{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,11]],"date-time":"2025-03-11T04:33:10Z","timestamp":1741667590091,"version":"3.38.0"},"reference-count":27,"publisher":"SAGE Publications","issue":"1-2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["HIS"],"published-print":{"date-parts":[[2021,7,13]]},"abstract":"<jats:p>The main problem in agriculture is the attack of diseases on the leaves of plants and the spread of agricultural pests. For this reason, we will present how to treat certain phenomena of disease in plants, or how to prevent and do the precautionary measures to adopt a modern method to diagnose the deficiency of the leaves elements of the diseased plants. Thus, the deep learning is the most appropriate solution to detect the properties of the leaves and is essential in the tracking of large fields of crops as well as automatically detecting the symptoms of the leaves characteristics as soon as they appear on the plants leaves. In this paper, we clarified the Transfer Learning (TL) architecture for VGG-16 and the other architecture like ResNet to detect plants that suffer from diseases in the sheet due to a lack of ingredient using a set of increased data based on the leaves of healthy and unhealthy plants alike. The experimental results show that significant detection accuracy improvement has been achieved thanks to our proposed model compared to other reported methods.<\/jats:p>","DOI":"10.3233\/his-210002","type":"journal-article","created":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T09:19:53Z","timestamp":1624699193000},"page":"33-42","source":"Crossref","is-referenced-by-count":4,"title":["Diagnostic method based DL approach to detect the lack of elements from the leaves of diseased plants"],"prefix":"10.1177","volume":"17","author":[{"given":"Mohamed","family":"Elleuch","sequence":"first","affiliation":[{"name":"National School of Computer Science (ENSI), University of Manouba, Manouba, Tunisia"}]},{"given":"Fatma","family":"Marzougui","sequence":"additional","affiliation":[{"name":"Faculty of Sciences, University of Gafsa, Gafsa, Tunisia"}]},{"given":"Monji","family":"Kherallah","sequence":"additional","affiliation":[{"name":"Faculty of Sciences, University of Sfax, Sfax, Tunisia"}]}],"member":"179","reference":[{"key":"10.3233\/HIS-210002_ref1","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"10.3233\/HIS-210002_ref2","first-page":"1","article-title":"Going deeper with convolutions","author":"Szegedy","year":"2015","journal-title":"IEEE Conference on Computer Vision and Pattern Recognition"},{"key":"10.3233\/HIS-210002_ref3","unstructured":"M. G\u00fczel, The importance of good agricultural practices (gap) in the context of quality practices in agriculture and a sample application, Ph.D. Dissertation, Dokuz Eyl\u00fcl University, \u0130zmir, Turkey, 2012."},{"issue":"8","key":"10.3233\/HIS-210002_ref4","doi-asserted-by":"crossref","first-page":"2674","DOI":"10.3390\/s18082674","article-title":"Machine learning in agriculture: A review","volume":"18","author":"Liakos","year":"2018","journal-title":"Sensors"},{"key":"10.3233\/HIS-210002_ref5","doi-asserted-by":"crossref","unstructured":"A. Kamilaris and F.X. Prenafeta-Bold\u00fa, Deep learning in agriculture: A survey, Computers and Electronics in Agriculture 147 (2018), 70\u201390.","DOI":"10.1016\/j.compag.2018.02.016"},{"key":"10.3233\/HIS-210002_ref6","first-page":"1","article-title":"A deep CNN approach for plant disease detection","author":"Marzougui","year":"2020","journal-title":"21st International Arab Conference on Information Technology (ACIT)"},{"key":"10.3233\/HIS-210002_ref7","first-page":"1557","article-title":"Prediction models for identification and diagnosis of tomato plant diseases","author":"Verma","year":"2018","journal-title":"International Conference on Advances in Computing, Communications and Informatics (ICACCI)"},{"key":"10.3233\/HIS-210002_ref8","first-page":"1","article-title":"Deep learning based on nasnet for plant disease recognition using leave images","author":"Adedoja","year":"2019","journal-title":"International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD)"},{"key":"10.3233\/HIS-210002_ref9","first-page":"1","article-title":"Crop disease detection using deep learning","author":"Kulkarni","year":"2018","journal-title":"Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)"},{"key":"10.3233\/HIS-210002_ref10","doi-asserted-by":"crossref","first-page":"229","DOI":"10.1016\/j.aiia.2020.10.002","article-title":"A review of imaging techniques for plant disease detection","volume":"4","author":"Singh","year":"2020","journal-title":"Artificial Intelligence in Agriculture"},{"key":"10.3233\/HIS-210002_ref11","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.aiia.2020.03.001","article-title":"Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images","volume":"4","author":"Anami","year":"2020","journal-title":"Artificial Intelligence in Agriculture"},{"key":"10.3233\/HIS-210002_ref12","doi-asserted-by":"crossref","first-page":"103615","DOI":"10.1016\/j.micpro.2020.103615","article-title":"Performance of deep learning vs machine learning in plant leaf disease detection","volume":"80","author":"Sujatha","year":"2021","journal-title":"Microprocessors and Microsystems"},{"key":"10.3233\/HIS-210002_ref13","first-page":"1","article-title":"Automatic recognition of guava leaf diseases using deep con-volution neural network","author":"Howlader","year":"2019","journal-title":"International Conference on Electrical, Computer and Communication Engineering (ECCE)"},{"key":"10.3233\/HIS-210002_ref14","first-page":"1","article-title":"A computer vision system for guava disease detection and recommend curative solution using deep learning approach","author":"Al\u00a0Haque","year":"2019","journal-title":"22nd International Conference on Computer and Information Technology (ICCIT)"},{"key":"10.3233\/HIS-210002_ref15","doi-asserted-by":"crossref","unstructured":"A. Sagar and D. Jacob, On using transfer learning for plant disease detection, bioRxiv, 2020.","DOI":"10.1101\/2020.05.22.110957"},{"key":"10.3233\/HIS-210002_ref16","first-page":"396","article-title":"Handwritten digit recognition with a backpropagation network","author":"LeCun","year":"1990","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"10","key":"10.3233\/HIS-210002_ref17","doi-asserted-by":"crossref","first-page":"1302","DOI":"10.3390\/plants9101302","article-title":"Review of the state of the art of deep learning for plant diseases: A broad analysis and discussion","volume":"9","author":"Hasan","year":"2020","journal-title":"Plants"},{"issue":"7","key":"10.3233\/HIS-210002_ref18","doi-asserted-by":"crossref","first-page":"939","DOI":"10.3390\/sym11070939","article-title":"Solving current limitations of deep learning based approaches for plant disease detection","volume":"11","author":"Arsenovic","year":"2019","journal-title":"Symmetry"},{"issue":"5","key":"10.3233\/HIS-210002_ref19","doi-asserted-by":"crossref","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","article-title":"Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning","volume":"35","author":"Shin","year":"2016","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"10.3233\/HIS-210002_ref20","unstructured":"M. Raghu, C. Zhang, J. Kleinberg and S. Bengio, Transfusion: Understanding transfer learning for medical imaging, arXiv preprint arXiv: 1902.07208, 2019."},{"key":"10.3233\/HIS-210002_ref21","first-page":"806","article-title":"CNN features off-the-shelf: An astounding baseline for recognition","author":"Razavian","year":"2014","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops"},{"key":"10.3233\/HIS-210002_ref22","doi-asserted-by":"crossref","first-page":"105220","DOI":"10.1016\/j.compag.2020.105220","article-title":"New perspectives on plant disease characterization based on deep learning","volume":"170","author":"Lee","year":"2020","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.3233\/HIS-210002_ref23","unstructured":"Y. Katariya, Image captioning using inception V3 and beam search, 2017."},{"key":"10.3233\/HIS-210002_ref24","first-page":"740","article-title":"Microsoft coco: Common objects in context","author":"Lin","year":"2014","journal-title":"European Conference on Computer Vision"},{"key":"10.3233\/HIS-210002_ref25","first-page":"464","article-title":"Evaluation of data augmentation for detection plant disease","author":"Marzougui","year":"2020","journal-title":"20th International Conference on Hybrid Intelligent Systems (HIS)"},{"key":"10.3233\/HIS-210002_ref26","doi-asserted-by":"crossref","first-page":"479","DOI":"10.5220\/0006196204790486","article-title":"Comparing local descriptors and bags of visual words to deep convolutional neural networks for plant recognition","author":"Pawara","year":"2017","journal-title":"International Conference on Pattern Recognition Applications and Methods"},{"key":"10.3233\/HIS-210002_ref27","first-page":"770","article-title":"Deep residual learning for image recognition","author":"He","year":"2016","journal-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition"}],"container-title":["International Journal of Hybrid Intelligent Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/HIS-210002","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,3,10]],"date-time":"2025-03-10T20:56:56Z","timestamp":1741640216000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/HIS-210002"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,13]]},"references-count":27,"journal-issue":{"issue":"1-2"},"URL":"https:\/\/doi.org\/10.3233\/his-210002","relation":{},"ISSN":["1448-5869","1875-8819"],"issn-type":[{"type":"print","value":"1448-5869"},{"type":"electronic","value":"1875-8819"}],"subject":[],"published":{"date-parts":[[2021,7,13]]}}}