{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T15:21:44Z","timestamp":1783437704557,"version":"3.54.6"},"reference-count":45,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T00:00:00Z","timestamp":1641945600000},"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>Agriculture is crucial to the economic prosperity and development of India. Plant diseases can have a devastating influence towards food safety and a considerable loss in the production of agricultural products. Disease identification on the plant is essential for long-term agriculture sustainability. Manually monitoring plant diseases is difficult due to time limitations and the diversity of diseases. In the realm of agricultural inputs, automatic characterization of plant diseases is widely required. Based on performance out of all image-processing methods, is better suited for solving this task. This work investigates plant diseases in grapevines. Leaf blight, Black rot, stable, and Black measles are the four types of diseases found in grape plants. Several earlier research proposals using machine learning algorithms were created to detect one or two diseases in grape plant leaves; no one offers a complete detection of all four diseases. The photos are taken from the plant village dataset in order to use transfer learning to retrain the EfficientNet B7 deep architecture. Following the transfer learning, the collected features are down-sampled using a Logistic Regression technique. Finally, the most discriminant traits are identified with the highest constant accuracy of 98.7% using state-of-the-art classifiers after 92 epochs. Based on the simulation findings, an appropriate classifier for this application is also suggested. The proposed technique\u2019s effectiveness is confirmed by a fair comparison to existing procedures.<\/jats:p>","DOI":"10.3390\/s22020575","type":"journal-article","created":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T09:10:36Z","timestamp":1641978636000},"page":"575","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":197,"title":["Recognition of Leaf Disease Using Hybrid Convolutional Neural Network by Applying Feature Reduction"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3539-0622","authenticated-orcid":false,"given":"Prabhjot","family":"Kaur","sequence":"first","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7692-2349","authenticated-orcid":false,"given":"Shilpi","family":"Harnal","sequence":"additional","affiliation":[{"name":"Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura 140401, Punjab, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8245-4748","authenticated-orcid":false,"given":"Rajeev","family":"Tiwari","sequence":"additional","affiliation":[{"name":"Department of Systemics, School of Computer Science, University of Petroleum and Energy Studies, Bidholi, Dehradun 248007, Uttarakhand, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuchi","family":"Upadhyay","sequence":"additional","affiliation":[{"name":"Department of Allied Health Sciences, School of Health Sciences, University of Petroleum and Energy Studies, Bidholi, Dehradun 248007, Uttarakhand, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Surbhi","family":"Bhatia","sequence":"additional","affiliation":[{"name":"Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, P.O. Box 400, Hofuf 31982, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0612-6005","authenticated-orcid":false,"given":"Arwa","family":"Mashat","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5958-5443","authenticated-orcid":false,"given":"Aliaa M.","family":"Alabdali","sequence":"additional","affiliation":[{"name":"Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 344, Rabigh 21911, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"17608","DOI":"10.1109\/JSEN.2021.3049471","article-title":"Unmanned Aerial Vehicles in Smart Agriculture: Applications, Requirements, and Challenges","volume":"21","author":"Maddikunta","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hang, J., Zhang, D., Chen, P., Zhang, J., and Wang, B. (2019). Classification of Plant Leaf Diseases Based on Improved Convolutional Neural Network. Sensors, 19.","DOI":"10.3390\/s19194161"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"591","DOI":"10.12694\/scpe.v21i4.1714","article-title":"Principles and Practices of Making Agriculture Sustainable: Crop Yield prediction using Random Forest","volume":"21","author":"Basha","year":"2020","journal-title":"Scalable Comput. Pract. Exp."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"14539","DOI":"10.1007\/s11042-018-7092-0","article-title":"Identification of grape diseases using image analysis and BP neural networks","volume":"79","author":"Zhu","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Islam, M., Anh, D., Wahid, K., and Bhowmik, P. (May, January 30). Detection of potato diseases using image segmentation and multiclass support vector machine. Proceedings of the 2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), Windsor, ON, Canada.","DOI":"10.1109\/CCECE.2017.7946594"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nagaraju, M., Chawla, P., Upadhyay, S., and Tiwari, R. (2021). Convolution network model based leaf disease detection using augmentation techniques. Expert Syst., e12885.","DOI":"10.1111\/exsy.12885"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3289801","DOI":"10.1155\/2016\/3289801","article-title":"Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification","volume":"2016","author":"Sladojevic","year":"2016","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kuwata, K., and Shibasaki, R. (2015, January 26\u201331). Estimating crop yields with deep learning and remotely sensed data. Proceedings of the 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Milan, Italy.","DOI":"10.1109\/IGARSS.2015.7325900"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kaur, P., Harnal, S., Tiwari, R., Alharithi, F.S., Almulihi, A.H., Noya, I.D., and Goyal, N. (2021). A Hybrid Convolutional Neural Network Model for Diagnosis of COVID-19 Using Chest X-ray Images. Int. J. Environ. Res. Public Health, 18.","DOI":"10.3390\/ijerph182212191"},{"key":"ref_10","first-page":"299","article-title":"Rice Leaf Diseases Recognition Using Convolutional Neural Networks","volume":"Volume 12447","author":"Hossain","year":"2020","journal-title":"Advanced Data Mining and Applications. ADMA 2020"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"20704","DOI":"10.1109\/JSEN.2021.3100151","article-title":"Ear Recognition Based on Deep Unsupervised Active Learning","volume":"21","author":"Khaldi","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Arbaoui, A., Ouahabi, A., Jacques, S., and Hamiane, M. (2021). Concrete Cracks Detection and Monitoring Using Deep Learning-Based Multiresolution Analysis. Electronics, 10.","DOI":"10.20944\/preprints202106.0194.v1"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1007\/978-981-15-7345-3_19","article-title":"Classification of Banana Leaf Diseases Using Enhanced Gabor Feature Descriptor","volume":"Volume 145","author":"Mary","year":"2021","journal-title":"Inventive Communication and Computational Technologies"},{"key":"ref_14","unstructured":"Kaur, P., and Gautam, V. (2020, January 29\u201330). Plant Biotic Disease Identification and Classification Based on Leaf Image: A Review. In Proceedings of 3rd International Conference on Computing Informatics and Networks, Delhi, India."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101182","DOI":"10.1016\/j.ecoinf.2020.101182","article-title":"Plant leaf disease classification using EfficientNet deep learning model","volume":"61","author":"Atila","year":"2021","journal-title":"Ecol. Inform."},{"key":"ref_16","first-page":"418","article-title":"Automatic grape leaf diseases identification via UnitedModel based on multiple convolutional neural networks","volume":"7","author":"Ji","year":"2019","journal-title":"Inf. Process. Agric."},{"key":"ref_17","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":"2019","journal-title":"Inf. Process. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"108650","DOI":"10.1016\/j.measurement.2020.108650","article-title":"A deep learning approach to measure stress level in plants due to Nitrogen deficiency","volume":"173","author":"Azimi","year":"2020","journal-title":"Measurement"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1383","DOI":"10.1007\/s11554-020-00987-8","article-title":"A novel PCA\u2013whale optimization-based deep neural network model for classification of tomato plant diseases using GPU","volume":"18","author":"Gadekallu","year":"2020","journal-title":"J. Real-Time Image Process."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2328","DOI":"10.1016\/j.procs.2020.03.285","article-title":"Olive Spot Disease Detection and Classification using Analysis of Leaf Image Textures","volume":"167","author":"Sinha","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.procs.2019.09.168","article-title":"Coffee Leaf Disease Recognition Based on Deep Learning and Texture Attributes","volume":"159","author":"Sorte","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Kallam, S., Basha, S.M., Rajput, D.S., Patan, R., Balamurugan, B., and Basha, S.A.K. (2018, January 22\u201323). Evaluating the Performance of Deep Learning Techniques on Classification Using Tensor Flow Application. Proceedings of the 2018 International Conference on Advances in Computing and Communication Engineering (ICACCE), Paris, France.","DOI":"10.1109\/ICACCE.2018.8441674"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2380","DOI":"10.1016\/j.procs.2020.04.258","article-title":"Classification and Grading of Okra-ladies finger using Deep Learning","volume":"171","author":"Raikar","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1211","DOI":"10.1016\/j.procs.2020.09.117","article-title":"Deep learning for grape variety recognition","volume":"176","author":"Franczyk","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Kundu, N., Rani, G., Dhaka, V., Gupta, K., Nayak, S., Verma, S., Ijaz, M., and Wo\u017aniak, M. (2021). IoT and Interpretable Machine Learning Based Framework for Disease Prediction in Pearl Millet. Sensors, 21.","DOI":"10.3390\/s21165386"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Almadhor, A., Rauf, H., Lali, M., Dama\u0161evi\u010dius, R., Alouffi, B., and Alharbi, A. (2021). AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery. Sensors, 21.","DOI":"10.3390\/s21113830"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e352","DOI":"10.7717\/peerj-cs.352","article-title":"Detecting cassava mosaic disease using a deep residual convolutional neural network with distinct block processing","volume":"7","author":"Oyewola","year":"2021","journal-title":"PeerJ Comput. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e12746","DOI":"10.1111\/exsy.12746","article-title":"Cassava disease recognition from low-quality images using enhanced data augmentation model and deep learning","volume":"38","author":"Damasevicius","year":"2021","journal-title":"Expert Syst."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1007\/s11277-020-07590-x","article-title":"Tomato Leaf Disease Classification using Multiple Feature Extraction Techniques","volume":"115","author":"Basavaiah","year":"2020","journal-title":"Wirel. Pers. Commun."},{"key":"ref_30","first-page":"670","article-title":"Machine learning for plant disease detection: An investigative comparison between support vector machine and deep learning","volume":"9","author":"Abdu","year":"2020","journal-title":"IAES Int. J. Artif. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"102589","DOI":"10.1016\/j.scs.2020.102589","article-title":"Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey","volume":"65","author":"Bhattacharya","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Gadekallu, T.R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P.K.R., and Srivastava, G. (2020). Deep neural networks to predict diabetic retinopathy. J. Ambient. Intell. Humaniz. Comput., 1\u201314.","DOI":"10.1007\/s12652-020-01963-7"},{"key":"ref_33","unstructured":"Hughes, D.P., and Salathe, M. (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"101197","DOI":"10.1016\/j.ecoinf.2020.101197","article-title":"VirLeafNet: Automatic analysis and viral disease diagnosis using deep-learning in Vigna mungo plant","volume":"61","author":"Joshi","year":"2020","journal-title":"Ecol. Inform."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1007\/s11042-020-09567-1","article-title":"Tea leaf disease detection using multi-objective image segmentation","volume":"80","author":"Mukhopadhyay","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Jasim, M.A., and Al-Tuwaijari, J.M. (2020, January 16\u201318). Plant Leaf Diseases Detection and Classification Using Image Processing and Deep Learning Techniques. Proceedings of the 2020 International Conference on Computer Science and Software Engineering (CSASE), Duhok, Iraq.","DOI":"10.1109\/CSASE48920.2020.9142097"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Tran, T.-T., Choi, J.-W., Le, T.-T.H., and Kim, J.-W. (2019). A Comparative Study of Deep CNN in Forecasting and Classifying the Macronutrient Deficiencies on Development of Tomato Plant. Appl. Sci., 9.","DOI":"10.3390\/app9081601"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bi, C., Wang, J., Duan, Y., Fu, B., Kang, J.-R., and Shi, Y. (2020). MobileNet Based Apple Leaf Diseases Identification. Mob. Netw. Appl., 1\u20139.","DOI":"10.1007\/s11036-020-01640-1"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.compag.2018.03.032","article-title":"A comparative study of fine-tuning deep learning models for plant disease identification","volume":"161","author":"Too","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_40","unstructured":"Subetha, T., Khilar, R., and Christo, M.S. (2021). A comparative analysis on plant pathology classification using deep learning architecture\u2014Resnet and VGG19. Mater. Today Proc."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Jiang, D., Li, F., Yang, Y., and Yu, S. (2020, January 22\u201324). A Tomato Leaf Diseases Classification Method Based on Deep Learning. Proceedings of the 2020 Chinese Control And Decision Conference (CCDC), Hefei, China.","DOI":"10.1109\/CCDC49329.2020.9164457"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"105393","DOI":"10.1016\/j.compag.2020.105393","article-title":"Using deep transfer learning for image-based plant disease identification","volume":"173","author":"Chen","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"105712","DOI":"10.1016\/j.compag.2020.105712","article-title":"Identification of cash crop diseases using automatic image segmentation algorithm and deep learning with expanded dataset","volume":"177","author":"Xiong","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1","DOI":"10.4236\/oalib.1106296","article-title":"Deep Learning Convolution Neural Network to Detect and Classify Tomato Plant Leaf Diseases","volume":"7","author":"Salih","year":"2020","journal-title":"OALib"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"105701","DOI":"10.1016\/j.compag.2020.105701","article-title":"A detection and severity estimation system for generic diseases of tomato greenhouse plants","volume":"178","author":"Wspanialy","year":"2020","journal-title":"Comput. Electron. Agric."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/575\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:02:08Z","timestamp":1760364128000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/2\/575"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,12]]},"references-count":45,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22020575"],"URL":"https:\/\/doi.org\/10.3390\/s22020575","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,12]]}}}