{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T15:03:10Z","timestamp":1779894190885,"version":"3.53.1"},"reference-count":55,"publisher":"Springer Science and Business Media LLC","issue":"24","license":[{"start":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T00:00:00Z","timestamp":1705276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T00:00:00Z","timestamp":1705276800000},"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\/s11042-023-17992-1","type":"journal-article","created":{"date-parts":[[2024,1,15]],"date-time":"2024-01-15T08:02:34Z","timestamp":1705305754000},"page":"65663-65685","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Quantum convolution neural network for multi-nutrient detection and stress identification in plant leaves"],"prefix":"10.1007","volume":"83","author":[{"given":"Kummari","family":"Venkatesh","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"K. Jairam","family":"Naik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Achyut","family":"Shankar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,1,15]]},"reference":[{"issue":"7","key":"17992_CR1","doi-asserted-by":"publisher","first-page":"074001","DOI":"10.1088\/1361-6633\/aab406","volume":"81","author":"V Dunjko","year":"2018","unstructured":"Dunjko V, Briegel HJ (2018) Machine learning & artificial intelligence in the quantum domain: a review of recent progress. Rep Prog Phys 81(7):074001","journal-title":"Rep Prog Phys"},{"issue":"1","key":"17992_CR2","first-page":"2165452","volume":"8","author":"A Melnikov","year":"2023","unstructured":"Melnikov A, Kordzanganeh M, Alodjants A, Lee RK (2023) Quantum machine learning: From physics to software engineering. Adv Physics: X 8(1):2165452","journal-title":"Adv Physics: X"},{"key":"17992_CR3","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2023\/2842217","volume":"2023","author":"G Chen","year":"2023","unstructured":"Chen G, Long S, Yuan Z, Li W, Peng J (2023) Robustness and explainability of image classification based on QCNN. Quantum Eng 2023:1","journal-title":"Quantum Eng"},{"issue":"7671","key":"17992_CR4","doi-asserted-by":"publisher","first-page":"195","DOI":"10.1038\/nature23474","volume":"549","author":"J Biamonte","year":"2017","unstructured":"Biamonte J, Wittek P, Pancotti N, Rebentrost P, Wiebe N, Lloyd S (2017) Quantum machine learning. Nature 549(7671):195\u2013202","journal-title":"Nature"},{"issue":"6","key":"17992_CR5","doi-asserted-by":"publisher","first-page":"60002","DOI":"10.1209\/0295-5075\/119\/60002","volume":"119","author":"M Schuld","year":"2017","unstructured":"Schuld M, Fingerhuth M, Petruccione F (2017) Implementing a distance-based classifier with a quantum interference circuit. Europhys Lett 119(6):60002","journal-title":"Europhys Lett"},{"key":"17992_CR6","doi-asserted-by":"publisher","first-page":"1069985","DOI":"10.3389\/fphy.2022.1069985","volume":"10","author":"D Bokhan","year":"2022","unstructured":"Bokhan D, Mastiukova AS, Boev AS, Trubnikov DN, Fedorov AK (2022) Multiclass classification using quantum convolutional neural networks with hybrid quantum-classical learning. Frontiers in Physics 10:1069985","journal-title":"Frontiers in Physics"},{"key":"17992_CR7","doi-asserted-by":"publisher","unstructured":"Lloyd S, Mohseni M, Rebentrost P (2013) Quantum algorithms for supervised and unsupervised machine learning. arXiv preprint https:\/\/doi.org\/10.48550\/arXiv.1307.0411","DOI":"10.48550\/arXiv.1307.0411"},{"key":"17992_CR8","doi-asserted-by":"crossref","unstructured":"Shor PW (1994) Algorithms for quantum computation: discrete logarithms and factoring. In Proceedings 35th annual symposium on foundations of computer science (pp. 124\u2013134). IEEE","DOI":"10.1109\/SFCS.1994.365700"},{"issue":"5278","key":"17992_CR9","doi-asserted-by":"publisher","first-page":"1073","DOI":"10.1126\/science.273.5278.1073","volume":"273","author":"S Lloyd","year":"1996","unstructured":"Lloyd S (1996) Universal quantum simulators. Science 273(5278):1073\u20131078","journal-title":"Science"},{"key":"17992_CR10","doi-asserted-by":"publisher","unstructured":"Aaronson S, Chen L (2016) Complexity-theoretic foundations of quantum supremacy experiments. arXiv preprint https:\/\/doi.org\/10.48550\/arXiv.1612.05903","DOI":"10.48550\/arXiv.1612.05903"},{"key":"17992_CR11","doi-asserted-by":"publisher","unstructured":"Wiebe N, Kapoor A, Svore KM (2014) Quantum deep learning. arXiv preprint https:\/\/doi.org\/10.48550\/arXiv.1412.3489","DOI":"10.48550\/arXiv.1412.3489"},{"issue":"7747","key":"17992_CR12","doi-asserted-by":"publisher","first-page":"209","DOI":"10.1038\/s41586-019-0980-2","volume":"567","author":"V Havl\u00ed\u010dek","year":"2019","unstructured":"Havl\u00ed\u010dek V, C\u00f3rcoles AD, Temme K, Harrow AW, Kandala A, Chow JM, Gambetta JM (2019) Supervised learning with quantum-enhanced feature spaces. Nature 567(7747):209\u2013212","journal-title":"Nature"},{"issue":"7671","key":"17992_CR13","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/nature23458","volume":"549","author":"AW Harrow","year":"2017","unstructured":"Harrow AW, Montanaro A (2017) Quantum computational supremacy. Nature 549(7671):203\u2013209","journal-title":"Nature"},{"key":"17992_CR14","doi-asserted-by":"crossref","unstructured":"Tang E (2019) A quantum-inspired classical algorithm for recommendation systems. In Proceedings of the 51st annual ACM SIGACT symposium on theory of computing (pp. 217\u2013228)","DOI":"10.1145\/3313276.3316310"},{"issue":"12","key":"17992_CR15","doi-asserted-by":"publisher","first-page":"1273","DOI":"10.1038\/s41567-019-0648-8","volume":"15","author":"I Cong","year":"2019","unstructured":"Cong I, Choi S, Lukin MD (2019) Quantum convolutional neural networks. Nat Phys 15(12):1273\u20131278","journal-title":"Nat Phys"},{"key":"17992_CR16","doi-asserted-by":"crossref","unstructured":"Kossaifi J, Bulat A, Tzimiropoulos G, Pantic M (2019) T-net: Parametrizing fully convolutional nets with a single high-order tensor. In Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (pp. 7822\u20137831)","DOI":"10.1109\/CVPR.2019.00801"},{"issue":"1","key":"17992_CR17","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1007\/s42484-020-00012-y","volume":"2","author":"M Henderson","year":"2020","unstructured":"Henderson M, Shakya S, Pradhan S, Cook T (2020) Quanvolutional neural networks: powering image recognition with quantum circuits. Quantum Mach Intell 2(1):2","journal-title":"Quantum Mach Intell"},{"key":"17992_CR18","doi-asserted-by":"publisher","unstructured":"Broughton M, Verdon G, McCourt T, Martinez AJ, Yoo JH, Isakov SV, ... Mohseni M (2020) Tensorflow quantum: A software framework for quantum machine learning. arXiv preprint https:\/\/doi.org\/10.48550\/arXiv.2003.02989","DOI":"10.48550\/arXiv.2003.02989"},{"issue":"6","key":"17992_CR19","doi-asserted-by":"publisher","first-page":"062324","DOI":"10.1103\/PhysRevA.98.062324","volume":"98","author":"JG Liu","year":"2018","unstructured":"Liu JG, Wang L (2018) Differentiable learning of quantum circuit born machines. Phys Rev A 98(6):062324","journal-title":"Phys Rev A"},{"key":"17992_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s43673-021-00030-3","volume":"32","author":"S Wei","year":"2022","unstructured":"Wei S, Chen Y, Zhou Z, Long G (2022) A quantum convolutional neural network on NISQ devices. AAPPS Bull 32:1\u201311","journal-title":"AAPPS Bull"},{"issue":"1","key":"17992_CR21","doi-asserted-by":"publisher","first-page":"013231","DOI":"10.1103\/PhysRevResearch.4.013231","volume":"4","author":"SYC Chen","year":"2022","unstructured":"Chen SYC, Wei TC, Zhang C, Yu H, Yoo S (2022) Quantum convolutional neural networks for high energy physics data analysis. Phys Rev Res 4(1):013231","journal-title":"Phys Rev Res"},{"issue":"4","key":"17992_CR22","doi-asserted-by":"publisher","first-page":"044003","DOI":"10.1088\/2058-9565\/ab9f93","volume":"5","author":"Y Li","year":"2020","unstructured":"Li Y, Zhou RG, Xu R, Luo J, Hu W (2020) A quantum deep convolutional neural network for image recognition. Quantum Sci Technol 5(4):044003","journal-title":"Quantum Sci Technol"},{"key":"17992_CR23","doi-asserted-by":"publisher","first-page":"79","DOI":"10.22331\/q-2018-08-06-79","volume":"2","author":"J Preskill","year":"2018","unstructured":"Preskill J (2018) Quantum computing in the NISQ era and beyond. Quantum 2:79","journal-title":"Quantum"},{"key":"17992_CR24","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1038\/nphys3272","volume":"11","author":"S Aaronson","year":"2015","unstructured":"Aaronson S (2015) Read the fine print. Nature Phys 11:291\u2013293. https:\/\/doi.org\/10.1038\/nphys3272","journal-title":"Nature Phys"},{"key":"17992_CR25","volume-title":"Deep learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow I, Bengio Y, Courville A (2016) Deep learning. MIT press"},{"issue":"03","key":"17992_CR26","doi-asserted-by":"publisher","first-page":"1730001","DOI":"10.1142\/S0219749917300017","volume":"15","author":"F Yan","year":"2017","unstructured":"Yan F, Iliyasu AM, Le PQ (2017) Quantum image processing: a review of advances in its security technologies. Int J Quantum Inform 15(03):1730001","journal-title":"Int J Quantum Inform"},{"key":"17992_CR27","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521:436\u2013444. https:\/\/doi.org\/10.1038\/nature14539","journal-title":"Nature"},{"issue":"5","key":"17992_CR28","doi-asserted-by":"publisher","first-page":"179","DOI":"10.1007\/s11128-023-03930-5","volume":"22","author":"S Mishra","year":"2023","unstructured":"Mishra S, Tsai CY (2023) QSurfNet: a hybrid quantum convolutional neural network for surface defect recognition. Quantum Inf Process 22(5):179","journal-title":"Quantum Inf Process"},{"key":"17992_CR29","doi-asserted-by":"publisher","unstructured":"Deva Priya VH, Juliet AV (2022) Automatic detection of Covid-19 based on xception network with optimized CNN. IETE J Res 1\u20139. https:\/\/doi.org\/10.1080\/03772063.2022.2138583","DOI":"10.1080\/03772063.2022.2138583"},{"key":"17992_CR30","doi-asserted-by":"publisher","DOI":"10.1007\/s00530-023-01084-z","author":"B B\u00fcy\u00fckar\u0131kan","year":"2023","unstructured":"B\u00fcy\u00fckar\u0131kan B, \u00dclker E (2023) Convolutional neural network-based apple images classification and image quality measurement by light colors using the color-balancing approach. Multimed Syst. https:\/\/doi.org\/10.1007\/s00530-023-01084-z","journal-title":"Multimed Syst"},{"key":"17992_CR31","doi-asserted-by":"publisher","first-page":"625","DOI":"10.1038\/s42254-021-00348-","volume":"3","author":"M Cerezo","year":"2021","unstructured":"Cerezo M, Arrasmith A, Babbush R et al (2021) Variational quantum algorithms. Nat Rev Phys 3:625\u2013644. https:\/\/doi.org\/10.1038\/s42254-021-00348-","journal-title":"Nat Rev Phys"},{"key":"17992_CR32","doi-asserted-by":"publisher","unstructured":"Farhi E, Neven H (2018) Classification with quantum neural networks on near term processors. arXiv preprint https:\/\/doi.org\/10.48550\/arXiv.1802.06002","DOI":"10.48550\/arXiv.1802.06002"},{"issue":"3","key":"17992_CR33","doi-asserted-by":"publisher","first-page":"032308","DOI":"10.1103\/PhysRevA.101.032308","volume":"101","author":"M Schuld","year":"2020","unstructured":"Schuld M, Bocharov A, Svore KM, Wiebe N (2020) Circuit-centric quantum classifiers. Phys Rev A 101(3):032308","journal-title":"Phys Rev A"},{"issue":"4","key":"17992_CR34","first-page":"041011","volume":"11","author":"A Pesah","year":"2021","unstructured":"Pesah A, Cerezo M, Wang S, Volkoff T, Sornborger AT, Coles PJ (2021) Absence of barren plateaus in quantum convolutional neural networks. Phys Rev X 11(4):041011","journal-title":"Phys Rev X"},{"key":"17992_CR35","doi-asserted-by":"publisher","unstructured":"Qin Z, Lu X, Liu D, Nie X, Yin Y, Shen J, Loui AC (2023) Reformulating graph Kernels for self-supervised space-time correspondence learning. IEEE Trans Image Process. https:\/\/doi.org\/10.1109\/TIP.2023.3328485.","DOI":"10.1109\/TIP.2023.3328485"},{"key":"17992_CR36","doi-asserted-by":"crossref","unstructured":"Yan L, Han C, Xu Z, Liu D, Wang Q (n.d.) Prompt learns prompt: exploring knowledge-aware generative prompt collaboration for video captioning. Proc Thirty-Second Int Joint Conf Artif Intell (IJCAI-23), (pp. 1622-1630)","DOI":"10.24963\/ijcai.2023\/180"},{"issue":"5","key":"17992_CR37","doi-asserted-by":"publisher","first-page":"1192","DOI":"10.1109\/JAS.2023.123456","volume":"10","author":"Z Qin","year":"2023","unstructured":"Qin Z, Lu X, Nie X, Liu D, Yin Y, Wang W (2023) Coarse-to-fine video instance segmentation with factorized conditional appearance flows. IEEE\/CAA J Autom Sin 10(5):1192\u20131208","journal-title":"IEEE\/CAA J Autom Sin"},{"key":"17992_CR38","doi-asserted-by":"crossref","unstructured":"Qin Z, Lu X, Nie X, Yin Y, Shen J (2023) Exposing the self-supervised space-time correspondence learning via graph kernels. Proc AAAI Conf Artif Intell 37(2):2110\u20132118","DOI":"10.1609\/aaai.v37i2.25304"},{"key":"17992_CR39","doi-asserted-by":"publisher","unstructured":"Liu D, Liang J, Geng T, Loui A, Zhou T (2023) Tripartite feature enhanced pyramid network for dense prediction. IEEE Trans Image Process. https:\/\/doi.org\/10.1109\/TIP.2023.3272826","DOI":"10.1109\/TIP.2023.3272826"},{"issue":"13","key":"17992_CR40","doi-asserted-by":"publisher","first-page":"130501","DOI":"10.1103\/PhysRevLett.117.130501","volume":"117","author":"V Dunjko","year":"2016","unstructured":"Dunjko V, Taylor JM, Briegel HJ (2016) Quantum-enhanced machine learning. Phys Rev Lett 117(13):130501","journal-title":"Phys Rev Lett"},{"key":"17992_CR41","doi-asserted-by":"crossref","unstructured":"A\u00efmeur E, Brassard G, Gambs S (2006) Machine learning in a quantum world. In Advances in Artificial Intelligence: 19th Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2006, Qu\u00e9bec City, Qu\u00e9bec, Canada, June 7-9, 2006. Proceedings 19 (pp. 431-442). Springer Berlin Heidelberg","DOI":"10.1007\/11766247_37"},{"issue":"1","key":"17992_CR42","doi-asserted-by":"publisher","first-page":"13585","DOI":"10.1038\/s41598-019-49968-3","volume":"9","author":"Y Takeuchi","year":"2019","unstructured":"Takeuchi Y, Morimae T, Hayashi M (2019) Quantum computational universality of hypergraph states with Pauli-X and Z basis measurements. Sci Rep 9(1):13585","journal-title":"Sci Rep"},{"key":"17992_CR43","doi-asserted-by":"publisher","first-page":"103320","DOI":"10.1016\/j.advengsoft.2022.103320","volume":"175","author":"M Janani","year":"2023","unstructured":"Janani M, Jebakumar R (2023) Detection and classification of groundnut leaf nutrient level extraction in RGB images. Adv Eng Softw 175:103320","journal-title":"Adv Eng Softw"},{"key":"17992_CR44","doi-asserted-by":"publisher","unstructured":"Bergholm V, Izaac J, Schuld M, Gogolin C, Ahmed S, Ajith V, ... Killoran N (2018) Pennylane: Automatic differentiation of hybrid quantum-classical computations. arXiv preprint https:\/\/doi.org\/10.48550\/arXiv.1811.04968","DOI":"10.48550\/arXiv.1811.04968"},{"key":"17992_CR45","unstructured":"Venkatesh Kummari KJ (n.d.) Naik.: Groundnut Nutrient Deficiency Dataset. Accessed 02 May 2022. https:\/\/drive.google.com\/file\/d\/1xNRX9gAlqMToWaJ9VbhIXh725dIcyCT\/view?usp=sharing"},{"key":"17992_CR46","doi-asserted-by":"publisher","first-page":"100650","DOI":"10.1016\/j.iot.2022.100650","volume":"21","author":"S Yu","year":"2023","unstructured":"Yu S, Xie L, Huang Q (2023) Inception convolutional vision transformers for plant disease identification. Internet Things 21:100650","journal-title":"Internet Things"},{"key":"17992_CR47","unstructured":"Raksarikon W (n.d.) Nutrient deficiency symptom in rice, Kaggle V1. 2020. Available online: https:\/\/www.kaggle.com\/guy007\/nutrientdeficiencysymptomsinrice\/activity (accessed on 7th March 2023)"},{"key":"17992_CR48","unstructured":"OpenCV: Changing Colour Spaces. OpenCV. https:\/\/docs.opencv.org\/4.x\/df\/d9d\/tutorial py colorspaces.html Accessed 30 Sep 2022"},{"key":"17992_CR49","doi-asserted-by":"publisher","first-page":"100325","DOI":"10.1016\/j.jafr.2022.100325","volume":"9","author":"GC Sunil","year":"2022","unstructured":"Sunil GC, Zhang Y, Koparan C, Ahmed MR, Howatt K, Sun X (2022) Weed and crop species classification using computer vision and deep learning technologies in greenhouse conditions. J Agric Food Res 9:100325. https:\/\/doi.org\/10.1016\/j.jafr.2022.100325","journal-title":"J Agric Food Res"},{"key":"17992_CR50","doi-asserted-by":"publisher","first-page":"103448","DOI":"10.1016\/j.bspc.2021.103448","volume":"73","author":"AK Poyraz","year":"2022","unstructured":"Poyraz AK, Dogan S, Akbal E, Tuncer T (2022) Automated brain disease classification using exemplar deep features. Biomed Sig Process Control 73:103448. https:\/\/doi.org\/10.1016\/j.bspc.2021.103448","journal-title":"Biomed Sig Process Control"},{"issue":"5","key":"17992_CR51","doi-asserted-by":"publisher","first-page":"3786","DOI":"10.1080\/03772063.2020.1780163","volume":"68","author":"S Sampathkumar","year":"2022","unstructured":"Sampathkumar S, Rajeswari R (2022) An automated crop and plant disease identification scheme using cognitive fuzzy C-means algorithm. IETE J Res 68(5):3786\u20133797. https:\/\/doi.org\/10.1080\/03772063.2020.1780163","journal-title":"IETE J Res"},{"key":"17992_CR52","doi-asserted-by":"publisher","first-page":"108431","DOI":"10.1016\/j.patcog.2021.108431","volume":"124","author":"H Chen","year":"2022","unstructured":"Chen H, Liang M, Liu W, Wang W, Liu PX (2022) An approach to boundary detection for 3D point clouds based on DBSCAN clustering. Pattern Recogn 124:108431. https:\/\/doi.org\/10.1016\/j.patcog.2021.108431","journal-title":"Pattern Recogn"},{"key":"17992_CR53","first-page":"85","volume-title":"Mineral Disorders of groundnut","author":"AL Singh","year":"2004","unstructured":"Singh AL, Basu MS, Singh NB (2004) Mineral Disorders of groundnut. National Research Centre for groundnut (ICAR), Junagadh, India, p 85"},{"key":"17992_CR54","unstructured":"Government of Tamilnadu (2020) Expert system for paddy, \u201cNutrient management\u201d. http:\/\/www.agritech.tnau.ac.in\/expert_system\/paddy\/nutrientmanagement.html#disorders. Accessed 6 May 2023"},{"issue":"3","key":"17992_CR55","doi-asserted-by":"publisher","first-page":"101","DOI":"10.1007\/s11128-022-03442-8","volume":"21","author":"Y Jing","year":"2022","unstructured":"Jing Y, Li X, Yang Y, Wu C, Fu W, Hu W, Xu H (2022) RGB image classification with quantum convolutional ansatz. Quantum Inf Process 21(3):101","journal-title":"Quantum Inf Process"}],"container-title":["Multimedia Tools and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17992-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11042-023-17992-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11042-023-17992-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,8]],"date-time":"2024-11-08T01:36:04Z","timestamp":1731029764000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11042-023-17992-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,1,15]]},"references-count":55,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2024,7]]}},"alternative-id":["17992"],"URL":"https:\/\/doi.org\/10.1007\/s11042-023-17992-1","relation":{},"ISSN":["1573-7721"],"issn-type":[{"value":"1573-7721","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,1,15]]},"assertion":[{"value":"17 October 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 December 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 December 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"15 January 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors have reviewed and approved the content of the manuscript, expressing their anticipation for the publication of this paper in the specified journal. We prioritize ethical use of plant imaging data by obtaining informed consent, anonymizing data, and ensuring robust security. Our practices comply with regulations, and we maintain transparency through stakeholder engagement. Periodic ethical reviews guide continuous improvement, addressing privacy concerns and upholding ethical standards.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors assert that they have no identifiable conflicting financial or ethical interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}