{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,9]],"date-time":"2026-06-09T18:30:47Z","timestamp":1781029847436,"version":"3.54.1"},"reference-count":54,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,6,1]],"date-time":"2024-06-01T00:00:00Z","timestamp":1717200000000},"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":["Wireless Pers Commun"],"published-print":{"date-parts":[[2024,6]]},"DOI":"10.1007\/s11277-024-11374-y","type":"journal-article","created":{"date-parts":[[2024,7,2]],"date-time":"2024-07-02T16:02:13Z","timestamp":1719936133000},"page":"2275-2298","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":46,"title":["Classification of Different Plant Species Using Deep Learning and Machine Learning Algorithms"],"prefix":"10.1007","volume":"136","author":[{"given":"Siddharth Singh","family":"Chouhan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Uday Pratap","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Utkarsh","family":"Sharma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sanjeev","family":"Jain","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2024,7,2]]},"reference":[{"key":"11374_CR1","doi-asserted-by":"publisher","first-page":"108796","DOI":"10.1016\/j.measurement.2020.108796","volume":"171","author":"SS Chouhan","year":"2021","unstructured":"Chouhan, S. S., et al. (2021). Leaf disease segmentation and classification of Jatropha curcas L. and Pongamia pinnata L. biofuel plants using computer vision based approaches. Measurement, 171, 108796. https:\/\/doi.org\/10.1016\/j.measurement.2020.108796","journal-title":"Measurement"},{"key":"11374_CR2","doi-asserted-by":"publisher","first-page":"1807","DOI":"10.1007\/s11277-021-08970-7","volume":"122","author":"A Srivastava","year":"2022","unstructured":"Srivastava, A., & Das, D. K. (2022). A comprehensive review on the application of internet of thing (IoT) in smart agriculture. Wireless Personal Communications, 122, 1807\u20131837. https:\/\/doi.org\/10.1007\/s11277-021-08970-7","journal-title":"Wireless Personal Communications"},{"key":"11374_CR3","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05235-5","author":"S Uguz","year":"2020","unstructured":"Uguz, S., & Uysal, N. (2020). Classification of olive leaf diseases using deep convolutional neural networks. Neural Computing and Applications. https:\/\/doi.org\/10.1007\/s00521-020-05235-5","journal-title":"Neural Computing and Applications"},{"key":"11374_CR4","doi-asserted-by":"publisher","first-page":"499","DOI":"10.1007\/s00354-019-00072-0","volume":"37","author":"W Jearanaiwongkul","year":"2019","unstructured":"Jearanaiwongkul, W., et al. (2019). A semantic-based framework for rice plant disease management. New Gener Comput, 37, 499\u2013523. https:\/\/doi.org\/10.1007\/s00354-019-00072-0","journal-title":"New Gener Comput"},{"key":"11374_CR5","doi-asserted-by":"publisher","first-page":"1757","DOI":"10.1007\/s11277-021-08734-3","volume":"121","author":"SS Chouhan","year":"2021","unstructured":"Chouhan, S. S., Singh, U. P., & Jain, S. (2021). Automated plant leaf disease detection and classification using fuzzy based function network. Wireless Personal Communications, 121, 1757\u20131779. https:\/\/doi.org\/10.1007\/s11277-021-08734-3","journal-title":"Wireless Personal Communications"},{"key":"11374_CR6","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2018.03.032","author":"EC Too","year":"2018","unstructured":"Too, E. C., et al. (2018). A comparative study of fine-tuning deep learning models for plant disease identification. Computers and Electronics in Agriculture. https:\/\/doi.org\/10.1016\/j.compag.2018.03.032","journal-title":"Computers and Electronics in Agriculture"},{"key":"11374_CR7","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05064-6","author":"S Rasti","year":"2020","unstructured":"Rasti, S., et al. (2020). Crop growth stage estimation prior to canopy closure using deep learning algorithms. Neural Computing and Applications. https:\/\/doi.org\/10.1007\/s00521-020-05064-6","journal-title":"Neural Computing and Applications"},{"key":"11374_CR8","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1016\/j.measurement.2018.10.039","volume":"133","author":"X Qiao","year":"2019","unstructured":"Qiao, X., et al. (2019). Underwater sea cucumber identification based on principal component analysis and support vector machine. Measurement, 133, 444\u2013455. https:\/\/doi.org\/10.1016\/j.measurement.2018.10.039","journal-title":"Measurement"},{"key":"11374_CR9","doi-asserted-by":"publisher","first-page":"611","DOI":"10.1007\/s11831-019-09324-0","volume":"27","author":"SS Chouhan","year":"2020","unstructured":"Chouhan, S. S., et al. (2020). Applications of computer vision in plant pathology: a survey. Archives of Computational Methods in Engineering, 27, 611\u2013632. https:\/\/doi.org\/10.1007\/s11831-019-09324-0","journal-title":"Archives of Computational Methods in Engineering"},{"key":"11374_CR10","doi-asserted-by":"publisher","first-page":"22","DOI":"10.1016\/j.measurement.2018.05.037","volume":"126","author":"S Sabzi","year":"2018","unstructured":"Sabzi, S., et al. (2018). Using video processing to classify potato plant and three types of weed using hybrid of artificial neural network and particle swarm algorithm. Measurement, 126, 22\u201336. https:\/\/doi.org\/10.1016\/j.measurement.2018.05.037","journal-title":"Measurement"},{"key":"11374_CR11","doi-asserted-by":"publisher","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","volume":"147","author":"A Kamilaris","year":"2018","unstructured":"Kamilaris, A., et al. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70\u201390. https:\/\/doi.org\/10.1016\/j.compag.2018.02.016","journal-title":"Computers and Electronics in Agriculture"},{"key":"11374_CR12","doi-asserted-by":"publisher","first-page":"43721","DOI":"10.1109\/ACCESS.2019.2907383","volume":"7","author":"UP Singh","year":"2019","unstructured":"Singh, U. P., et al. (2019). Multilayer convolution neural network for the classification of mango leaves infected by anthracnose disease. IEEE Access, 7, 43721\u201343729. https:\/\/doi.org\/10.1109\/ACCESS.2019.2907383","journal-title":"IEEE Access"},{"key":"11374_CR13","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-019-04228-3","author":"R Ahila Priyadharshini","year":"2019","unstructured":"Ahila Priyadharshini, R., et al. (2019). Maize leaf disease classification using deep convolutional neural networks. Neural Computing and Applications. https:\/\/doi.org\/10.1007\/s00521-019-04228-3","journal-title":"Neural Computing and Applications"},{"key":"11374_CR14","doi-asserted-by":"publisher","first-page":"8852","DOI":"10.1109\/ACCESS.2018.2800685","volume":"6","author":"SS Chouhan","year":"2018","unstructured":"Chouhan, S. S., et al. (2018). Bacterial foraging optimization based radial basis function neural network (BRBFNN) for identification and classification of plant leaf diseases: An automatic approach towards plant pathology. IEEE Access, 6, 8852\u20138863. https:\/\/doi.org\/10.1109\/ACCESS.2018.2800685","journal-title":"IEEE Access"},{"key":"11374_CR15","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-04797-8","author":"SH Bhojani","year":"2020","unstructured":"Bhojani, S. H., & Bhatt, N. (2020). Wheat crop yield prediction using new activation functions in neural network. Neural Computing and Applications. https:\/\/doi.org\/10.1007\/s00521-020-04797-8","journal-title":"Neural Computing and Applications"},{"key":"11374_CR16","doi-asserted-by":"publisher","DOI":"10.1007\/s11277-020-07279-1","author":"SS Chouhan","year":"2020","unstructured":"Chouhan, S. S., et al. (2020). Web facilitated anthracnose disease segmentation from the leaf of mango tree using radial basis function (RBF) neural network. Wireless Personal Communications. https:\/\/doi.org\/10.1007\/s11277-020-07279-1","journal-title":"Wireless Personal Communications"},{"key":"11374_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-020-10038-w","author":"D Bisen","year":"2020","unstructured":"Bisen, D. (2020). Deep convolutional neural network based plant species recognition through features of leaf. Multimedia Tools and Applications. https:\/\/doi.org\/10.1007\/s11042-020-10038-w","journal-title":"Multimedia Tools and Applications"},{"key":"11374_CR18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105616","volume":"176","author":"A Aquino","year":"2020","unstructured":"Aquino, A., et al. (2020). Identification of olive fruit, in intensive olive orchards, by means of its morphological structure using convolutional neural networks. Computers and Electronics in Agriculture, 176, 105616. https:\/\/doi.org\/10.1016\/j.compag.2020.105616","journal-title":"Computers and Electronics in Agriculture"},{"key":"11374_CR19","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.biosystemseng.2019.12.003","volume":"190","author":"AZ da Costa","year":"2020","unstructured":"da Costa, A. Z., et al. (2020). Computer vision based detection of external defects on tomatoes using deep learning. Biosystems Engineering, 190, 131\u2013144. https:\/\/doi.org\/10.1016\/j.biosystemseng.2019.12.003","journal-title":"Biosystems Engineering"},{"key":"11374_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.105002","author":"L Wu","year":"2019","unstructured":"Wu, L., et al. (2019). A deep learning model to recognize food contaminating beetle species based on elytra fragments. Computers and Electronics in Agriculture. https:\/\/doi.org\/10.1016\/j.compag.2019.105002","journal-title":"Computers and Electronics in Agriculture"},{"key":"11374_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/s42979-020-0094-9","author":"S Srivastava","year":"2020","unstructured":"Srivastava, S., et al. (2020). A novel deep learning framework approach for sugarcane disease detection. SN Computer Science. https:\/\/doi.org\/10.1007\/s42979-020-0094-9","journal-title":"SN Computer Science"},{"key":"11374_CR22","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1016\/j.compag.2019.01.041","volume":"158","author":"A Kaya","year":"2019","unstructured":"Kaya, A., et al. (2019). Analysis of transfer learning for deep neural network based plant classification models. Computers and Electronics in Agriculture, 158, 20\u201329. https:\/\/doi.org\/10.1016\/j.compag.2019.01.041","journal-title":"Computers and Electronics in Agriculture"},{"key":"11374_CR23","doi-asserted-by":"publisher","first-page":"16347","DOI":"10.1007\/s00500-020-04946-0","volume":"24","author":"MP Vaishnnave","year":"2020","unstructured":"Vaishnnave, M. P., et al. (2020). Automatic method for classification of groundnut diseases using deep convolutional neural network. Soft Computing, 24, 16347\u201316360. https:\/\/doi.org\/10.1007\/s00500-020-04946-0","journal-title":"Soft Computing"},{"key":"11374_CR24","doi-asserted-by":"publisher","DOI":"10.1109\/LGRS.2019.2932385","author":"EC Tetila","year":"2019","unstructured":"Tetila, E. C., et al. (2019). Automatic recognition of soybean leaf diseases using UAV images and deep convolutional neural networks. IEEE Geoscience and Remote Sensing Letters. https:\/\/doi.org\/10.1109\/LGRS.2019.2932385","journal-title":"IEEE Geoscience and Remote Sensing Letters"},{"key":"11374_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.104906","author":"K Thenmozhi","year":"2019","unstructured":"Thenmozhi, K., & SrinivasuluReddy, U. (2019). Crop pest classification based on deep convolutional neural network and transfer learning. Computers and Electronics in Agriculture. https:\/\/doi.org\/10.1016\/j.compag.2019.104906","journal-title":"Computers and Electronics in Agriculture"},{"key":"11374_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiia.2020.03.001","author":"BS Anami","year":"2020","unstructured":"Anami, B. S., et al. (2020). Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images. Artificial Intelligence in Agriculture. https:\/\/doi.org\/10.1016\/j.aiia.2020.03.001","journal-title":"Artificial Intelligence in Agriculture"},{"key":"11374_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.105162","author":"JGM Esgario","year":"2020","unstructured":"Esgario, J. G. M., et al. (2020). Deep learning for classification and severity estimation of coffee leaf biotic stress. Computers and Electronics in Agriculture. https:\/\/doi.org\/10.1016\/j.compag.2019.105162","journal-title":"Computers and Electronics in Agriculture"},{"key":"11374_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.postharvbio.2019.05.023","author":"TT Le","year":"2016","unstructured":"Le, T. T., et al. (2016). Deep learning for noninvasive classification of clustered horticultural crops\u2014A case for banana fruit tiers. Postharvest Biology and Technology. https:\/\/doi.org\/10.1016\/j.postharvbio.2019.05.023","journal-title":"Postharvest Biology and Technology"},{"key":"11374_CR29","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1016\/j.compind.2019.02.003","volume":"108","author":"S Coulibaly","year":"2020","unstructured":"Coulibaly, S., et al. (2020). Deep neural networks with transfer learning in millet crop images. Computers in Industry, 108, 115\u2013120. https:\/\/doi.org\/10.1016\/j.compind.2019.02.003","journal-title":"Computers in Industry"},{"key":"11374_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2019.105108","author":"H Kang","year":"2019","unstructured":"Kang, H., & Chen, C. (2019). Fast implementation of real-time fruit detection in apple orchards using deep learning. Computers and Electronics in Agriculture. https:\/\/doi.org\/10.1016\/j.compag.2019.105108","journal-title":"Computers and Electronics in Agriculture"},{"key":"11374_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.113588","author":"H Cecotti","year":"2020","unstructured":"Cecotti, H., et al. (2020). Grape detection with convolutional neural networks. Expert Systems with Applications. https:\/\/doi.org\/10.1016\/j.eswa.2020.113588","journal-title":"Expert Systems with Applications"},{"key":"11374_CR32","doi-asserted-by":"publisher","DOI":"10.1007\/s40030-019-00390-y","author":"Wu Qiufeng","year":"2019","unstructured":"Qiufeng, Wu., et al. (2019). Identification of soybean leaf diseases via deep learning. Journal of The Institution of Engineers (India): Series A. https:\/\/doi.org\/10.1007\/s40030-019-00390-y","journal-title":"Journal of The Institution of Engineers (India): Series A"},{"key":"11374_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfoodeng.2020.110102","author":"S Fan","year":"2020","unstructured":"Fan, S., et al. (2020). On line detection of defective apples using computer vision system combined with deep learning methods. Journal of Food Engineering. https:\/\/doi.org\/10.1016\/j.jfoodeng.2020.110102","journal-title":"Journal of Food Engineering"},{"key":"11374_CR34","doi-asserted-by":"publisher","DOI":"10.1007\/s00034-019-01041-0","author":"A Khamparia","year":"2019","unstructured":"Khamparia, A., et al. (2019). Seasonal crops disease prediction and classification using deep convolutional encoder network. Circuits, Systems, and Signal Processing. https:\/\/doi.org\/10.1007\/s00034-019-01041-0","journal-title":"Circuits, Systems, and Signal Processing"},{"key":"11374_CR35","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1016\/j.cogsys.2018.06.008","volume":"52","author":"X Zhu","year":"2018","unstructured":"Zhu, X., et al. (2018). Method of plant leaf recognition based on improved deep convolutional neural network. Cognitive Systems Research, 52, 223\u2013233. https:\/\/doi.org\/10.1016\/j.cogsys.2018.06.008","journal-title":"Cognitive Systems Research"},{"key":"11374_CR36","doi-asserted-by":"publisher","first-page":"418","DOI":"10.1016\/j.compag.2016.07.003","volume":"127","author":"GL Grinblat","year":"2016","unstructured":"Grinblat, G. L., et al. (2016). Deep learning for plant identification using vein morphological patterns. Computers and Electronics in Agriculture, 127, 418\u2013424. https:\/\/doi.org\/10.1016\/j.compag.2016.07.003","journal-title":"Computers and Electronics in Agriculture"},{"key":"11374_CR37","doi-asserted-by":"publisher","first-page":"228","DOI":"10.1016\/j.neucom.2017.01.018","volume":"235","author":"MM Ghazi","year":"2017","unstructured":"Ghazi, M. M., et al. (2017). Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing, 235, 228\u2013235. https:\/\/doi.org\/10.1016\/j.neucom.2017.01.018","journal-title":"Neurocomputing"},{"key":"11374_CR38","doi-asserted-by":"publisher","first-page":"2379","DOI":"10.1007\/s11277-024-10873-2","volume":"133","author":"A Kumar","year":"2023","unstructured":"Kumar, A., & Sachar, S. (2023). Deep learning techniques in leaf image segmentation and leaf species classification: a survey. Wireless Personal Communications, 133, 2379\u20132410. https:\/\/doi.org\/10.1007\/s11277-024-10873-2","journal-title":"Wireless Personal Communications"},{"key":"11374_CR39","doi-asserted-by":"publisher","first-page":"5609","DOI":"10.1007\/s00500-023-09358-4","volume":"28","author":"SB Jadhav","year":"2024","unstructured":"Jadhav, S. B., & Patil, S. B. (2024). Plant leaf species identification using LBHPG feature extraction and machine learning classifier technique. Soft Computing, 28, 5609\u20135623. https:\/\/doi.org\/10.1007\/s00500-023-09358-4","journal-title":"Soft Computing"},{"key":"11374_CR40","doi-asserted-by":"publisher","first-page":"1094","DOI":"10.1007\/s10661-023-11680-1","volume":"195","author":"M Manaouch","year":"2023","unstructured":"Manaouch, M., et al. (2023). Predicting potential reforestation areas by Quercus ilex (L.) species using machine learning algorithms: Case of upper Ziz, southeastern Morocco. Environmental Monitoring and Assessment, 195, 1094. https:\/\/doi.org\/10.1007\/s10661-023-11680-1","journal-title":"Environmental Monitoring and Assessment"},{"key":"11374_CR41","doi-asserted-by":"publisher","first-page":"2573","DOI":"10.1007\/s11277-023-10555-5","volume":"131","author":"T Meenakshi","year":"2023","unstructured":"Meenakshi, T. (2023). Automatic detection of diseases in leaves of medicinal plants using modified logistic regression algorithm. Wireless Personal Communications, 131, 2573\u20132597. https:\/\/doi.org\/10.1007\/s11277-023-10555-5","journal-title":"Wireless Personal Communications"},{"key":"11374_CR42","doi-asserted-by":"publisher","first-page":"2445","DOI":"10.1007\/s11277-023-10545-7","volume":"131","author":"A Singh","year":"2023","unstructured":"Singh, A., et al. (2023). Smart agriculture framework for automated detection of leaf blast disease in paddy crop using colour slicing and GLCM features based random forest approach. Wireless Personal Communications, 131, 2445\u20132462. https:\/\/doi.org\/10.1007\/s11277-023-10545-7","journal-title":"Wireless Personal Communications"},{"key":"11374_CR43","doi-asserted-by":"publisher","first-page":"1189","DOI":"10.1007\/s11277-023-10476-3","volume":"131","author":"JG Thanikkal","year":"2023","unstructured":"Thanikkal, J. G., et al. (2023). An efficient mobile application for identification of immunity boosting medicinal plants using shape descriptor algorithm. Wireless Personal Communications, 131, 1189\u20131205. https:\/\/doi.org\/10.1007\/s11277-023-10476-3","journal-title":"Wireless Personal Communications"},{"key":"11374_CR44","doi-asserted-by":"publisher","first-page":"1317","DOI":"10.1007\/s11277-023-10333-3","volume":"130","author":"M Tholkapiyan","year":"2023","unstructured":"Tholkapiyan, M., et al. (2023). Performance analysis of rice plant diseases identification and classification methodology. Wireless Personal Communications, 130, 1317\u20131341. https:\/\/doi.org\/10.1007\/s11277-023-10333-3","journal-title":"Wireless Personal Communications"},{"key":"11374_CR45","doi-asserted-by":"publisher","DOI":"10.1186\/s43067-024-00141-5","author":"HM Hama","year":"2024","unstructured":"Hama, H. M., et al. (2024). Houseplant leaf classification system based on deep learning algorithms. Journal of Electrical Systems and Inf Technol. https:\/\/doi.org\/10.1186\/s43067-024-00141-5","journal-title":"Journal of Electrical Systems and Inf Technol"},{"key":"11374_CR46","doi-asserted-by":"publisher","first-page":"1513","DOI":"10.1007\/s00217-024-04490-3","volume":"250","author":"K Kayaalp","year":"2024","unstructured":"Kayaalp, K. (2024). A deep ensemble learning method for cherry classification. European Food Research and Technology, 250, 1513\u20131528. https:\/\/doi.org\/10.1007\/s00217-024-04490-3","journal-title":"European Food Research and Technology"},{"key":"11374_CR47","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-024-19265-x","author":"T Varma","year":"2024","unstructured":"Varma, T., et al. (2024). Automatic mango leaf disease detection using different transfer learning models. Multimed Tools Appl. https:\/\/doi.org\/10.1007\/s11042-024-19265-x","journal-title":"Multimed Tools Appl"},{"key":"11374_CR48","unstructured":"https:\/\/towardsdatascience.com\/data-augmentation-techniques-in-python-f216ef5eed69."},{"key":"11374_CR49","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-020-09813-w","author":"R Cristin","year":"2020","unstructured":"Cristin, R., et al. (2020). Deep neural network based Rider-Cuckoo search algorithm for plant disease detection. Artificial Intelligence Review. https:\/\/doi.org\/10.1007\/s10462-020-09813-w","journal-title":"Artificial Intelligence Review"},{"key":"11374_CR50","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s00354-021-00151-1","volume":"10","author":"U Sharma","year":"2022","unstructured":"Sharma, U., et al. (2022). A transformer-based model for evaluation of information relevance in online social-media: A case study of covid-19 media posts. New Gener. Comput, 10, 1\u201324. https:\/\/doi.org\/10.1007\/s00354-021-00151-1","journal-title":"New Gener. Comput"},{"key":"11374_CR51","doi-asserted-by":"publisher","first-page":"8471","DOI":"10.1007\/s00500-019-03961-0","volume":"23","author":"S Remya","year":"2019","unstructured":"Remya, S., & Sasikala, R. (2019). Classification of rubberized coir fibres using deep learning-based neural fuzzy decision tree approach. Soft Computing, 23, 8471\u20138485. https:\/\/doi.org\/10.1007\/s00500-019-03961-0","journal-title":"Soft Computing"},{"key":"11374_CR52","doi-asserted-by":"publisher","DOI":"10.1007\/s11119-019-09642-0","author":"A Koirala","year":"2019","unstructured":"Koirala, A., et al. (2019). Deep learning for real-time fruit detection and orchard fruit load estimation: Benchmarking of \u2018MangoYOLO.\u2019 Precision Agriculture. https:\/\/doi.org\/10.1007\/s11119-019-09642-0","journal-title":"Precision Agriculture"},{"key":"11374_CR53","doi-asserted-by":"publisher","DOI":"10.1016\/j.compag.2020.105254","author":"S Mao","year":"2020","unstructured":"Mao, S., et al. (2020). Automatic cucumber recognition algorithm for harvesting robots in the natural environment using deep learning and multi-feature fusion. Computers and Electronics in Agriculture. https:\/\/doi.org\/10.1016\/j.compag.2020.105254","journal-title":"Computers and Electronics in Agriculture"},{"key":"11374_CR54","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2020.108650","author":"S Azimi","year":"2021","unstructured":"Azimi, S., et al. (2021). A deep learning approach to measure stress level in plants due to Nitrogen deficiency. Measurement. https:\/\/doi.org\/10.1016\/j.measurement.2020.108650","journal-title":"Measurement"}],"container-title":["Wireless Personal Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-024-11374-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11277-024-11374-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11277-024-11374-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,9]],"date-time":"2024-07-09T13:20:21Z","timestamp":1720531221000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11277-024-11374-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6]]},"references-count":54,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["11374"],"URL":"https:\/\/doi.org\/10.1007\/s11277-024-11374-y","relation":{},"ISSN":["0929-6212","1572-834X"],"issn-type":[{"value":"0929-6212","type":"print"},{"value":"1572-834X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6]]},"assertion":[{"value":"14 June 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 July 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical Approval"}}]}}