{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T19:05:18Z","timestamp":1732043118841},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T00:00:00Z","timestamp":1699401600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T00:00:00Z","timestamp":1699401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Multimed Tools Appl"],"DOI":"10.1007\/s11042-023-17629-3","type":"journal-article","created":{"date-parts":[[2023,11,8]],"date-time":"2023-11-08T09:02:15Z","timestamp":1699434135000},"page":"50733-50755","update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A novel framework for semi-automated system for grape leaf disease detection"],"prefix":"10.1007","volume":"83","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-8789-4013","authenticated-orcid":false,"given":"Navneet","family":"Kaur","sequence":"first","affiliation":[]},{"given":"V.","family":"Devendran","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,8]]},"reference":[{"key":"17629_CR1","unstructured":"Sainis JK, Chadda VK (2004) Applications\u00a0of image processing in biology and agriculture.\u00a0https:\/\/www.semanticscholar.org\/paper\/APPLICATIONS-OF-IMAGE-PROCESSING-IN-BIOLOGY-AND-Sainis-Chadda\/cddebd14a59e05a8748a9e39ae58f64c2c95f5f8"},{"key":"17629_CR2","first-page":"83","volume":"46","author":"SC Mittal","year":"2001","unstructured":"Mittal SC (2001) Role of information technology in agriculture and its scope in India. Fertiliser News 46:83\u201388","journal-title":"Fertiliser News"},{"key":"17629_CR3","doi-asserted-by":"publisher","unstructured":"Rachidi M, Chappard C, Marchadier A, Gadois C, Lespessailles E, Benhamou CL (2008) Application of laws\u2019 masks to bone texture analysis: an innovative image analysis tool in osteoporosis. 2008 5th IEEE Int. Symp. Biomed. Imaging From Nano to Macro, Proceedings 1191\u20131194. https:\/\/doi.org\/10.1109\/ISBI.2008.4541215","DOI":"10.1109\/ISBI.2008.4541215"},{"key":"17629_CR4","doi-asserted-by":"publisher","first-page":"78","DOI":"10.3923\/jai.2008.78.85","volume":"1","author":"SS Abu-Naser","year":"2008","unstructured":"Abu-Naser SS, Kashkash KA, Fayyad M (2008) Developing an expert system for plant disease diagnosis. J Artif Intell 1:78\u201385. https:\/\/doi.org\/10.3923\/jai.2008.78.85","journal-title":"J Artif Intell"},{"key":"17629_CR5","doi-asserted-by":"publisher","first-page":"267","DOI":"10.3923\/itj.2011.267.275","volume":"10","author":"D Al Bashish","year":"2011","unstructured":"Al Bashish D, Braik M, Bani-Ahmad S (2011) Detection and classification of leaf diseases using K-means-based segmentation and neural-networks-based classification. Inf Technol J 10:267\u2013275. https:\/\/doi.org\/10.3923\/itj.2011.267.275","journal-title":"Inf Technol J"},{"key":"17629_CR6","doi-asserted-by":"publisher","first-page":"26","DOI":"10.3844\/ajassp.2011.26.32","volume":"8","author":"HY Chai","year":"2011","unstructured":"Chai HY, Wee KL, Swee TT, Salleh SH, Ariff AK (2011) Gray-level co-occurrence matrix bone fracture detection. Am J Appl Sci 8:26\u201332. https:\/\/doi.org\/10.3844\/ajassp.2011.26.32","journal-title":"Am J Appl Sci"},{"key":"17629_CR7","doi-asserted-by":"publisher","first-page":"15","DOI":"10.9756\/bijaip.1004","volume":"1","author":"R Shenbagavalli","year":"2011","unstructured":"Shenbagavalli R (2011) Classification of Soil textures based on laws features extracted from preprocessing images on sequential and Random Windows. Bonfring Int J Adv Image Process 1:15\u201318. https:\/\/doi.org\/10.9756\/bijaip.1004","journal-title":"Bonfring Int J Adv Image Process"},{"key":"17629_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5402\/2012\/248285","volume":"2012","author":"AJ Afifi","year":"2012","unstructured":"Afifi AJ, Ashour WM (2012) Image retrieval based on content using color feature. ISRN Comput Graph 2012:1\u201311. https:\/\/doi.org\/10.5402\/2012\/248285","journal-title":"ISRN Comput Graph"},{"key":"17629_CR9","doi-asserted-by":"publisher","unstructured":"Kamarainen JK (2012) Gabor features in image analysis. 2012 3rd Int Conf Image Process Theory Tools Appl IPTA: 13\u201314. https:\/\/doi.org\/10.1109\/IPTA.2012.6469502","DOI":"10.1109\/IPTA.2012.6469502"},{"key":"17629_CR10","doi-asserted-by":"publisher","first-page":"372","DOI":"10.1007\/978-3-642-35380-2_44","volume":"7677","author":"S Prasad","year":"2012","unstructured":"Prasad S, Kumar P, Hazra R, Kumar A (2012) Plant leaf disease detection using Gabor wavelet transform. Lect Notes Comput Sci (Including Subser Lect Notes Artif Intell Lect Notes Bioinformatics) 7677:372\u2013379. https:\/\/doi.org\/10.1007\/978-3-642-35380-2_44","journal-title":"Lect Notes Comput Sci (Including Subser Lect Notes Artif Intell Lect Notes Bioinformatics)"},{"key":"17629_CR11","doi-asserted-by":"publisher","unstructured":"Jhuria M, Kum A (2013) Image processing for smart farming: detection of disease and fruit grading. In: 2013 IEEE Second International Conference on Image Information Processing (ICIIP-2013), Shimla, India, pp 521\u2013526. https:\/\/doi.org\/10.1109\/ICIIP.2013.6707647","DOI":"10.1109\/ICIIP.2013.6707647"},{"key":"17629_CR12","first-page":"116","volume":"2","author":"V Tyagi","year":"2012","unstructured":"Tyagi V (2012) India\u2019s agriculture: challenges for growth & development in present scenario. Int J Phys Soc Sci 2:116\u2013128","journal-title":"Int J Phys Soc Sci"},{"key":"17629_CR13","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1016\/j.advengsoft.2013.12.007","volume":"69","author":"S Mirjalili","year":"2014","unstructured":"Mirjalili S, Mirjalili SM, Lewis A (2014) Grey wolf optimizer. Adv Eng Softw 69:46\u201361. https:\/\/doi.org\/10.1016\/j.advengsoft.2013.12.007","journal-title":"Adv Eng Softw"},{"key":"17629_CR14","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.procs.2015.07.341","volume":"59","author":"AS Setiawan","year":"2014","unstructured":"Setiawan AS, Wesley J, Purnama Y (2014) Mammogram classification using Law\u2019s texture energy measure and neural networks. Procedia Comput Sci 59:92\u201397. https:\/\/doi.org\/10.1016\/j.procs.2015.07.341","journal-title":"Procedia Comput Sci"},{"key":"17629_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1140\/epjnbp\/s40366-015-0017-1","volume":"3","author":"JD De Certaines","year":"2015","unstructured":"De Certaines JD et al (2015) Application of texture analysis to muscle MRI: 1-What kind of information should be expected from texture analysis? EPJ Nonlinear Biomed Phys 3:1\u201314. https:\/\/doi.org\/10.1140\/epjnbp\/s40366-015-0017-1","journal-title":"EPJ Nonlinear Biomed Phys"},{"key":"17629_CR16","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.neucom.2014.12.032","volume":"154","author":"S Beura","year":"2015","unstructured":"Beura S, Majhi B, Dash R (2015) Mammogram classification using two dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of Breast cancer. Neurocomputing 154:1\u201314. https:\/\/doi.org\/10.1016\/j.neucom.2014.12.032","journal-title":"Neurocomputing"},{"key":"17629_CR17","doi-asserted-by":"publisher","unstructured":"Prasad S, Peddoju SK, Ghosh D (2015) Multi-resolution mobile vision system for plant leaf Disease diagnosis. https:\/\/doi.org\/10.1007\/s11760-015-0751-y","DOI":"10.1007\/s11760-015-0751-y"},{"key":"17629_CR18","unstructured":"Szil\u00e1gyi T, Brady SM, Brunner T, Joshi N (2015) Local phase significance estimated with uncertainties to detect fibrotic regions from in vivo Pancreatic cancer images. Semantic Scholar.\u00a0https:\/\/www.semanticscholar.org\/paper\/Local-phase-significance-estimated-with-to-detect-Szil%C3%A1gyi-Brady\/3e2bb68200782913624efdc76259baded1d39daa"},{"key":"17629_CR19","doi-asserted-by":"publisher","unstructured":"Jobin F, Anto SD, Anoop BK (2016) Identification of leaf diseases in pepper plants using soft computing techniques. In: 2016 Conference on Emerging Devices and Smart Systems (ICEDSS), Namakkal, India, pp 168\u2013173. https:\/\/doi.org\/10.1109\/ICEDSS.2016.7587787","DOI":"10.1109\/ICEDSS.2016.7587787"},{"key":"17629_CR20","first-page":"225","volume":"6","author":"GT Hariharan","year":"2016","unstructured":"Hariharan GT, Hariharan GPS, Anandh RV (2016) Crop Disease Identification using image processing. Int J Latest Trends Eng Technol (IJLTET) 6:225\u2013259","journal-title":"Int J Latest Trends Eng Technol (IJLTET)"},{"key":"17629_CR21","doi-asserted-by":"publisher","first-page":"6","DOI":"10.9781\/ijimai.2016.371","volume":"3","author":"JD Pujari","year":"2016","unstructured":"Pujari JD, Yakkundimath R, Byadgi AS (2016) SVM and ANN based classification of plant diseases using feature reduction technique. Int J Interact Multimed Artif Intell 3:6. https:\/\/doi.org\/10.9781\/ijimai.2016.371","journal-title":"Int J Interact Multimed Artif Intell"},{"key":"17629_CR22","doi-asserted-by":"publisher","first-page":"56","DOI":"10.1109\/PDGC.2016.7913115","volume":"2016","author":"S Bhusri","year":"2016","unstructured":"Bhusri S, Jain S (2016) Analysis of breast lesions using laws\u2019 mask texture features. 2016 4th Int Conf Parallel Distrib Grid Comput 2016:56\u201360. https:\/\/doi.org\/10.1109\/PDGC.2016.7913115","journal-title":"2016 4th Int Conf Parallel Distrib Grid Comput"},{"key":"17629_CR23","doi-asserted-by":"crossref","unstructured":"Pantazi XE, Moshou D, Tamouridou AA, Kasderidis S (2016) Leaf disease recognition in vine plants based on local binary patterns and one class support vector machines. Int Federation Inform Process: 319\u2013327. https:\/\/doi.og\/10.1007\/978-3-319-44944-9_27","DOI":"10.1007\/978-3-319-44944-9_27"},{"key":"17629_CR24","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.aei.2017.09.007","volume":"34","author":"K Kamal","year":"2017","unstructured":"Kamal K, Qayyum R, Mathavan S, Zafar T (2017) Wood defects classification using laws texture energy measures and supervised learning approach. Adv Eng Informatics 34:125\u2013135. https:\/\/doi.org\/10.1016\/j.aei.2017.09.007","journal-title":"Adv Eng Informatics"},{"key":"17629_CR25","doi-asserted-by":"publisher","first-page":"61","DOI":"10.9790\/9622-0708066163","volume":"7","author":"M Dcruz","year":"2017","unstructured":"Dcruz M (2017) Feature exraction in mammograms using NSCT and LAWS texture analysis approach. Int J Eng Res 7:61\u201363. https:\/\/doi.org\/10.9790\/9622-0708066163","journal-title":"Int J Eng Res"},{"key":"17629_CR26","doi-asserted-by":"publisher","unstructured":"Suresha M, Shreekanth KN (2017) Recognition of diseases in paddy leaves using kNN classifier. 2017 2nd International Conference for Convergence in Technology (I2CT). 663\u2013666. https:\/\/doi.org\/10.1109\/I2CT.2017.8226213","DOI":"10.1109\/I2CT.2017.8226213"},{"key":"17629_CR27","doi-asserted-by":"publisher","unstructured":"Agrawal N, Singhai J (2017) Grape leaf disease detection and classification using multi-class support vector machine. 2017 Int Conf Recent Innov Signal Process Embed Syst: 238\u2013244. https:\/\/doi.org\/10.1109\/RISE.2017.8378160","DOI":"10.1109\/RISE.2017.8378160"},{"key":"17629_CR28","doi-asserted-by":"publisher","unstructured":"De Luna RG, Dadios EP, Bandala AA (2018) Automated image capturing system for deep learning-based tomato plant leaf disease detection and recognition. In: TENCON 2018 - 2018 IEEE Region 10 Conference, Jeju, Korea (South), pp 1414\u20131419. https:\/\/doi.org\/10.1109\/TENCON.2018.8650088","DOI":"10.1109\/TENCON.2018.8650088"},{"key":"17629_CR29","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.1049\/iet-ipr.2017.0822","volume":"12","author":"S Kaur","year":"2018","unstructured":"Kaur S, Pandey S, Goel S (2018) Semi-automatic leaf disease detection and classification system for soybean culture. IET Image Process 12:1038\u20131048. https:\/\/doi.org\/10.1049\/iet-ipr.2017.0822","journal-title":"IET Image Process"},{"key":"17629_CR30","doi-asserted-by":"publisher","first-page":"100349","DOI":"10.1016\/j.suscom.2019.08.002","volume":"24","author":"A Adeel","year":"2019","unstructured":"Adeel A et al (2019) An automated system based on a novel saliency approach and canonical correlation analysis based multiple features fusion. Sustain Comput Inf Syst 24:100349. https:\/\/doi.org\/10.1016\/j.suscom.2019.08.002","journal-title":"Sustain Comput Inf Syst"},{"key":"17629_CR31","doi-asserted-by":"publisher","first-page":"8975","DOI":"10.1109\/ACCESS.2018.2890743","volume":"7","author":"A Humeau-Heurtier","year":"2019","unstructured":"Humeau-Heurtier A (2019) Texture feature extraction methods: a survey. IEEE Access 7:8975\u20139000. https:\/\/doi.org\/10.1109\/ACCESS.2018.2890743","journal-title":"IEEE Access"},{"key":"17629_CR32","doi-asserted-by":"publisher","unstructured":"Usha Kumari C, Jeevan Prasad S, Mounika G (2019) Leaf disease detection: Feature extraction with k-means clustering and classification with ANN. Proc 3rd Int Conf Comput Methodol Commun ICCMC. 1095\u20131098. https:\/\/doi.org\/10.1109\/ICCMC.2019.8819750","DOI":"10.1109\/ICCMC.2019.8819750"},{"key":"17629_CR33","doi-asserted-by":"publisher","first-page":"782","DOI":"10.1016\/j.measurement.2018.12.027","volume":"135","author":"G Dhingra","year":"2019","unstructured":"Dhingra G, Kumar V, Joshi HD (2019) A novel computer vision based neutrosophic approach for leaf disease identification and classification. Meas J Int Meas Confed 135:782\u2013794. https:\/\/doi.org\/10.1016\/j.measurement.2018.12.027","journal-title":"Meas J Int Meas Confed"},{"key":"17629_CR34","doi-asserted-by":"publisher","unstructured":"Ahmed K, Shahidi TR, Alam SMI, Momen S (2020) Rice leaf disease detection using machine learning techniques. 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI). https:\/\/doi.org\/10.1109\/STI47673.2019.9068096","DOI":"10.1109\/STI47673.2019.9068096"},{"key":"17629_CR35","doi-asserted-by":"publisher","first-page":"3728","DOI":"10.1166\/jctn.2019.8241","volume":"16","author":"N Kaur","year":"2019","unstructured":"Kaur N, Devendran V, Verma S (2019) Detection of plant leaf diseases by applying image processing schemes. J Comput Theor Nanosci 16:3728\u20133734. https:\/\/doi.org\/10.1166\/jctn.2019.8241","journal-title":"J Comput Theor Nanosci"},{"key":"17629_CR36","first-page":"121","volume":"4","author":"N Kaur","year":"2019","unstructured":"Kaur N, Devendran V, Verma S (2019) Plant leaf disease identification supported by image segmentation. Feature Extraction and Ensemble Classif 4:121\u2013133","journal-title":"Feature Extraction and Ensemble Classif"},{"key":"17629_CR37","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1016\/j.aiia.2019.09.002","volume":"3","author":"V Singh","year":"2019","unstructured":"Singh V (2019) Sunflower leaf diseases detection using image segmentation based on particle swarm optimization. Artif Intell Agric 3:62\u201368. https:\/\/doi.org\/10.1016\/j.aiia.2019.09.002","journal-title":"Artif Intell Agric"},{"key":"17629_CR38","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1007\/s12530-019-09289-2","volume":"11","author":"AD Andrushia","year":"2020","unstructured":"Andrushia AD, Patricia AT (2020) Artificial bee colony optimization (ABC) for grape leaves disease detection. Evol Syst 11:105\u2013117. https:\/\/doi.org\/10.1007\/s12530-019-09289-2","journal-title":"Evol Syst"},{"key":"17629_CR39","first-page":"194","volume":"29","author":"E Samatha","year":"2020","unstructured":"Samatha E, Chaturved S, Shailaja C (2020) Plant leaf disease detection and classification using texture feature based back propagated Artificial neural network classifier. Int J Adv Sci Technol 29:194\u2013203","journal-title":"Int J Adv Sci Technol"},{"key":"17629_CR40","doi-asserted-by":"publisher","first-page":"11419","DOI":"10.1007\/s00521-019-04634-7","volume":"32","author":"MS Mustafa","year":"2020","unstructured":"Mustafa MS, Husin Z, Tan WK, Mavi MF, Farook RSM (2020) Development of automated hybrid intelligent system for herbs plant classification and early herbs plant disease detection. Neural Comput Appl 32:11419\u201311441. https:\/\/doi.org\/10.1007\/s00521-019-04634-7","journal-title":"Neural Comput Appl"},{"key":"17629_CR41","doi-asserted-by":"publisher","unstructured":"Kaur N, Devendran V (2020) Novel plant leaf disease detection based on optimize segmentation and law mask feature extraction with SVM classifier. Mater Today Proc. https:\/\/doi.org\/10.1016\/j.matpr.2020.10.901","DOI":"10.1016\/j.matpr.2020.10.901"},{"key":"17629_CR42","doi-asserted-by":"publisher","unstructured":"Xiong Y, Liang L, Wang L, She J, Wu M (n.d.) Identification of cash crop Diseases using automatic image segmentation algorithm and deep learning with expanded dataset. Comput Electron Agric 177:105712. https:\/\/doi.org\/10.1016\/j.compag.2020.105712","DOI":"10.1016\/j.compag.2020.105712"},{"key":"17629_CR43","doi-asserted-by":"publisher","unstructured":"Chauhan D, Walia R, Singh C, Deivakani M, Kumbhkar M (2021) Detection of maize disease using random forest classification algorithm. Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12(9):715\u2013720. https:\/\/doi.org\/10.17762\/turcomat.v12i9.3141","DOI":"10.17762\/turcomat.v12i9.3141"},{"key":"17629_CR44","doi-asserted-by":"publisher","unstructured":"Ahmed AA, Reddy GH (2021) A mobile-based system for detecting plant leaf diseases using deep learning. AgriEngineering 3:478\u2013493. https:\/\/doi.org\/10.3390\/agriengineering3030032","DOI":"10.3390\/agriengineering3030032"},{"key":"17629_CR45","doi-asserted-by":"publisher","unstructured":"Ayu HR, Surtono A, Apriyanto DK (2021) Deep learning for detection cassava leaf disease. https:\/\/doi.org\/10.1088\/1742-6596\/1751\/1\/012072","DOI":"10.1088\/1742-6596\/1751\/1\/012072"},{"key":"17629_CR46","unstructured":"Sutha P, Nandhu Kishore AH, Jayanthi VE, Periyanan A, Vahima P (2021) Plant disease detection using fuzzy classification. Annals of the Romanian Society for Cell Biology 9430\u20139441. Retrieved from https:\/\/annalsofrscb.ro\/index.php\/journal\/article\/view\/3683"},{"key":"17629_CR47","unstructured":"Kaur N, Devendran V, Verma S (2021) Crop leaf disease classification identification based on ensemble classification. In: Algorithms, Computing and Mathematics Conference, 19\u201320 Aug 2021, Chennai, India"},{"key":"17629_CR48","doi-asserted-by":"publisher","unstructured":"Kaur N, Devendran V (2021) Ensemble classification and feature extraction based plant leaf disease recognition. 2021 9th Int Conf Reliab Infocom Technol Optim (Trends Futur. Dir.) 1\u20134. https:\/\/doi.org\/10.1109\/icrito51393.2021.9596456","DOI":"10.1109\/icrito51393.2021.9596456"},{"key":"17629_CR49","first-page":"2339","volume":"12","author":"N Kaur","year":"2021","unstructured":"Kaur N, Devendran V (2021) Plant leaf disease detection using ensemble classification and feature extraction. Turkish J Comput Math Educ 12:2339\u20132352","journal-title":"Turkish J Comput Math Educ"},{"key":"17629_CR50","doi-asserted-by":"publisher","unstructured":"Kaur N, Devendran V (2021) Plant leaf disease diagnostic system built on feature extraction and ensemble classification. 2021 9th Int. Conf. Reliab. Infocom Technol. Optim. (Trends Futur. Dir).1\u20133. https:\/\/doi.org\/10.1109\/icrito51393.2021.9596070","DOI":"10.1109\/icrito51393.2021.9596070"},{"key":"17629_CR51","first-page":"1135","volume":"17","author":"S Sivagami","year":"2021","unstructured":"Sivagami S, Mohanapriya S (2021) Tomato leaf disease detection using image processing technique. Int J Agric Technol 17:1135\u20131146","journal-title":"Int J Agric Technol"},{"key":"17629_CR52","doi-asserted-by":"publisher","first-page":"17","DOI":"10.1007\/s42161-020-00683-3","volume":"103","author":"VK Shrivastava","year":"2021","unstructured":"Shrivastava VK, Pradhan MK (2021) Rice plant disease classification using color features: a machine learning paradigm. J Plant Pathol 103:17\u201326. https:\/\/doi.org\/10.1007\/s42161-020-00683-3","journal-title":"J Plant Pathol"},{"key":"17629_CR53","doi-asserted-by":"publisher","first-page":"589","DOI":"10.1007\/s11760-020-01780-7","volume":"15","author":"Y Kurmi","year":"2021","unstructured":"Kurmi Y, Gangwar S, Agrawal D, Kumar S, Srivastava HS (2021) Leaf image analysis-based crop diseases classification. Signal Image Video Process 15:589\u2013597. https:\/\/doi.org\/10.1007\/s11760-020-01780-7","journal-title":"Signal Image Video Process"},{"key":"17629_CR54","doi-asserted-by":"publisher","first-page":"717","DOI":"10.3906\/tar-2007-105","volume":"45","author":"A Alkan","year":"2021","unstructured":"Alkan A, Abdullah MU, Abdullah HO, Assaf M, Zhou H (2021) A smart agricultural application: automated detection of diseases in vine leaves usinghybrid deep learning. Turkish J Agric Forestry 45:717\u2013729. https:\/\/doi.org\/10.3906\/tar-2007-105","journal-title":"Turkish J Agric Forestry"},{"key":"17629_CR55","doi-asserted-by":"publisher","unstructured":"Islam MT, Tusher AN (2021) Automatic detection of grape, potato and strawberry leaf diseases using CNN and Image Processing. Lect Notes Netw Syst 238. https:\/\/doi.org\/10.1007\/978-981-16-2641-8_20","DOI":"10.1007\/978-981-16-2641-8_20"},{"key":"17629_CR56","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/3114525","volume":"2022","author":"A Ali","year":"2022","unstructured":"Ali A et al (2022) Detection of deficiency of nutrients in grape leaves using deep network. Math Probl Eng 2022:1\u201312. https:\/\/doi.org\/10.1155\/2022\/3114525","journal-title":"Math Probl Eng"},{"key":"17629_CR57","doi-asserted-by":"publisher","DOI":"10.3390\/agriculture12060887","volume":"12","author":"J Lin","year":"2022","unstructured":"Lin J, Chen X, Pan R, Cao T, Cai J, Chen Y, Peng X (2022) GrapeNet: a lightweight convolutional neural network model for identification of grape leaf diseases. Agriculture 12:887. https:\/\/doi.org\/10.3390\/agriculture12060887","journal-title":"Agriculture"},{"key":"17629_CR58","doi-asserted-by":"publisher","unstructured":"Miaomiao J (2022) Automatic detection, quantification and classification method for plant foliar diseases based on deep learning. Northeast Agric Univ. https:\/\/doi.org\/10.21203\/rs.3.rs-2234059\/v1","DOI":"10.21203\/rs.3.rs-2234059\/v1"},{"key":"17629_CR59","doi-asserted-by":"publisher","unstructured":"Nagi R, Sanjaya ST (2022) Disease identification in grapevine leaf images using fuzzy-PNN. In: 2022 2nd International Conference on Artificial Intelligence and Signal Processing (AISP), 12\u201314 Feb 2022, pp 2\u20135. https:\/\/doi.org\/10.1109\/AISP53593.2022.9760547","DOI":"10.1109\/AISP53593.2022.9760547"},{"key":"17629_CR60","doi-asserted-by":"publisher","unstructured":"Ouhami M, Es-saady Y, Hajj ME, Canals R, Hafiane A (2022) Meteorological data and UAV images for the detection and identification of grapevine disease using deep learning. 2022 E-Health and Bioengineering Conference (EHB). https:\/\/doi.org\/10.1109\/EHB55594.2022.9991443","DOI":"10.1109\/EHB55594.2022.9991443"},{"key":"17629_CR61","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-022-01589-w","author":"R Ramamoorthy","year":"2022","unstructured":"Ramamoorthy R, Kumar ES, Naidu R, Ch A, Shruthi K (2022) Reliable and accurate plant leaf disease detection with treatment suggestions using enhanced deep learning techniques. SN Comput Sci. https:\/\/doi.org\/10.1007\/s42979-022-01589-w","journal-title":"SN Comput Sci"},{"key":"17629_CR62","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1016\/j.procs.2021.11.081","volume":"196","author":"DM Silva","year":"2022","unstructured":"Silva DM, Bernardin T, Fanton K, Nepaul R, Padua L, Sousa JJ, Cunha A (2022) Automatic detection of Flavescense dor\u00e9e grapevine disease in hyperspectral images using machine learning. Procedia Comput Sci 196:125\u2013132. https:\/\/doi.org\/10.1016\/j.procs.2021.11.081","journal-title":"Procedia Comput Sci"},{"key":"17629_CR63","doi-asserted-by":"publisher","unstructured":"Varga D (2022) No-reference image quality assessment with convolutional neural networks and decision fusion. Appl Sci 12. https:\/\/doi.org\/10.3390\/app12010101","DOI":"10.3390\/app12010101"},{"key":"17629_CR64","doi-asserted-by":"publisher","unstructured":"Varga D (2022) No-reference video quality assessment using multi-pooled, saliency weighted deep features and decision fusion. Appl Sci 26. https:\/\/doi.org\/10.3390\/s22062209","DOI":"10.3390\/s22062209"},{"issue":"1","key":"17629_CR65","doi-asserted-by":"publisher","first-page":"235","DOI":"10.1007\/s11119-022-09941-z","volume":"24","author":"Y Chen","year":"2023","unstructured":"Chen Y, Qiufeng W (2023) Grape leaf disease identification with sparse data via generative adversarial networks and convolutional neural networks. Precis Agric 24(1):235\u2013253. https:\/\/doi.org\/10.1007\/s11119-022-09941-z","journal-title":"Precis Agric"},{"key":"17629_CR66","unstructured":"https:\/\/www.kaggle.com\/emmarex\/plantdisease"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17629-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17629-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17629-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T07:23:56Z","timestamp":1715757836000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17629-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,8]]},"references-count":66,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["17629"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17629-3","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,11,8]]},"assertion":[{"value":"8 May 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 October 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 October 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 November 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"We affirm that we have no financial or personal ties to other people or groups that could have an improper influence on our work.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}
