{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T15:05:12Z","timestamp":1777043112990,"version":"3.51.4"},"reference-count":44,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T00:00:00Z","timestamp":1698105600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Taif University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The occurrence of tomato diseases has substantially reduced agricultural output and financial losses. The timely detection of diseases is crucial to effectively manage and mitigate the impact of episodes. Early illness detection can improve output, reduce chemical use, and boost a nation\u2019s economy. A complete system for plant disease detection using EfficientNetV2B2 and deep learning (DL) is presented in this paper. This research aims to develop a precise and effective automated system for identifying several illnesses that impact tomato plants. This will be achieved by analyzing tomato leaf photos. A dataset of high-resolution photographs of healthy and diseased tomato leaves was created to achieve this goal. The EfficientNetV2B2 model is the foundation of the deep learning system and excels at picture categorization. Transfer learning (TF) trains the model on a tomato leaf disease dataset using EfficientNetV2B2\u2019s pre-existing weights and a 256-layer dense layer. Tomato leaf diseases can be identified using the EfficientNetV2B2 model and a dense layer of 256 nodes. An ideal loss function and algorithm train and tune the model. Next, the concept is deployed in smartphones and online apps. The user can accurately diagnose tomato leaf diseases with this application. Utilizing an automated system facilitates the rapid identification of diseases, assisting in making informed decisions on disease management and promoting sustainable tomato cultivation practices. The 5-fold cross-validation method achieved 99.02% average weighted training accuracy, 99.22% average weighted validation accuracy, and 98.96% average weighted test accuracy. The split method achieved 99.93% training accuracy and 100% validation accuracy. Using the DL approach, tomato leaf disease identification achieves nearly 100% accuracy on a test dataset.<\/jats:p>","DOI":"10.3390\/s23218685","type":"journal-article","created":{"date-parts":[[2023,10,24]],"date-time":"2023-10-24T11:39:04Z","timestamp":1698147544000},"page":"8685","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":36,"title":["A Smartphone-Based Detection System for Tomato Leaf Disease Using EfficientNetV2B2 and Its Explainability with Artificial Intelligence (AI)"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-7958-3027","authenticated-orcid":false,"given":"Anjan","family":"Debnath","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, North Western University, Khulna 9100, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6255-9921","authenticated-orcid":false,"given":"Md. Mahedi","family":"Hasan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, North Western University, Khulna 9100, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1072-3555","authenticated-orcid":false,"given":"M.","family":"Raihan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, North Western University, Khulna 9100, Bangladesh"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1727-3391","authenticated-orcid":false,"given":"Nadim","family":"Samrat","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, North Western University, Khulna 9100, Bangladesh"}]},{"given":"Mashael M.","family":"Alsulami","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6019-7245","authenticated-orcid":false,"given":"Mehedi","family":"Masud","sequence":"additional","affiliation":[{"name":"Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1639-1301","authenticated-orcid":false,"given":"Anupam Kumar","family":"Bairagi","sequence":"additional","affiliation":[{"name":"Computer Science and Engineering Discipline, Khulna University, Khulna 9208, Bangladesh"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1007\/s41348-021-00500-8","article-title":"Artificial intelligence in tomato leaf disease detection: A comprehensive review and discussion","volume":"129","author":"Thangaraj","year":"2022","journal-title":"J. Plant Dis. Prot."},{"key":"ref_2","first-page":"2079","article-title":"Crop leaf disease detection and classification using machine learning and deep learning algorithms by visual symptoms: A review","volume":"12","author":"Vasavi","year":"2022","journal-title":"Int. J. Electr. Comput. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"56683","DOI":"10.1109\/ACCESS.2021.3069646","article-title":"Plant disease detection and classification by deep learning\u2014A review","volume":"9","author":"Li","year":"2021","journal-title":"IEEE Access"},{"key":"ref_4","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_5","doi-asserted-by":"crossref","first-page":"110534","DOI":"10.1016\/j.asoc.2023.110534","article-title":"Leaf disease detection using machine learning and deep learning: Review and challenges","volume":"145","author":"Sarkar","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mittal, U., Wadhawan, A., Singla, J., Jhanjhi, N., Ghoniem, R.M., Ray, S.K., and Abdelmaboud, A. (2023). Plant Disease Detection and Classification: A Systematic Literature Review. Sensors, 23.","DOI":"10.3390\/s23104769"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"3287561","DOI":"10.1155\/2022\/3287561","article-title":"A Systematic analysis of machine learning and deep learning based approaches for plant leaf disease classification: A review","volume":"2022","author":"Kumar","year":"2022","journal-title":"J. Sens."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Yamamoto, K., Togami, T., and Yamaguchi, N. (2017). Super-resolution of plant disease images for the acceleration of image-based phenotyping and vigor diagnosis in agriculture. Sensors, 17.","DOI":"10.3390\/s17112557"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"299","DOI":"10.1080\/08839514.2017.1315516","article-title":"Deep learning for tomato diseases: Classification and symptoms visualization","volume":"31","author":"Brahimi","year":"2017","journal-title":"Appl. Artif. Intell."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4757","DOI":"10.1007\/s11831-023-09958-1","article-title":"A Systematic Review of Different Categories of Plant Disease Detection Using Deep Learning-Based Approaches","volume":"30","author":"Kumar","year":"2023","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1007\/s40030-022-00642-4","article-title":"Identification of tomato leaf diseases based on a deep neuro-fuzzy network","volume":"103","author":"Tian","year":"2022","journal-title":"J. Inst. Eng. (India) Ser. A"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Bhujel, A., Kim, N.E., Arulmozhi, E., Basak, J.K., and Kim, H.T. (2022). A lightweight Attention-based convolutional neural networks for tomato leaf disease classification. Agriculture, 12.","DOI":"10.3390\/agriculture12020228"},{"key":"ref_13","first-page":"e00590","article-title":"Early identification of Tuta absoluta in tomato plants using deep learning","volume":"10","author":"Mkonyi","year":"2020","journal-title":"Sci. Afr."},{"key":"ref_14","first-page":"362","article-title":"Machine learning techniques in plant disease detection and classification-a state of the art","volume":"65","author":"John","year":"2021","journal-title":"INMATEH-Agric. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tugrul, B., Elfatimi, E., and Eryigit, R. (2022). Convolutional neural networks in detection of plant leaf diseases: A review. Agriculture, 12.","DOI":"10.3390\/agriculture12081192"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"98","DOI":"10.36596\/jcse.v2i2.171","article-title":"Analysis of Tomato Leaf Disease Identification Techniques","volume":"2","author":"Chopra","year":"2021","journal-title":"J. Comput. Sci. Eng. (JCSE)"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"790","DOI":"10.18196\/jrc.v3i6.15948","article-title":"Disease Detection of Solanaceous Crops Using Deep Learning for Robot Vision","volume":"3","author":"Hidayah","year":"2022","journal-title":"J. Robot. Control (JRC)"},{"key":"ref_18","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_19","doi-asserted-by":"crossref","first-page":"105730","DOI":"10.1016\/j.compag.2020.105730","article-title":"Identification of tomato leaf diseases based on combination of ABCK-BWTR and B-ARNet","volume":"178","author":"Chen","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"108969","DOI":"10.1016\/j.asoc.2022.108969","article-title":"MMDGAN: A fusion data augmentation method for tomato-leaf disease identification","volume":"123","author":"Zhang","year":"2022","journal-title":"Appl. Soft Comput."},{"key":"ref_21","first-page":"290","article-title":"Classification of tomato leaf diseases using MobileNet v2","volume":"9","author":"Zaki","year":"2020","journal-title":"IAES Int. J. Artif. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1158933","DOI":"10.3389\/fpls.2023.1158933","article-title":"An advanced deep learning models-based plant disease detection: A review of recent research","volume":"14","author":"Shoaib","year":"2023","journal-title":"Front. Plant Sci."},{"key":"ref_23","unstructured":"Tan, M., and Le, Q. (2021, January 18\u201324). Efficientnetv2: Smaller models and faster training. Proceedings of the International Conference on Machine Learning, Virtual Event."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Parez, S., Dilshad, N., Alghamdi, N.S., Alanazi, T.M., and Lee, J.W. (2023). Visual Intelligence in Precision Agriculture: Exploring Plant Disease Detection via Efficient Vision Transformers. Sensors, 23.","DOI":"10.3390\/s23156949"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Ghosh, P., Mondal, A.K., Chatterjee, S., Masud, M., Meshref, H., and Bairagi, A.K. (2023). Recognition of Sunflower Diseases Using Hybrid Deep Learning and Its Explainability with AI. Mathematics, 11.","DOI":"10.3390\/math11102241"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Khan, H., Haq, I.U., Munsif, M., Khan, S.U., and Lee, M.Y. (2022). Automated wheat diseases classification framework using advanced machine learning technique. Agriculture, 12.","DOI":"10.3390\/agriculture12081226"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Javeed, D., Gao, T., Saeed, M.S., and Kumar, P. (2023). An Intrusion Detection System for Edge-Envisioned Smart Agriculture in Extreme Environment. IEEE Internet Things J.","DOI":"10.1109\/JIOT.2023.3288544"},{"key":"ref_28","unstructured":"Kaustubh, B. (2023, June 30). Tomato Leaf Disease Detection. Available online: https:\/\/www.kaggle.com\/datasets\/kaustubhb999\/tomatoleaf."},{"key":"ref_29","first-page":"209","article-title":"Recognition of multiple plant leaf diseases based on improved convolutional neural network","volume":"33","author":"Sun","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1016\/j.procs.2020.03.225","article-title":"ToLeD: Tomato leaf disease detection using convolution neural network","volume":"167","author":"Agarwal","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhao, S., Peng, Y., Liu, J., and Wu, S. (2021). Tomato leaf disease diagnosis based on improved convolution neural network by attention module. Agriculture, 11.","DOI":"10.3390\/agriculture11070651"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"542","DOI":"10.3390\/agriengineering3030035","article-title":"Tomato leaf diseases classification based on leaf images: A comparison between classical machine learning and deep learning methods","volume":"3","author":"Tan","year":"2021","journal-title":"AgriEngineering"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1040","DOI":"10.1016\/j.procs.2018.07.070","article-title":"Tomato crop disease classification using pre-trained deep learning algorithm","volume":"133","author":"Rangarajan","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"101663","DOI":"10.1016\/j.ecoinf.2022.101663","article-title":"Detection and classification of chilli leaf disease using a squeeze-and-excitation-based CNN model","volume":"69","author":"Naik","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"981","DOI":"10.1007\/s11045-022-00820-4","article-title":"Deep CNN model for crops\u2019 diseases detection using leaf images","volume":"33","author":"Kurmi","year":"2022","journal-title":"Multidimens. Syst. Signal Process."},{"key":"ref_36","first-page":"23","article-title":"Transfer learning for multi-crop leaf disease image classification using convolutional neural network VGG","volume":"6","author":"Paymode","year":"2022","journal-title":"Artif. Intell. Agric."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"8812019","DOI":"10.1155\/2020\/8812019","article-title":"Optimizing pretrained convolutional neural networks for tomato leaf disease detection","volume":"2020","author":"Ahmad","year":"2020","journal-title":"Complexity"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"28822","DOI":"10.1109\/ACCESS.2021.3058947","article-title":"Tomato leaf disease identification by restructured deep residual dense network","volume":"9","author":"Zhou","year":"2021","journal-title":"IEEE Access"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Trivedi, N.K., Gautam, V., Anand, A., Aljahdali, H.M., Villar, S.G., Anand, D., Goyal, N., and Kadry, S. (2021). Early detection and classification of tomato leaf disease using high-performance deep neural network. Sensors, 21.","DOI":"10.3390\/s21237987"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"98716","DOI":"10.1109\/ACCESS.2020.2997001","article-title":"DCGAN-based data augmentation for tomato leaf disease identification","volume":"8","author":"Wu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"242","DOI":"10.3311\/PPtr.11480","article-title":"Transfer learning based traffic sign recognition using inception-v3 model","volume":"47","author":"Lin","year":"2019","journal-title":"Period. Polytech. Transp. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6999","DOI":"10.1109\/TNNLS.2021.3084827","article-title":"A survey of convolutional neural networks: Analysis, applications, and prospects","volume":"33","author":"Li","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_43","unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking model scaling for convolutional neural networks. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA."},{"key":"ref_44","unstructured":"Garreau, D., and Luxburg, U. (2020, January 26\u201328). Explaining the explainer: A first theoretical analysis of LIME. Proceedings of the International Conference on Artificial Intelligence and Statistics, Online."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/21\/8685\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T21:11:07Z","timestamp":1760130667000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/21\/8685"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,24]]},"references-count":44,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2023,11]]}},"alternative-id":["s23218685"],"URL":"https:\/\/doi.org\/10.3390\/s23218685","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,24]]}}}