{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T17:55:17Z","timestamp":1774288517454,"version":"3.50.1"},"reference-count":47,"publisher":"Springer Science and Business Media LLC","issue":"22","license":[{"start":{"date-parts":[[2021,8,27]],"date-time":"2021-08-27T00:00:00Z","timestamp":1630022400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,8,27]],"date-time":"2021-08-27T00:00:00Z","timestamp":1630022400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Soft Comput"],"published-print":{"date-parts":[[2021,11]]},"DOI":"10.1007\/s00500-021-06139-9","type":"journal-article","created":{"date-parts":[[2021,8,27]],"date-time":"2021-08-27T19:03:03Z","timestamp":1630090983000},"page":"14119-14138","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["A convolutional neural network-driven computer vision system toward identification of species and maturity stage of medicinal leaves: case studies with Neem, Tulsi and Kalmegh leaves"],"prefix":"10.1007","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3959-3718","authenticated-orcid":false,"given":"Gunjan","family":"Mukherjee","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9323-4276","authenticated-orcid":false,"given":"Bipan","family":"Tudu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3850-694X","authenticated-orcid":false,"given":"Arpitam","family":"Chatterjee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,8,27]]},"reference":[{"key":"6139_CR1","unstructured":"Abadi M et al. (2016) TensorFlow: A system for large-scale machine learning. In:12th USENIX Symposium on operating systems design and implementation (OSDI 16), USENIX Association:265\u2013283."},{"key":"6139_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cej.2020.127081","volume":"406","author":"B Ahmed","year":"2021","unstructured":"Ahmed B et al (2021) Experimental investigation, binary modelling and artificial neural network prediction of surfactant adsorption for enhanced oil recovery application. Chem Eng J 406:127081. https:\/\/doi.org\/10.1016\/j.cej.2020.127081","journal-title":"Chem Eng J"},{"key":"6139_CR3","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/7382506","author":"M Alzohairy","year":"2016","unstructured":"Alzohairy M (2016) Therapeutics role of Azadirachta indica (Neem) and their active constituents in diseases prevention and treatment. Evidence-Based Complement Altern Med. https:\/\/doi.org\/10.1155\/2016\/7382506","journal-title":"Evidence-Based Complement Altern Med"},{"issue":"3","key":"6139_CR4","doi-asserted-by":"publisher","first-page":"992","DOI":"10.1007\/s00344-018-09909-2","volume":"38","author":"R Attanayake","year":"2019","unstructured":"Attanayake R et al (2019) The effect of maturity status on biochemical composition, antioxidant activity and anthocyanin biosynthesis gene expression in a pomegranate (Punica granatumL) cultivars with red flowers, yellow peel, and pinkish arils. J Plant Growth Regul 38(3):992\u20131006","journal-title":"J Plant Growth Regul"},{"key":"6139_CR5","doi-asserted-by":"publisher","first-page":"546","DOI":"10.1016\/B978-0-12-809633-8.20351-8","volume-title":"Encyclopedia of bioinformatics and computational biology","author":"D Berrar","year":"2019","unstructured":"Berrar D (2019) Performance measures for binary classification. In: Ranganathan S, Gribskov M, Nakai K, Sch\u00f6nbach C (eds) Encyclopedia of bioinformatics and computational biology. Academic Press, Oxford, pp 546\u2013560. https:\/\/doi.org\/10.1016\/B978-0-12-809633-8.20351-8"},{"key":"6139_CR6","volume-title":"Soft computing techniques and applications: advances in intelligent systems and computing","author":"MR Bhuiyan","year":"2021","unstructured":"Bhuiyan MR et al (2021) MediNET: A deep learning approach to recognize Bangladeshi ordinary medicinal plants using CNN. In: Borah S, Pradhan R, Dey N, Gupta P (eds) Soft computing techniques and applications: advances in intelligent systems and computing. Springer, Singapore"},{"issue":"4","key":"6139_CR7","doi-asserted-by":"publisher","first-page":"283","DOI":"10.2298\/ABS0504283B","volume":"57","author":"B Bojovic","year":"2005","unstructured":"Bojovic B, Stojanovic J (2005) Chlorophyll and carotenoid content in wheat cultivars as a function of mineral nutrition. Arch Biol Sci, Belgrade 57(4):283\u2013290","journal-title":"Arch Biol Sci, Belgrade"},{"key":"6139_CR8","unstructured":"Buitinck L et al. (2013) API design for machine learning software: experiences from the scikit-learn project. In: European conference on machine learning and principles and practices of knowledge discovery in databases. arXiv:1309.0238"},{"key":"6139_CR9","doi-asserted-by":"publisher","DOI":"10.35842\/ijicom.v2i2.28","author":"P Catur","year":"2020","unstructured":"Catur P, Mohammad D, Hasta M (2020) Implementation of CNN for plant leaf classification. Int J Inform Comput. https:\/\/doi.org\/10.35842\/ijicom.v2i2.28","journal-title":"Int J Inform Comput"},{"key":"6139_CR10","doi-asserted-by":"crossref","unstructured":"Chaki J, Parekh R, Bhattacharya S (2015) Recognition of whole and deformed plant leaves using statistical shape features and neuro-fuzzy classifier. In: IEEE Proceedings of 2nd international conference on recent trends in information system (ReTIS): 2015","DOI":"10.1109\/ReTIS.2015.7232876"},{"key":"6139_CR11","unstructured":"Chollet F (2015) Keras. https:\/\/github.com\/fchollet\/keras, Accessed on 10 January 2020"},{"key":"6139_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114312","author":"N Costa","year":"2020","unstructured":"Costa N, Lima M, Rommel B (2020) Evaluation of feature selection methods based on artificial neural network weights. Expert Syst Appl. https:\/\/doi.org\/10.1016\/j.eswa.2020.114312","journal-title":"Expert Syst Appl"},{"issue":"1","key":"6139_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.4018\/IJISMD.2021010101","volume":"12","author":"P Deepalakshmi","year":"2021","unstructured":"Deepalakshmi P, Lavanya K, Srinivasu PN (2021) Plant leaf disease detection using CNN algorithm. Int J Inf Syst Model Des (IJISMD) 12(1):1\u201321","journal-title":"Int J Inf Syst Model Des (IJISMD)"},{"issue":"2","key":"6139_CR14","first-page":"1","volume":"14","author":"HF Eid","year":"2018","unstructured":"Eid HF, Abraham A (2018) Plant species identification using leaf biometrics and swarm optimization: a hybrid PSO GWO, SVM Model. Int J Hybrid Intell Syst 14(2):1\u201311","journal-title":"Int J Hybrid Intell Syst"},{"key":"6139_CR15","unstructured":"Faridi H, Aboonajmi M (2017) Application of machine vision in agricultural products. In: Proceedings 4th iranian international NDT conference, Olympic Hotel, Tehran, Iran, Feb 26\u201327"},{"key":"6139_CR16","doi-asserted-by":"publisher","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","volume":"27","author":"T Fawcett","year":"2006","unstructured":"Fawcett T (2006) An introduction to ROC analysis. Pattern Recogn Lett 27:861\u2013874","journal-title":"Pattern Recogn Lett"},{"issue":"7","key":"6139_CR18","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"E Hinton","year":"2006","unstructured":"Hinton E, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief networks. Neural Comput 18(7):1527\u20131554","journal-title":"Neural Comput"},{"issue":"6","key":"6139_CR19","first-page":"1661","volume":"3","author":"D Jaswal","year":"2014","unstructured":"Jaswal D, Sowmya V, Soman KP (2014) Image classification using convolutional neural networks. Int J Adv Res Technol 3(6):1661\u20131668","journal-title":"Int J Adv Res Technol"},{"key":"6139_CR20","doi-asserted-by":"publisher","first-page":"26","DOI":"10.5391\/IJFIS.2017.17.1.26","volume":"17","author":"WS Jeon","year":"2017","unstructured":"Jeon WS, Rhee SY (2017) Plant leaf recognition using a convolution neural network. Int J Fuzzy Log Intell Syst 17:26\u201334","journal-title":"Int J Fuzzy Log Intell Syst"},{"issue":"6","key":"6139_CR21","first-page":"306","volume":"5","author":"PN Kamble","year":"2015","unstructured":"Kamble PN et al (2015) Estimation of Chlorophyll content in young and adult leaves of some selected plants. Univers J Environ Res Technol 5(6):306\u2013310","journal-title":"Univers J Environ Res Technol"},{"key":"6139_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2019.105933","author":"R Karthik","year":"2019","unstructured":"Karthik R, Hariharan M, Anand S et al (2019) Attention embedded residual CNN for disease detection in tomato leaves. Appl Soft Comput. https:\/\/doi.org\/10.1016\/j.asoc.2019.105933","journal-title":"Appl Soft Comput"},{"key":"6139_CR23","doi-asserted-by":"publisher","first-page":"105933","DOI":"10.1016\/j.asoc.2019.105933","volume":"86","author":"R Karthik","year":"2020","unstructured":"Karthik R, Hariharan M, Anand S et al (2020) Attention embedded residual CNN for disease detection in tomato leaves. Appl Soft Comput 86:105933","journal-title":"Appl Soft Comput"},{"issue":"1","key":"6139_CR24","doi-asserted-by":"publisher","first-page":"17","DOI":"10.18280\/ts.370103","volume":"37","author":"M Keivani","year":"2020","unstructured":"Keivani M, Mazloum J, Sedaghatfar E, Tavakoli MB (2020) Automated analysis of leaf shape, texture, and color features for plant classification. Traitement du Signal 37(1):17\u201328. https:\/\/doi.org\/10.18280\/ts.370103","journal-title":"Traitement du Signal"},{"issue":"3","key":"6139_CR25","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1177\/2156587216671392","volume":"22","author":"S Kumkar","year":"2017","unstructured":"Kumkar S, Dobos GJ, Ramp T (2017) The significance of ayurvedic medicinal plants. J Evid Based Complement Altern Med 22(3):494\u2013501","journal-title":"J Evid Based Complement Altern Med"},{"issue":"7553","key":"6139_CR26","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun Y, Bengio Y, Hinton GR (2015) Deep learning. Nature 521(7553):436\u2013444","journal-title":"Nature"},{"key":"6139_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2021.120331","volume":"225","author":"Y Li","year":"2021","unstructured":"Li Y et al (2021) Towards a comprehensive optimization of engine efficiency and emissions by coupling artificial neural network (ANN) with genetic algorithm (GA). Energy 225:120331.  https:\/\/doi.org\/10.1016\/j.energy.2021.120331","journal-title":"Energy"},{"key":"6139_CR27","unstructured":"Lipton ZC, Berkowitz J (2015) A critical review of recurrent neural networks for sequence learning, arXiv:1506.00019v4 [cs.LG] 17 Oct, 2015."},{"key":"6139_CR28","doi-asserted-by":"publisher","first-page":"356","DOI":"10.3390\/sym13020356","volume":"13","author":"S Mahajan","year":"2021","unstructured":"Mahajan S, Raina A, Gao X-Z, Pandit K (2021) A plant recognition using morphological feature extraction and transfer learning over SVM and AdaBoost. Symmetry 13:356. https:\/\/doi.org\/10.3390\/sym13020356","journal-title":"Symmetry"},{"issue":"3","key":"6139_CR29","doi-asserted-by":"publisher","first-page":"276","DOI":"10.11613\/BM.2012.031","volume":"22","author":"ML McHugh","year":"2012","unstructured":"McHugh ML (2012) Interrater reliability: the kappa statistic. Biochem Med 22(3):276\u2013282","journal-title":"Biochem Med"},{"key":"6139_CR30","doi-asserted-by":"publisher","unstructured":"Panigrahi K, Sahoo A, Das H (2020). A CNN approach for corn leaves disease detection to support digital agricultural system. pp. 678\u2013683. https:\/\/doi.org\/10.1109\/ICOEI48184.2020.9142871.","DOI":"10.1109\/ICOEI48184.2020.9142871"},{"issue":"6","key":"6139_CR32","first-page":"1197","volume":"3","author":"A Rafiqa","year":"2013","unstructured":"Rafiqa A et al (2013) Application of computer vision system in food processing- A review. J Eng Res Appl 3(6):1197\u20131205","journal-title":"J Eng Res Appl"},{"issue":"2","key":"6139_CR33","first-page":"105","volume":"5","author":"B Renuka","year":"2016","unstructured":"Renuka B, Sanjeev B, Ranganathan D (2016) Evaluation of phytoconstituents of Caralluma Nilagiriana by FTIR and UV-VIS spectroscopic analysis. J Pharmacogn Phytochem 5(2):105\u2013108","journal-title":"J Pharmacogn Phytochem"},{"key":"6139_CR34","doi-asserted-by":"publisher","DOI":"10.1155\/2016\/1537325","author":"LMR Rere","year":"2016","unstructured":"Rere LMR, Fanany MI, Arymurthy AM (2016) Metaheuristic algorithms for convolution neural network. Comput Intell Neurosci. https:\/\/doi.org\/10.1155\/2016\/1537325","journal-title":"Comput Intell Neurosci"},{"key":"6139_CR35","doi-asserted-by":"crossref","unstructured":"Salle A, Villavicencio A (2018) Restricted recurrent neural Tensor networks: Exploiting word frequency and compositionality. In: Proceedings of the 56th annual meeting of the association for computational linguistics (Short Papers): 8\u201313, Melbourne, Australia, July 15 \u2013 20","DOI":"10.18653\/v1\/P18-2002"},{"issue":"1","key":"6139_CR36","first-page":"322","volume":"9","author":"AK Santra","year":"2012","unstructured":"Santra AK, Christy CJ (2012) Genetic algorithm and confusion matrix for document clustering. Int J Comput Sci 9(1):322\u2013328","journal-title":"Int J Comput Sci"},{"key":"6139_CR37","doi-asserted-by":"publisher","unstructured":"Sapijaszko G, Mikhael WB (2018) An overview of recent convolutional neural network algorithms for image recognition. In: 2018 IEEE 61st International midwest symposium on circuits and systems (MWSCAS), 2018, pp. 743\u2013746. https:\/\/doi.org\/10.1109\/MWSCAS.2018.8623911","DOI":"10.1109\/MWSCAS.2018.8623911"},{"key":"6139_CR38","doi-asserted-by":"crossref","unstructured":"Sardogan M, Tuncer A, Ozen Y (2018, September). Plant leaf disease detection and classification based on CNN with LVQ algorithm. In: 2018 3rd International conference on computer science and engineering (UBMK) (pp. 382\u2013385). IEEE","DOI":"10.1109\/UBMK.2018.8566635"},{"key":"6139_CR40","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.ecoinf.2018.10.002","volume":"48","author":"BB Traorea","year":"2018","unstructured":"Traorea BB, Foguema BK, Tangara F (2018) Deep convolution neural network for image recognition. Eco Inform 48:257\u2013268","journal-title":"Eco Inform"},{"key":"6139_CR41","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.cam.2018.08.039","volume":"352","author":"M Turkoglu","year":"2019","unstructured":"Turkoglu M, Hanbay D (2019) Recognition of plant leaves: an approach with hybrid features produced by dividing leaf images into two and four parts. Appl Math Comput 352:1\u201314","journal-title":"Appl Math Comput"},{"issue":"1","key":"6139_CR42","first-page":"S1","volume":"11","author":"RK Upadhyay","year":"2017","unstructured":"Upadhyay RK (2017) Tulsi: a holy plant with high medicinal and therapeutic value. Int J Green Pharm (Suppl) 11(1):S1\u2013S12","journal-title":"Int J Green Pharm"},{"key":"6139_CR43","doi-asserted-by":"crossref","unstructured":"Venkataraman D, Mangayarkarasi N (2016) Computer vision based feature extraction of leaves for identification of medicinal values of plants. In: IEEE International conference on computational intelligence and computing research: 978\u20131\u20135090\u20130612\u20130\/16","DOI":"10.1109\/ICCIC.2016.7919637"},{"issue":"4","key":"6139_CR44","first-page":"835","volume":"8","author":"H Verma","year":"2019","unstructured":"Verma H et al (2019) Evaluation of an emerging medicinal crop Kalmegh [Andrographis paniculata (Burm. F.)Wall Ex. Nees] for commercial cultivation and pharmaceutical & industrial uses: a review. J Pharmacogn Phytochem 8(4):835\u2013838","journal-title":"J Pharmacogn Phytochem"},{"key":"6139_CR45","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2019.2897283","author":"G Wang","year":"2019","unstructured":"Wang G et al (2019) A PSO and BFO-based learning strategy applied to faster R-CNN for object detection in autonomous driving. IEEE Access. https:\/\/doi.org\/10.1109\/ACCESS.2019.2897283","journal-title":"IEEE Access"},{"key":"6139_CR46","doi-asserted-by":"publisher","first-page":"114","DOI":"10.1016\/j.swevo.2019.06.002","volume":"49","author":"Y Wang","year":"2019","unstructured":"Wang Y, Zhang H, Zhang G (2019b) cPSO-CNN: An efficient PSO-based algorithm for fine-tuning hyper-parameters of convolutional neural networks. Swarm Evol Comput 49:114\u2013123","journal-title":"Swarm Evol Comput"},{"issue":"12","key":"6139_CR47","doi-asserted-by":"publisher","first-page":"3305","DOI":"10.1073\/pnas.1524473113","volume":"113","author":"P Wilfa","year":"2016","unstructured":"Wilfa P et al (2016) Computer vision cracks the leaf code. PNAS 113(12):3305\u20133310","journal-title":"PNAS"},{"key":"6139_CR48","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s13244-018-0639-9","volume":"9","author":"R Yamashita","year":"2018","unstructured":"Yamashita R et al (2018) Convolutional neural networks: an overview and application in radiology. Insights Imaging 9:611\u2013629","journal-title":"Insights Imaging"},{"key":"6139_CR49","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1845\/1\/012026","volume":"1845","author":"AP Yuanita","year":"2021","unstructured":"Yuanita AP, Esmeralda CD, Ridwan I (2021) Identification of medicinal plant leaves using convolutional neural network. J Phys: Conf Ser 1845:012026. https:\/\/doi.org\/10.1088\/1742-6596\/1845\/1\/012026","journal-title":"J Phys: Conf Ser"}],"container-title":["Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-06139-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00500-021-06139-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00500-021-06139-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T15:41:37Z","timestamp":1634917297000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00500-021-06139-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,27]]},"references-count":47,"journal-issue":{"issue":"22","published-print":{"date-parts":[[2021,11]]}},"alternative-id":["6139"],"URL":"https:\/\/doi.org\/10.1007\/s00500-021-06139-9","relation":{},"ISSN":["1432-7643","1433-7479"],"issn-type":[{"value":"1432-7643","type":"print"},{"value":"1433-7479","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,27]]},"assertion":[{"value":"9 August 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 August 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Authors declare no conflicts of interest\/competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}