{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:35:59Z","timestamp":1791912959372,"version":"4.3.4"},"reference-count":63,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T00:00:00Z","timestamp":1628467200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Decrease in crop yield and degradation in product quality due to plant diseases such as rust and blast in pearl millet is the cause of concern for farmers and the agriculture industry. The stipulation of expert advice for disease identification is also a challenge for the farmers. The traditional techniques adopted for plant disease detection require more human intervention, are unhandy for farmers, and have a high cost of deployment, operation, and maintenance. Therefore, there is a requirement for automating plant disease detection and classification. Deep learning and IoT-based solutions are proposed in the literature for plant disease detection and classification. However, there is a huge scope to develop low-cost systems by integrating these techniques for data collection, feature visualization, and disease detection. This research aims to develop the \u2018Automatic and Intelligent Data Collector and Classifier\u2019 framework by integrating IoT and deep learning. The framework automatically collects the imagery and parametric data from the pearl millet farmland at ICAR, Mysore, India. It automatically sends the collected data to the cloud server and the Raspberry Pi. The \u2018Custom-Net\u2019 model designed as a part of this research is deployed on the cloud server. It collaborates with the Raspberry Pi to precisely predict the blast and rust diseases in pearl millet. Moreover, the Grad-CAM is employed to visualize the features extracted by the \u2018Custom-Net\u2019. Furthermore, the impact of transfer learning on the \u2018Custom-Net\u2019 and state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19 is shown in this manuscript. Based on the experimental results, and features visualization by Grad-CAM, it is observed that the \u2018Custom-Net\u2019 extracts the relevant features and the transfer learning improves the extraction of relevant features. Additionally, the \u2018Custom-Net\u2019 model reports a classification accuracy of 98.78% that is equivalent to state-of-the-art models viz. Inception ResNet-V2, Inception-V3, ResNet-50, VGG-16, and VGG-19. Although the classification of \u2018Custom-Net\u2019 is comparable to state-of-the-art models, it is effective in reducing the training time by 86.67%. It makes the model more suitable for automating disease detection. This proves that the proposed model is effective in providing a low-cost and handy tool for farmers to improve crop yield and product quality.<\/jats:p>","DOI":"10.3390\/s21165386","type":"journal-article","created":{"date-parts":[[2021,8,9]],"date-time":"2021-08-09T21:41:46Z","timestamp":1628545306000},"page":"5386","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":187,"title":["IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet"],"prefix":"10.3390","volume":"21","author":[{"given":"Nidhi","family":"Kundu","sequence":"first","affiliation":[{"name":"Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Geeta","family":"Rani","sequence":"additional","affiliation":[{"name":"Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vijaypal Singh","family":"Dhaka","sequence":"additional","affiliation":[{"name":"Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kalpit","family":"Gupta","sequence":"additional","affiliation":[{"name":"Department of Computer and Communication Engineering, Manipal University Jaipur, Jaipur 303007, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siddaiah Chandra","family":"Nayak","sequence":"additional","affiliation":[{"name":"ICAR DOS in Biotechnology, University of Mysore Manasagangotri, Mysore 570005, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3136-4029","authenticated-orcid":false,"given":"Sahil","family":"Verma","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Chandigarh University, Mohali 140413, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5206-272X","authenticated-orcid":false,"given":"Muhammad Fazal","family":"Ijaz","sequence":"additional","affiliation":[{"name":"Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9073-5347","authenticated-orcid":false,"given":"Marcin","family":"Wo\u017aniak","sequence":"additional","affiliation":[{"name":"Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,8,9]]},"reference":[{"key":"ref_1","unstructured":"C. Pearl and M (2021, July 06). Improvement, Pearl Millet News. Project Coordinator ICAR\u2014All India Coordinated Research Project on Pearl Millet., Available online: http:\/\/www.aicpmip.res.in\/pmnews.html."},{"key":"ref_2","unstructured":"Authority, P.B.Y., and Delhi, N.E.W. (2021, July 06). The Gazette of India. 2018; pp. 1\u20132. Available online: https:\/\/en.wikipedia.org\/wiki\/The_Gazette_of_India."},{"key":"ref_3","unstructured":"ICRISAT (2021, July 06). Millet in Schools by Union Minitry. Available online: https:\/\/www.icrisat.org\/indias-millets-makeover-set-to-reach-poor-school-meals\/."},{"key":"ref_4","unstructured":"(2021, July 06). Climate Change Impact. Available online: https:\/\/thewire.in\/environment\/millets-india-food-basket-climate-change."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s12571-016-0557-y","article-title":"Crops that feed the world 11. Pearl Millet (Pennisetum glaucum L.): An important source of food security, nutrition and health in the arid and semi-arid tropics","volume":"8","author":"Jukanti","year":"2016","journal-title":"Food Secur."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Chougule, A., Jha, V.K., and Mukhopadhyay, D. (2016, January 22\u201324). Using IoT for integrated pest management. Proceedings of the 2016 International Conference on Internet of Things and Applications (IOTA), Prune, India.","DOI":"10.1109\/IOTA.2016.7562688"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1007\/s12571-017-0659-1","article-title":"Crop health and its global impacts on the components of food security","volume":"9","author":"Savary","year":"2017","journal-title":"Food Secur."},{"key":"ref_8","first-page":"151","article-title":"How to feed the world in 2050","volume":"228","author":"Jansen","year":"2011","journal-title":"Arch. Kriminol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1023\/B:CLIM.0000043138.67784.27","article-title":"Effects of Greenhouse Gas Emissions on World Agriculture, Food Consumption, and Economic Welfare","volume":"66","author":"Darwin","year":"2004","journal-title":"Clim. Chang."},{"key":"ref_10","first-page":"1","article-title":"Development of a Real-Time Stroke Detection System for Elderly Drivers Using Quad-Chamber Air Cushion and IoT Devices","volume":"2018","author":"Park","year":"2018","journal-title":"SAE Tech. Pap."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"213574","DOI":"10.1109\/ACCESS.2020.3040437","article-title":"HealthSOS: Real-Time Health Monitoring System for Stroke Prognostics","volume":"8","author":"Hussain","year":"2020","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Park, S.J., Hussain, I., Hong, S., Kim, D., Park, H., and Benjamin, H.C.M. (2020, January 4\u20136). Real-time gait monitoring system for consumer stroke prediction service. Proceedings of the 2020 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE46568.2020.9043098"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"El-Jerjawi, N.S., and Abu-Naser, S.S. (2020). Diabetes prediction using artificial neural network. Int. J. Adv. Sci. Technol., 327\u2013339.","DOI":"10.1016\/B978-0-12-819061-6.00014-8"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Oza, M.G., Rani, G., and Dhaka, V.S. (2021). Glaucoma Detection Using Convolutional Neural Networks. Handbook of Research on Disease Prediction through Data Analytics and Machine Learning, IGI Global.","DOI":"10.4018\/978-1-7998-2742-9.ch001"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rani, G., Oza, M.G., Dhaka, V.S., Pradhan, N., Verma, S., and Rodrigues, J.J. (2021). Applying Deep Learning for Genome Detection of Coronavirus. Multimed. Syst., 1\u201312.","DOI":"10.21203\/rs.3.rs-93564\/v1"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kundu, N., Rani, G., and Dhaka, V.S. (2020, January 11\u201313). Machine Learning and IoT based Disease Predictor and Alert Generator System. Proceedings of the 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC), Erode, India.","DOI":"10.1109\/ICCMC48092.2020.ICCMC-000142"},{"key":"ref_17","first-page":"155","article-title":"AI-Based Yield Prediction and Smart Irrigation","volume":"2","author":"Sinwar","year":"2019","journal-title":"Stud. Big Data"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.biosystemseng.2016.08.024","article-title":"Plant species classification using deep convolutional neural network","volume":"151","author":"Dyrmann","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.3389\/fpls.2016.01419","article-title":"Using Deep Learning for Image-Based Plant Disease Detection","volume":"7","author":"Mohanty","year":"2016","journal-title":"Front. Plant Sci."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kundu, N., Rani, G., and Dhaka, V.S. (2020, January 6\u20138). A Comparative Analysis of Deep Learning Models Applied for Disease Classification in Bell Pepper. Proceedings of the 2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC), Himachal Pradesh, India.","DOI":"10.1109\/PDGC50313.2020.9315821"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"931","DOI":"10.1016\/j.procs.2016.07.275","article-title":"Field Monitoring and Automation Using IOT in Agriculture Domain","volume":"93","author":"Mohanraj","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A Survey on Transfer Learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"115287","DOI":"10.1109\/ACCESS.2020.3001237","article-title":"MDFC\u2013ResNet: An Agricultural IoT System to Accurately Recognize Crop Diseases","volume":"8","author":"Hu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_24","unstructured":"Feng, J.Z.B., and Li, G.Z. (2019). DCNN Transfer Learning and Multi-Model Integration for Disease and Weed Identification, Springer."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1007\/978-1-4757-5221-2_9","article-title":"Diseases of Millet","volume":"Volume 500030","author":"Nyvall","year":"1989","journal-title":"Field Crop Diseases Handbook"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"296","DOI":"10.1007\/s40974-017-0074-7","article-title":"Traditional agriculture: A climate-smart approach for sustainable food production","volume":"2","author":"Singh","year":"2017","journal-title":"Energy Ecol. Environ."},{"key":"ref_27","unstructured":"Brahimi, M. (2018). Deep Learning for Plants Diseases, Springer International Publishing."},{"key":"ref_28","first-page":"012086","article-title":"Disorder detection of tomato plant(solanum lycopersicum) using IoT and machine learning","volume":"1432","author":"Khan","year":"2020","journal-title":"J. Phys."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Kitpo, N., and Inoue, M. (2018, January 12\u201313). Early rice disease detection and position mapping system using drone and IoT architecture. Proceedings of the 2018 12th South East Asian Technical University Consortium (SEATUC), Yogyakarta, Indonesia.","DOI":"10.1109\/SEATUC.2018.8788863"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Thorat, A., Kumari, S., and Valakunde, N.D. (2017, January 20\u201322). An IoT based smart solution for leaf disease detection. Proceedings of the 2017 International Conference on Big Data, IoT and Data Science (BID), Pune, India.","DOI":"10.1109\/BID.2017.8336597"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chapaneri, R., Desai, M., Goyal, A., Ghose, S., and Das, S. (2020, January 3\u20134). Plant Disease Detection: A Comprehensive Survey. Proceedings of the 2020 3rd International Conference on Communication System, Computing and IT Applications (CSCITA), Mumbai, India.","DOI":"10.1109\/CSCITA47329.2020.9137779"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Abdullahi, H.S., Sheriff, R., and Mahieddine, F. (2017, January 16\u201318). Convolution neural network in precision agriculture for plant image recognition and classification. Proceedings of the 2017 Seventh International Conference on Innovative Computing Technology (INTECH), Sao Carlos, Brazil.","DOI":"10.1109\/INTECH.2017.8102436"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"43721","DOI":"10.1109\/ACCESS.2019.2907383","article-title":"Multilayer Convolution Neural Network for the Classification of Mango Leaves Infected by Anthracnose Disease","volume":"7","author":"Singh","year":"2019","journal-title":"IEEE Access"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-019-0479-8","article-title":"Plant disease identification using explainable 3D deep learning on hyperspectral images","volume":"15","author":"Nagasubramanian","year":"2019","journal-title":"Plant Methods"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1852","DOI":"10.3389\/fpls.2017.01852","article-title":"Deep Learning for Image-Based Cassava Disease Detection","volume":"8","author":"Ramcharan","year":"2017","journal-title":"Front. Plant Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.biosystemseng.2019.02.002","article-title":"Plant disease identification from individual lesions and spots using deep learning","volume":"180","author":"Barbedo","year":"2019","journal-title":"Biosyst. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"59069","DOI":"10.1109\/ACCESS.2019.2914929","article-title":"Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks","volume":"7","author":"Jiang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1007\/s40030-019-00390-y","article-title":"Identification of Soybean Leaf Diseases via Deep Learning","volume":"100","author":"Wu","year":"2019","journal-title":"J. Inst. Eng. (India) Ser. A"},{"key":"ref_39","unstructured":"He, L., Zhang, X., Ren, S., and Sun, J. (2020). Deep Residual learning for image recognition. arXiv."},{"key":"ref_40","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., and Rabinovich, A. (2017, January 21\u201326). Going Deeper with Convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.patrec.2014.01.008","article-title":"A review of unsupervised feature learning and deep learning for time-series modeling","volume":"42","author":"Karlsson","year":"2014","journal-title":"Pattern Recognit. Lett."},{"key":"ref_42","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (July, January 26). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., van der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_44","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., and Adam, H. (2017). Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv."},{"key":"ref_45","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and<0.5 MB model size. arXiv."},{"key":"ref_46","unstructured":"(2020, September 12). Review: Inception-v3\u20141st Runner Up (Image Classification) in ILSVRC. Available online: https:\/\/medium.com\/@sh.tsang\/review-inception-v3-1st-runner-up-image-classification-in-ilsvrc-2015-17915421f77c."},{"key":"ref_47","unstructured":"(2020, December 10). AI Challenger Crop Disease Detection. Available online: https:\/\/pan.baidu.com\/s\/1TH9qL7Wded2Qiz03wHTDLw#list\/path=%2F."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1016\/j.compag.2018.01.009","article-title":"Deep learning models for plant disease detection and diagnosis","volume":"145","author":"Ferentinos","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Shah, J.P., Prajapati, H.B., and Dabhi, V.K. (2016, January 10\u201311). A survey on detection and classification of rice plant diseases. Proceedings of the 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), Piscataway, NJ, USA.","DOI":"10.1109\/ICCTAC.2016.7567333"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"101675","DOI":"10.1016\/j.bspc.2019.101675","article-title":"A novel data augmentation method to enhance deep neural networks for detection of atrial fibrillation","volume":"56","author":"Cao","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.compind.2019.02.003","article-title":"Deep neural networks with transfer learning in millet crop images","volume":"108","author":"Coulibaly","year":"2019","journal-title":"Comput. Ind."},{"key":"ref_53","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_54","first-page":"55","article-title":"Downy Mildew of Pearl Millet and its Management","volume":"342304","author":"Shetty","year":"2016","journal-title":"Indian Counc. Agric. Res. Mandor Jodhpur"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Garg, D., and Alam, M. (2020). Deep learning and IoT for agricultural applications. Internet of Things (IoT), Springer.","DOI":"10.1007\/978-3-030-37468-6_14"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.procs.2018.07.063","article-title":"Review on Application of Drone Systems in Precision Agriculture","volume":"133","author":"Mogili","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"180750","DOI":"10.1109\/ACCESS.2020.3024891","article-title":"An AIoT Based Smart Agricultural System for Pests Detection","volume":"8","author":"Chen","year":"2020","journal-title":"IEEE Access"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Materne, N., and Inoue, M. (2018, January 12\u201313). IoT Monitoring System for Early Detection of Agricultural Pests and Diseases. Proceedings of the 2018 12th South East Asian Technical University Consortium (SEATUC), Piscataway, NJ, USA.","DOI":"10.1109\/SEATUC.2018.8788860"},{"key":"ref_59","first-page":"13","article-title":"P Screening techniques forpearl millet","volume":"96","author":"Thakur","year":"2008","journal-title":"Flexo Tech."},{"key":"ref_60","first-page":"79","article-title":"A deep learning-based approach for banana leaf diseases classification","volume":"266","author":"Amara","year":"2017","journal-title":"Lect. Notes Inform."},{"key":"ref_61","first-page":"566","article-title":"Performance analysis of deep learning CNN models for disease detection in plants using image segmentation","volume":"7","author":"Sharma","year":"2020","journal-title":"Inf. Process. Agric."},{"key":"ref_62","unstructured":"(2020, October 02). ImageNet Dataset. Available online: Image-net.org."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/16\/5386\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:43:11Z","timestamp":1760164991000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/16\/5386"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,9]]},"references-count":63,"journal-issue":{"issue":"16","published-online":{"date-parts":[[2021,8]]}},"alternative-id":["s21165386"],"URL":"https:\/\/doi.org\/10.3390\/s21165386","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,8,9]]}}}