{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,6]],"date-time":"2026-06-06T03:06:17Z","timestamp":1780715177550,"version":"3.54.1"},"reference-count":40,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T00:00:00Z","timestamp":1637020800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Higher Education Project of Guizhou Province","award":["No. [2020]005, No. [2020]009"],"award-info":[{"award-number":["No. [2020]005, No. [2020]009"]}]},{"name":"the Science and Technology Project of Guizhou Province","award":["No. [2019]3003"],"award-info":[{"award-number":["No. [2019]3003"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>In response to the difficulty of plant leaf disease detection and classification, this study proposes a novel plant leaf disease detection method called deep block attention SSD (DBA_SSD) for disease identification and disease degree classification of plant leaves. We propose three plant leaf detection methods, namely, squeeze-and-excitation SSD (Se_SSD), deep block SSD (DB_SSD), and DBA_SSD. Se_SSD fuses SSD feature extraction network and attention mechanism channel, DB_SSD improves VGG feature extraction network, and DBA_SSD fuses the improved VGG network and channel attention mechanism. To reduce the training time and accelerate the training process, the convolutional layers trained in the Image Net image dataset by the VGG model are migrated to this model, whereas the collected plant leaves disease image dataset is randomly divided into training set, validation set, and test set in the ratio of 8:1:1. We chose the PlantVillage dataset after careful consideration because it contains images related to the domain of interest. This dataset consists of images of 14 plants, including images of apples, tomatoes, strawberries, peppers, and potatoes, as well as the leaves of other plants. In addition, data enhancement methods, such as histogram equalization and horizontal flip were used to expand the image data. The performance of the three improved algorithms is compared and analyzed in the same environment and with the classical target detection algorithms YOLOv4, YOLOv3, Faster RCNN, and YOLOv4 tiny. Experiments show that DBA_SSD outperforms the two other improved algorithms, and its performance in comparative analysis is superior to other target detection algorithms.<\/jats:p>","DOI":"10.3390\/info12110474","type":"journal-article","created":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T11:32:03Z","timestamp":1637062323000},"page":"474","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["DBA_SSD: A Novel End-to-End Object Detection Algorithm Applied to Plant Disease Detection"],"prefix":"10.3390","volume":"12","author":[{"given":"Jun","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University, Guiyang 520025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liya","family":"Yu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University, Guiyang 520025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1915-9487","authenticated-orcid":false,"given":"Jing","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5101-7430","authenticated-orcid":false,"given":"Hao","family":"Dong","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University, Guiyang 520025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.biosystemseng.2018.09.014","article-title":"Rice yield estimation based on K-means clustering with graph-cut segmentation using low-altitude UAV images","volume":"177","author":"Reza","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.biosystemseng.2019.05.002","article-title":"Maize seedling detection under different growth stages and complex field environments based on an improved Faster R\u2013CNN","volume":"184","author":"Quan","year":"2019","journal-title":"Biosyst. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/j.biosystemseng.2018.10.012","article-title":"Automatic in-trap pest detection using deep learning for pheromone-based Dendroctonus valens monitoring","volume":"176","author":"Sun","year":"2018","journal-title":"Biosyst. Eng."},{"key":"ref_4","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_5","doi-asserted-by":"crossref","unstructured":"Taheri-Garavand, A., Nasiri, A., Fanourakis, D., Fatahi, S., Omid, M., and Nikoloudakis, N. (2021). Automated In Situ Seed Variety Identification via Deep Learning: A Case Study in Chickpea. Plants, 10.","DOI":"10.3390\/plants10071406"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Nasiri, A., Taheri-Garavand, A., Fanourakis, D., Zhang, Y.-D., and Nikoloudakis, N. (2021). Automated Grapevine Cultivar Identification via Leaf Imaging and Deep Convolutional Neural Networks: A Proof-of-Concept Study Employing Primary Iranian Varieties. Plants, 10.","DOI":"10.3390\/plants10081628"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1007\/s11119-020-09754-y","article-title":"Fast and accurate detection of kiwifruit in orchard using improved YOLOv3-tiny model","volume":"22","author":"Fu","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Zhao, H., Zhou, Y., Zhang, L., Peng, Y., Hu, X., Peng, H., and Cai, X. (2020). Mixed YOLOv3-LITE: A Lightweight Real-Time Object Detection Method. Sensors, 20.","DOI":"10.3390\/s20071861"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Liu, G., Nouaze, J.C., Mbouembe, P.L.T., and Kim, J.H. (2020). YOLO-Tomato: A Robust Algorithm for Tomato Detection Based on YOLOv3. Sensors, 20.","DOI":"10.3390\/s20072145"},{"key":"ref_10","unstructured":"Redmon, J., and Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13007-020-00624-2","article-title":"Early recognition of tomato gray leaf spot disease based on MobileNetv2-YOLOv3 model","volume":"16","author":"Liu","year":"2020","journal-title":"Plant Methods"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Yi, J., Krusenbaum, L., Unger, P., H\u00fcging, H., Seidel, S.J., Schaaf, G., and Gall, J. (2020). Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images. Sensors, 20.","DOI":"10.3390\/s20205893"},{"key":"ref_14","first-page":"84","article-title":"ImageNet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_15","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2016). Densely Connected Convolutional Networks. arXiv.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_17","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_18","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., and Berg, A.C. (2015). SSD: Single Shot MultiBox Detector. arXiv.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1007\/s11738-021-03244-y","article-title":"Employment of artificial neural networks for non-invasive estimation of leaf water status using color features: A case study in Spathiphyllum wallisii","volume":"43","author":"Nejad","year":"2021","journal-title":"Acta Physiol. Plant."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chao, X., Sun, G., Zhao, H., Li, M., and He, D. (2020). Identification of Apple Tree Leaf Diseases Based on Deep Learning Models. Symmetry, 12.","DOI":"10.3390\/sym12071065"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Chollet, F. (2016). Xception: Deep Learning with Depthwise Separable Convolutions. arXiv.","DOI":"10.1109\/CVPR.2017.195"},{"key":"ref_22","unstructured":"Howard, A.G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. arXiv."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/s42161-020-00683-3","article-title":"Rice plant disease classification using color features: A machine learning paradigm","volume":"103","author":"Shrivastava","year":"2020","journal-title":"J. Plant Pathol."},{"key":"ref_24","unstructured":"Rao, A., and Kulkarni, S. (2020). A Hybrid Approach for Plant Leaf Disease Detection and Classification Using Digital Image Processing Methods. Int. J. Electr. Eng. Educ., 0020720920953126."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.biosystemseng.2020.07.001","article-title":"Detecting powdery mildew disease in squash at different stages using UAV-based hyperspectral imaging and artificial intelligence","volume":"197","author":"Abdulridha","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105660","DOI":"10.1016\/j.compag.2020.105660","article-title":"Automatic vegetable disease identification approach using individual lesion features","volume":"176","author":"Abdu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1186\/s13007-020-00647-9","article-title":"Deep learning-based detection of seedling development","volume":"16","author":"Samiei","year":"2020","journal-title":"Plant Methods"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Ple\u0219oianu, A.-I., Stupariu, M.-S., \u0218andric, I., P\u0103tru-Stupariu, I., and Dr\u0103gu\u021b, L. (2020). Individual Tree-Crown Detection and Species Classification in Very High-Resolution Remote Sensing Imagery Using a Deep Learning Ensemble Model. Remote Sens., 12.","DOI":"10.3390\/rs12152426"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"105446","DOI":"10.1016\/j.compag.2020.105446","article-title":"Vine disease detection in UAV multispectral images using optimized image registration and deep learning segmentation approach","volume":"174","author":"Kerkech","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Zhang, J., Xie, T., Yang, C., Song, H., Jiang, Z., Zhou, G., Zhang, D., Feng, H., and Xie, J. (2020). Segmenting Purple Rapeseed Leaves in the Field from UAV RGB Imagery Using Deep Learning as an Auxiliary Means for Nitrogen Stress Detection. Remote Sens., 12.","DOI":"10.3390\/rs12091403"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.biosystemseng.2020.08.015","article-title":"Active thermal imaging for immature citrus fruit detection","volume":"198","author":"Gan","year":"2020","journal-title":"Biosyst. Eng."},{"key":"ref_33","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_34","doi-asserted-by":"crossref","first-page":"105661","DOI":"10.1016\/j.compag.2020.105661","article-title":"Comparison of convolution neural networks for smartphone image based real time classification of citrus leaf disease","volume":"177","author":"Barman","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1109\/TPAMI.2019.2913372","article-title":"Squeeze-and-Excitation Networks","volume":"42","author":"Hu","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., and Kalinin, A.A. (2020). Albumentations: Fast and Flexible Image Augmentations. Information, 11.","DOI":"10.3390\/info11020125"},{"key":"ref_37","unstructured":"Hughes, D., and Salath\u00e9, M. (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv."},{"key":"ref_38","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_39","unstructured":"Jiang, Z., Zhao, L., Li, S., and Jia, Y. (2020). Real-time object detection method based on improved YOLOv4-tiny. arXiv."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"113985","DOI":"10.1016\/j.indcrop.2021.113985","article-title":"An artificial neural network approach for non-invasive estimation of essential oil content and composition through considering drying processing factors: A case study in Mentha aquatica","volume":"171","author":"Mumivand","year":"2021","journal-title":"Ind. Crop. Prod."}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/11\/474\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:31:12Z","timestamp":1760167872000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/12\/11\/474"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,16]]},"references-count":40,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["info12110474"],"URL":"https:\/\/doi.org\/10.3390\/info12110474","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,16]]}}}