN, In:\u00a02020 IEEE applied signal processing conference (ASPCON), 2020: IEEE, pp. 109\u2013113. https:\/\/doi.org\/10.1109\/ASPCON49795.2020.9276722","DOI":"10.1109\/ASPCON49795.2020.9276722"},{"key":"4607_CR6","doi-asserted-by":"publisher","first-page":"3438","DOI":"10.1016\/j.matpr.2021.07.267","volume":"80","author":"D Indira","year":"2023","unstructured":"Indira D, Goddu J, Indraja B, Challa VML, Manasa B. A review on fruit recognition and feature evaluation using CNN. Mater Today Proc. 2023;80:3438\u201343. https:\/\/doi.org\/10.1016\/j.matpr.2021.07.267.","journal-title":"Mater Today Proc"},{"issue":"7","key":"4607_CR7","doi-asserted-by":"publisher","first-page":"1552","DOI":"10.3390\/diagnostics12071552","volume":"12","author":"B Khomkham","year":"2022","unstructured":"Khomkham B, Lipikorn R. Pulmonary lesion classification framework using the weighted ensemble classification with random forest and cnn models for ebus images. Diagnostics. 2022;12(7):1552. https:\/\/doi.org\/10.3390\/diagnostics12071552.","journal-title":"Diagnostics"},{"key":"4607_CR8","doi-asserted-by":"publisher","unstructured":"Arya S, Singh R. A Comparative Study of CNN and AlexNet for Detection of Disease in Potato and Mango leaf, In: 2019 International conference on issues and challenges in intelligent computing techniques (ICICT), 2019, vol. 1: IEEE, pp. 1\u20136. https:\/\/doi.org\/10.1109\/ICICT46931.2019.8977648","DOI":"10.1109\/ICICT46931.2019.8977648"},{"key":"4607_CR9","doi-asserted-by":"publisher","unstructured":"Jasitha P, Dileep M, Divya M. Venation based plant leaves classification using GoogLeNet and VGG, In: 2019 4th International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), 2019: IEEE, pp. 715\u2013719. https:\/\/doi.org\/10.1109\/RTEICT46194.2019.9016966","DOI":"10.1109\/RTEICT46194.2019.9016966"},{"key":"4607_CR10","doi-asserted-by":"publisher","DOI":"10.14569\/IJACSA.2022.0130484","author":"AA Alatawi","year":"2022","unstructured":"Alatawi AA, Alomani SM, Alhawiti NI, Ayaz M. Plant disease detection using AI based VGG-16 model. Int J Adv Comput Sci Appl. 2022. https:\/\/doi.org\/10.14569\/IJACSA.2022.0130484.","journal-title":"Int J Adv Comput Sci Appl"},{"key":"4607_CR11","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-981-16-9967-2_7","volume-title":"Smart Trends in Computing and Communications","author":"N Dubey","year":"2023","unstructured":"Dubey N, Bhagat E, Rana S, Pathak K. A Novel Approach to Detect Plant Disease Using DenseNet-121 Neural Network. In: Zhang Y.-D., Senjyu T., So-In C., Joshi A., editors. Smart Trends in Computing and Communications. Singapore: Springer Nature Singapore; 2023. p. 63\u201374."},{"key":"4607_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.pmpp.2019.101426","volume":"108","author":"MM Ali","year":"2019","unstructured":"Ali MM, Bachik NA, Muhadi NA, Yusof TNT, Gomes C. Non-destructive techniques of detecting plant diseases: a review. Physiol Mol Plant Pathol. 2019;108:101426. https:\/\/doi.org\/10.1016\/j.pmpp.2019.101426.","journal-title":"Physiol Mol Plant Pathol"},{"key":"4607_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.105117","volume":"168","author":"H Nazki","year":"2020","unstructured":"Nazki H, Yoon S, Fuentes A, Park DS. Unsupervised image translation using adversarial networks for improved plant disease recognition. Comput Electron Agric. 2020;168:105117. https:\/\/doi.org\/10.1016\/j.compag.2019.105117.","journal-title":"Comput Electron Agric"},{"key":"4607_CR14","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.aiia.2021.05.002","volume":"5","author":"P Bedi","year":"2021","unstructured":"Bedi P, Gole P. Plant disease detection using hybrid model based on convolutional autoencoder and convolutional neural network. Artif Intell Agric. 2021;5:90\u2013101. https:\/\/doi.org\/10.1016\/j.aiia.2021.05.002.","journal-title":"Artif Intell Agric"},{"issue":"8","key":"4607_CR15","doi-asserted-by":"publisher","first-page":"1266","DOI":"10.3390\/electronics11081266","volume":"11","author":"JA Pandian","year":"2022","unstructured":"Pandian JA, et al. A five convolutional layer deep convolutional neural network for plant leaf disease detection. Electronics. 2022;11(8):1266. https:\/\/doi.org\/10.3390\/electronics11081266.","journal-title":"Electronics"},{"issue":"3","key":"4607_CR16","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1007\/s41348-021-00465-8","volume":"129","author":"S Vallabhajosyula","year":"2022","unstructured":"Vallabhajosyula S, Sistla V, Kolli VKK. Transfer learning-based deep ensemble neural network for plant leaf disease detection. J Plant Dis Prot. 2022;129(3):545\u201358. https:\/\/doi.org\/10.1007\/s41348-021-00465-8.","journal-title":"J Plant Dis Prot"},{"issue":"2","key":"4607_CR17","doi-asserted-by":"publisher","DOI":"10.3390\/agronomy12020365","volume":"12","author":"Z Chen","year":"2022","unstructured":"Chen Z, et al. Plant disease recognition model based on improved YOLOv5. Agronomy. 2022;12(2):365. https:\/\/doi.org\/10.3390\/agronomy12020365.","journal-title":"Agronomy"},{"issue":"2","key":"4607_CR18","doi-asserted-by":"publisher","first-page":"4465","DOI":"10.1007\/s11042-023-15809-9","volume":"83","author":"M Khanna","year":"2024","unstructured":"Khanna M, Singh LK, Thawkar S, Goyal M. PlaNet: a robust deep convolutional neural network model for plant leaves disease recognition. Multimedia Tools Appl. 2024;83(2):4465\u2013517. https:\/\/doi.org\/10.1007\/s11042-023-15809-9.","journal-title":"Multimedia Tools Appl"},{"issue":"6","key":"4607_CR19","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-024-03100-z","volume":"5","author":"J Lachure","year":"2024","unstructured":"Lachure J, Doriya R. Designing of lightweight deep learning framework for plant disease detection. SN Computer Science. 2024;5(6):761. https:\/\/doi.org\/10.1007\/s42979-024-03100-z.","journal-title":"SN Computer Science"},{"issue":"8","key":"4607_CR20","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-024-03400-4","volume":"5","author":"D Singh","year":"2024","unstructured":"Singh D, Kumar A. A deep recurrent neural network for plant disease classification. SN Computer Science. 2024;5(8):1053. https:\/\/doi.org\/10.1007\/s42979-024-03400-4.","journal-title":"SN Computer Science"},{"issue":"2","key":"4607_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-024-03582-x","volume":"6","author":"P Dhoundiyal","year":"2025","unstructured":"Dhoundiyal P, Sharma V, Vats S, Rawat P. A progressive hierarchical model for plant disease diagnosis. SN Computer Science. 2025;6(2):102. https:\/\/doi.org\/10.1007\/s42979-024-03582-x.","journal-title":"SN Computer Science"},{"issue":"4","key":"4607_CR22","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-025-03897-3","volume":"6","author":"A Thakur","year":"2025","unstructured":"Thakur A, Thakur A, Vivek V, Mahesh TR, Krishna KM. Enhanced layer extraction for efficient plant disease classification using Efficientnet B1. SN Comput Sci. 2025;6(4):379. https:\/\/doi.org\/10.1007\/s42979-025-03897-3.","journal-title":"SN Comput Sci"},{"key":"4607_CR23","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2010.11929","author":"A Dosovitskiy","year":"2020","unstructured":"Dosovitskiy A, et al. An image is worth 16x16 words: Transformers for image recognition at scale. ArXiv Preprint arXiv:2010 11929. 2020. https:\/\/doi.org\/10.48550\/arXiv.2010.11929.","journal-title":"ArXiv Preprint arXiv:2010 11929"},{"key":"4607_CR24","doi-asserted-by":"publisher","unstructured":"Liu Z et al. Swin transformer: Hierarchical vision transformer using shifted windows, In: 2021\u00a0Proceedings of the IEEE\/CVF international conference on computer vision, 2021, pp. 10012\u201310022. https:\/\/doi.org\/10.48550\/arXiv.2103.14030","DOI":"10.48550\/arXiv.2103.14030"},{"key":"4607_CR25","doi-asserted-by":"publisher","unstructured":"Touvron H, Cord M, Douze M, Massa F, Sablayrolles A, J\u00e9gou H. Training data-efficient image transformers & distillation through attention, In: 2021\u00a0International conference on machine learning, 2021: PMLR, pp. 10347\u201310357. https:\/\/doi.org\/10.48550\/arXiv.2012.12877","DOI":"10.48550\/arXiv.2012.12877"},{"issue":"1","key":"4607_CR26","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture14010142","volume":"14","author":"H Si","year":"2024","unstructured":"Si H, et al. A dual-branch model integrating CNN and swin transformer for efficient apple leaf disease classification. Agriculture. 2024;14(1):142. https:\/\/doi.org\/10.3390\/agriculture14010142.","journal-title":"Agriculture"},{"key":"4607_CR27","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1511.08060","author":"D Hughes","year":"2015","unstructured":"Hughes D, Salath\u00e9 M. An open access repository of images on plant health to enable the development of mobile disease diagnostics. ArXiv Preprint arXiv:1511 08060. 2015. https:\/\/doi.org\/10.48550\/arXiv.1511.08060.","journal-title":"ArXiv Preprint arXiv:1511 08060"},{"key":"4607_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105393","volume":"173","author":"J Chen","year":"2020","unstructured":"Chen J, Chen J, Zhang D, Sun Y, Nanehkaran YA. Using deep transfer learning for image-based plant disease identification. Comput Electron Agric. 2020;173:105393. https:\/\/doi.org\/10.1016\/j.compag.2020.105393.","journal-title":"Comput Electron Agric"},{"key":"4607_CR29","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.2004.11958","author":"R Thapa","year":"2020","unstructured":"Thapa R, Snavely N, Belongie S, Khan A. The plant pathology 2020 challenge dataset to classify foliar disease of apples. ArXiv Preprint arXiv:2004 11958. 2020. https:\/\/doi.org\/10.48550\/arXiv.2004.11958.","journal-title":"ArXiv Preprint arXiv:2004 11958"},{"key":"4607_CR30","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition, In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27\u201330 June 2016 2016, pp. 770\u2013778. https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"4607_CR31","doi-asserted-by":"publisher","unstructured":"Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC. MobileNetV2: Inverted Residuals and Linear Bottlenecks, In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 18\u201323 June 2018 2018, pp. 4510\u20134520. https:\/\/doi.org\/10.1109\/CVPR.2018.00474","DOI":"10.1109\/CVPR.2018.00474"},{"key":"4607_CR32","doi-asserted-by":"publisher","unstructured":"Zhou D, Hou Q, Chen Y, Feng J, Yan S, Rethinking bottleneck structure for efficient mobile network design, In: 2020, Proceedings, Part III 16, 2020: Springer, pp. 680\u2013697. https:\/\/doi.org\/10.1007\/978-3-030-58580-8_40","DOI":"10.1007\/978-3-030-58580-8_40"},{"key":"4607_CR33","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham M, Van Gool L, Williams CK, Winn J, Zisserman A. The pascal visual object classes (voc) challenge. Int J Comput Vis. 2010;88:303\u201338. https:\/\/doi.org\/10.1007\/s11263-009-0275-4.","journal-title":"Int J Comput Vis"},{"key":"4607_CR34","doi-asserted-by":"publisher","unstructured":"Howard A et al. Searching for mobilenetv3, In Proceedings of the IEEE\/CVF international conference on computer vision, 2019, pp. 1314\u20131324. https:\/\/doi.org\/10.1109\/ICCV.2019.00140","DOI":"10.1109\/ICCV.2019.00140"},{"key":"4607_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.iot.2022.100650","volume":"21","author":"S Yu","year":"2023","unstructured":"Yu S, Xie L, Huang Q. Inception convolutional vision transformers for plant disease identification. Internet Things. 2023;21:100650. https:\/\/doi.org\/10.1016\/j.iot.2022.100650.","journal-title":"Internet Things"},{"issue":"5","key":"4607_CR36","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2023.101555","volume":"35","author":"G Dai","year":"2023","unstructured":"Dai G, Fan J, Tian Z, Wang C. PPLC-net: neural network-based plant disease identification model supported by weather data augmentation and multi-level attention mechanism. J King Saud Univ-Comput Inf Sci. 2023;35(5):101555. https:\/\/doi.org\/10.1016\/j.jksuci.2023.101555.","journal-title":"J King Saud Univ-Comput Inf Sci"},{"key":"4607_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.105093","volume":"167","author":"A Picon","year":"2019","unstructured":"Picon A, Seitz M, Alvarez-Gila A, Mohnke P, Ortiz-Barredo A, Echazarra J. Crop conditional convolutional neural networks for massive multi-crop plant disease classification over cell phone acquired images taken on real field conditions. Comput Electron Agric. 2019;167:105093. https:\/\/doi.org\/10.1016\/j.compag.2019.105093.","journal-title":"Comput Electron Agric"},{"key":"4607_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105735","volume":"178","author":"Z Tang","year":"2020","unstructured":"Tang Z, Yang J, Li Z, Qi F. Grape disease image classification based on lightweight convolution neural networks and channelwise attention. Comput Electron Agric. 2020;178:105735. https:\/\/doi.org\/10.1016\/j.compag.2020.105735.","journal-title":"Comput Electron Agric"},{"key":"4607_CR39","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2021.106644","volume":"193","author":"Y Zhao","year":"2022","unstructured":"Zhao Y, Sun C, Xu X, Chen J. RIC-net: a plant disease classification model based on the fusion of Inception and residual structure and embedded attention mechanism. Comput Electron Agric. 2022;193:106644. https:\/\/doi.org\/10.1016\/j.compag.2021.106644.","journal-title":"Comput Electron Agric"},{"key":"4607_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107407","volume":"202","author":"Y Guo","year":"2022","unstructured":"Guo Y, Lan Y, Chen X. CST: convolutional Swin Transformer for detecting the degree and types of plant diseases. Comput Electron Agric. 2022;202:107407. https:\/\/doi.org\/10.1016\/j.compag.2022.107407.","journal-title":"Comput Electron Agric"},{"key":"4607_CR41","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2022.864486","author":"X Qian","year":"2022","unstructured":"Qian X, Zhang C, Chen L, Li K. Deep learning-based identification of maize leaf diseases is improved by an attention mechanism: self-attention. Front Plant Sci. 2022. https:\/\/doi.org\/10.3389\/fpls.2022.864486.","journal-title":"Front Plant Sci"},{"key":"4607_CR42","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105933","volume":"86","author":"K R","year":"2020","unstructured":"R K, M H, Anand S, Mathikshara P, Johnson A, R M. Attention embedded residual CNN for disease detection in tomato leaves. Appl Soft Comput. 2020;86:105933. https:\/\/doi.org\/10.1016\/j.asoc.2019.105933.","journal-title":"Appl Soft Comput"},{"key":"4607_CR43","doi-asserted-by":"publisher","first-page":"829479","DOI":"10.3389\/fpls.2022.829479","volume":"13","author":"T Zeng","year":"2022","unstructured":"Zeng T, et al. Rubber leaf disease recognition based on improved deep convolutional neural networks with a cross-scale attention mechanism. Front Plant Sci. 2022;13:829479. https:\/\/doi.org\/10.3389\/fpls.2022.829479.","journal-title":"Front Plant Sci"},{"key":"4607_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2022.107390","volume":"202","author":"J Lin","year":"2022","unstructured":"Lin J, et al. CAMFFNet: a novel convolutional neural network model for tobacco disease image recognition. Comput Electron Agric. 2022;202:107390. https:\/\/doi.org\/10.1016\/j.compag.2022.107390.","journal-title":"Comput Electron Agric"},{"issue":"1","key":"4607_CR45","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1007\/s11042-022-13144-z","volume":"82","author":"PS Thakur","year":"2023","unstructured":"Thakur PS, Sheorey T, Ojha A. VGG-ICNN: a lightweight CNN model for crop disease identification. Multimedia Tools Appl. 2023;82(1):497\u2013520. https:\/\/doi.org\/10.1007\/s11042-022-13144-z.","journal-title":"Multimedia Tools Appl"},{"key":"4607_CR46","doi-asserted-by":"publisher","DOI":"10.3389\/fpls.2023.1276728","author":"S Quan","year":"2023","unstructured":"Quan S, Wang J, Jia Z, Yang M, Xu Q. MS-net: a novel lightweight and precise model for plant disease identification. Front Plant Sci. 2023. https:\/\/doi.org\/10.3389\/fpls.2023.1276728.","journal-title":"Front Plant Sci"},{"key":"4607_CR47","doi-asserted-by":"publisher","DOI":"10.1016\/j.ecoinf.2023.102025","volume":"75","author":"V Sharma","year":"2023","unstructured":"Sharma V, Tripathi AK, Mittal H. DLMC-Net: deeper lightweight multi-class classification model for plant leaf disease detection. Ecol Inform. 2023;75:102025. https:\/\/doi.org\/10.1016\/j.ecoinf.2023.102025.","journal-title":"Ecol Inform"},{"issue":"3","key":"4607_CR48","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-024-02620-y","volume":"5","author":"AS Tewari","year":"2024","unstructured":"Tewari AS. Lightweight convolutional neural network model for cassava leaf diseases classification. SN Comput Sci. 2024;5(3):284. https:\/\/doi.org\/10.1007\/s42979-024-02620-y.","journal-title":"SN Comput Sci"},{"key":"4607_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2023.108481","volume":"216","author":"G Dai","year":"2024","unstructured":"Dai G, Tian Z, Fan J, Sunil CK, Dewi C. DFN-psan: multi-level deep information feature fusion extraction network for interpretable plant disease classification. Comput Electron Agric. 2024;216:108481. https:\/\/doi.org\/10.1016\/j.compag.2023.108481.","journal-title":"Comput Electron Agric"},{"key":"4607_CR50","doi-asserted-by":"publisher","unstructured":"Thepsueb P, Khomkham B. Cnns Optimization for Corn Leaf Disease Detection Using Post-Training Quantization Techniques, In: 2025 17th International Conference on Knowledge and Smart Technology (KST), 2025: IEEE, pp. 7\u201312. https:\/\/doi.org\/10.1109\/KST65016.2025.11003356","DOI":"10.1109\/KST65016.2025.11003356"},{"issue":"1","key":"4607_CR51","doi-asserted-by":"publisher","first-page":"37390","DOI":"10.1038\/s41598-025-20124-4","volume":"15","author":"S Muthusamy","year":"2025","unstructured":"Muthusamy S, Ramu SP. A neural architecture search optimized lightweight attention ensemble model for nutrient deficiency and severity assessment in diverse crop leaves. Sci Rep. 2025;15(1):37390. https:\/\/doi.org\/10.1038\/s41598-025-20124-4.","journal-title":"Sci Rep"}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04607-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-025-04607-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-025-04607-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T07:12:49Z","timestamp":1765609969000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-025-04607-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,13]]},"references-count":51,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["4607"],"URL":"https:\/\/doi.org\/10.1007\/s42979-025-04607-9","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,13]]},"assertion":[{"value":"2 July 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 December 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 December 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors report there are no competing interests to declare.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This study was reviewed and approved as exempt by the Human Research Ethics Committee of Srinakharinwirot University (Protocol code: SWUEC-671078).","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}],"article-number":"1028"}}