{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T20:17:26Z","timestamp":1773778646841,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,13]],"date-time":"2023-09-13T00:00:00Z","timestamp":1694563200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Light of West China","award":["XAB2022YN10"],"award-info":[{"award-number":["XAB2022YN10"]}]},{"name":"Light of West China","award":["2018ZDXM-SF-093"],"award-info":[{"award-number":["2018ZDXM-SF-093"]}]},{"name":"Light of West China","award":["S2022-YF-ZDCXL-ZDLGY-0093"],"award-info":[{"award-number":["S2022-YF-ZDCXL-ZDLGY-0093"]}]},{"name":"Light of West China","award":["2023-ZDLGY-45"],"award-info":[{"award-number":["2023-ZDLGY-45"]}]},{"name":"Shaanxi key research and development plan","award":["XAB2022YN10"],"award-info":[{"award-number":["XAB2022YN10"]}]},{"name":"Shaanxi key research and development plan","award":["2018ZDXM-SF-093"],"award-info":[{"award-number":["2018ZDXM-SF-093"]}]},{"name":"Shaanxi key research and development plan","award":["S2022-YF-ZDCXL-ZDLGY-0093"],"award-info":[{"award-number":["S2022-YF-ZDCXL-ZDLGY-0093"]}]},{"name":"Shaanxi key research and development plan","award":["2023-ZDLGY-45"],"award-info":[{"award-number":["2023-ZDLGY-45"]}]},{"name":"Shaanxi Province key industrial innovation chain","award":["XAB2022YN10"],"award-info":[{"award-number":["XAB2022YN10"]}]},{"name":"Shaanxi Province key industrial innovation chain","award":["2018ZDXM-SF-093"],"award-info":[{"award-number":["2018ZDXM-SF-093"]}]},{"name":"Shaanxi Province key industrial innovation chain","award":["S2022-YF-ZDCXL-ZDLGY-0093"],"award-info":[{"award-number":["S2022-YF-ZDCXL-ZDLGY-0093"]}]},{"name":"Shaanxi Province key industrial innovation chain","award":["2023-ZDLGY-45"],"award-info":[{"award-number":["2023-ZDLGY-45"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Plant growth is inevitably affected by diseases, and one effective method of disease detection is through the observation of leaf changes. To solve the problem of disease detection in complex backgrounds, where the distinction between plant diseases is hindered by large intra-class differences and small inter-class differences, a complete plant-disease recognition process is proposed. The process was tested through experiments and research into traditional and deep features. In the face of difficulties related to plant-disease classification in complex backgrounds, the advantages of strong interpretability of traditional features and great robustness of deep features are fully utilized, and include the following components: (1) The OSTU algorithm based on the naive Bayes model is proposed to focus on where leaves are located and remove interference from complex backgrounds. (2) A multi-dimensional feature model is introduced in an interpretable manner from the perspective of traditional features to obtain leaf characteristics. (3) A MobileNet V2 network with a dual attention mechanism is proposed to establish a model that operates in both spatial and channel dimensions at the network level to facilitate plant-disease recognition. In the Plant Village open database test, the results demonstrated an average SEN of 94%, greater than other algorithms by 12.6%.<\/jats:p>","DOI":"10.3390\/a16090442","type":"journal-article","created":{"date-parts":[[2023,9,14]],"date-time":"2023-09-14T10:00:23Z","timestamp":1694685623000},"page":"442","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["A Plant Disease Classification Algorithm Based on Attention MobileNet V2"],"prefix":"10.3390","volume":"16","author":[{"given":"Huan","family":"Wang","sequence":"first","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1548-6106","authenticated-orcid":false,"given":"Shi","family":"Qiu","sequence":"additional","affiliation":[{"name":"Xi\u2019an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi\u2019an 710119, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9114-205X","authenticated-orcid":false,"given":"Huping","family":"Ye","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Key Laboratory of Low Altitude Geographic Information and Air Route, Civil Aviation Administration of China, Beijing 100101, China"}]},{"given":"Xiaohan","family":"Liao","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Resources and Environment Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China"},{"name":"Key Laboratory of Low Altitude Geographic Information and Air Route, Civil Aviation Administration of China, Beijing 100101, China"},{"name":"The Research Center for UAV Applications and Regulation, Chinese Academy of Sciences, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,13]]},"reference":[{"key":"ref_1","first-page":"31","article-title":"Fast and accurate detection and classification of plant diseases","volume":"17","author":"Reyalat","year":"2011","journal-title":"Int. J. Comput. Appl."},{"key":"ref_2","first-page":"3661","article-title":"Applying image processing technique to detect plant diseases","volume":"2","author":"Kulkarni","year":"2012","journal-title":"Int. J. Mod. Eng. Res."},{"key":"ref_3","first-page":"211","article-title":"Detection of unhealthy region of plant leaves and classification of plant leaf diseases using texture features","volume":"15","author":"Arivazhagan","year":"2013","journal-title":"Agric. Eng. Int. CIGR J."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hossain, E., Hossain, M.F., and Rahaman, M.A. (2019, January 7\u20139). A color and texture based approach for the detection and classification of plant leaf disease using KNN classifier. Proceedings of the 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), Cox\u2019s Bazar, Bangladesh.","DOI":"10.1109\/ECACE.2019.8679247"},{"key":"ref_5","first-page":"41","article-title":"Detection of plant leaf diseases using image segmentation and soft computing techniques","volume":"4","author":"Singh","year":"2017","journal-title":"Inf. Process. Agric."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Kaur, R., and Singla, S. (2016, January 12\u201313). Classification of plant leaf diseases using gradient and texture feature. Proceedings of the International Conference on Advances in Information Communication Technology & Computing, Thai Nguyen, Vietnam.","DOI":"10.1145\/2979779.2979875"},{"key":"ref_7","first-page":"1","article-title":"Recognition of plant leaf diseases based on computer vision","volume":"11","author":"Nanehkaran","year":"2020","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"6","DOI":"10.9781\/ijimai.2016.371","article-title":"SVM and ANN based classification of plant diseases using feature reduction technique","volume":"3","author":"Pujari","year":"2016","journal-title":"IJIMAI"},{"key":"ref_9","unstructured":"Brahimi, M., Arsenovic, M., Laraba, S., Sladojevic, S., Boukhalfa, K., and Moussaoui, A. (2018). Human and Machine Learning, Springer."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"26245","DOI":"10.1007\/s11042-020-09239-0","article-title":"Pseudoinverse learning autoencoder with DCGAN for plant diseases classification","volume":"79","author":"Guo","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_11","unstructured":"Sandesh Kumar, C., Sharma, V.K., Yadav, A.K., and Singh, A. (2021). Innovations in Computational Intelligence and Computer Vision, Springer."},{"key":"ref_12","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_13","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_14","doi-asserted-by":"crossref","unstructured":"Sardogan, M., Tuncer, A., and Ozen, Y. (2018, January 20\u201323). Plant leaf disease detection and classification based on CNN with LVQ algorithm. Proceedings of the 2018 3rd International Conference on Computer Science and Engineering (UBMK), Sarajevo, Bosnia and Herzegovina.","DOI":"10.1109\/UBMK.2018.8566635"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5979","DOI":"10.1007\/s12652-020-02149-x","article-title":"Kuan noise filter with Hough transformation based reweighted linear program boost classification for plant leaf disease detection","volume":"12","author":"Deepa","year":"2021","journal-title":"J. Ambient. Intell. Humaniz. Comput."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"57","DOI":"10.18100\/ijamec.797392","article-title":"Performance evaluation of capsule networks for classification of plant leaf diseases","volume":"8","author":"Altan","year":"2020","journal-title":"Int. J. Appl. Math. Electron. Comput."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"105754","DOI":"10.1016\/j.engappai.2022.105754","article-title":"AgriDet: Plant leaf disease severity classification using agriculture detection framework","volume":"119","author":"Pal","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1016\/j.compag.2019.01.034","article-title":"PD2SE-Net: Computer-assisted plant disease diagnosis and severity estimation network","volume":"157","author":"Liang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"143824","DOI":"10.1109\/ACCESS.2021.3120379","article-title":"Corn leaf diseases diagnosis based on K-means clustering and deep learning","volume":"9","author":"Yu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Padol, P.B., and Yadav, A.A. (2016, January 9\u201311). SVM classifier based grape leaf disease detection. Proceedings of the 2016 Conference on Advances in Signal Processing (CASP), Pune, India.","DOI":"10.1109\/CASP.2016.7746160"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rani, F.P., Kumar, S.N., Fred, A.L., Dyson, C., Suresh, V., and Jeba, P.S. (2019, January 7\u201320). K-means clustering and SVM for plant leaf disease detection and classification. Proceedings of the 2019 International Conference on Recent Advances in Energy-Efficient Computing and Communication (ICRAECC), Nagercoil, India.","DOI":"10.1109\/ICRAECC43874.2019.8995157"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"20201","DOI":"10.1007\/s11042-022-12518-7","article-title":"Automatic segmentation of plant leaves disease using min-max hue histogram and k-mean clustering","volume":"81","author":"Trivedi","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_23","first-page":"189","article-title":"Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters","volume":"4","author":"Faithpraise","year":"2013","journal-title":"Int. J. Adv. Biotechnol. Res."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Tamilselvi, P., and Kumar, K.A. (2017, January 23\u201324). Unsupervised machine learning for clustering the infected leaves based on the leaf-colours. Proceedings of the 2017 Third International Conference on Science Technology Engineering & Management (ICONSTEM), Chennai, India.","DOI":"10.1109\/ICONSTEM.2017.8261265"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Hasan, R.I., Yusuf, S.M., Mohd Rahim, M.S., and Alzubaidi, L. (2023). Automatic clustering and classification of coffee leaf diseases based on an extended kernel density estimation approach. Plants, 12.","DOI":"10.3390\/plants12081603"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Yadhav, S.Y., Senthilkumar, T., Jayanthy, S., and Kovilpillai, J.J.A. (2020, January 2\u20134). Plant disease detection and classification using cnn model with optimized activation function. Proceedings of the 2020 International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India.","DOI":"10.1109\/ICESC48915.2020.9155815"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"21701","DOI":"10.1007\/s11042-023-14631-7","article-title":"Prediction and classification of rice leaves using the improved PSO clustering and improved CNN","volume":"82","author":"Bhimavarapu","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_28","first-page":"13","article-title":"Plant leaf disease recognition using random Forest, KNN, SVM and CNN","volume":"62","author":"Hatuwal","year":"2020","journal-title":"Polibits"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Pareek, P.K., Ramya, I.M., Jagadeesh, B.N., and LeenaShruthi, H.M. (2023, January 24\u201325). Clustering based segmentation with 1D-CNN model for grape fruit disease detection. Proceedings of the 2023 IEEE International Conference on Integrated Circuits and Communication Systems (ICICACS), Raichur, India.","DOI":"10.1109\/ICICACS57338.2023.10099916"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mukti, I.Z., and Biswas, D. (2019, January 20\u201322). Transfer learning based plant diseases detection using ResNet50. Proceedings of the 2019 4th International Conference on Electrical Information and Communication Technology (EICT), Khulna, Bangladesh.","DOI":"10.1109\/EICT48899.2019.9068805"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Li, M., Cheng, S., Cui, J., Li, C., Li, Z., Zhou, C., and Lv, C. (2023). High-performance plant pest and disease detection based on model ensemble with inception module and cluster algorithm. Plants, 12.","DOI":"10.3390\/plants12010200"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1636","DOI":"10.3906\/elk-1809-181","article-title":"Plant disease and pest detection using deep learning-based features","volume":"27","author":"Muammer","year":"2019","journal-title":"Turk. J. Electr. Eng. Comput. Sci."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ramesh, S., Hebbar, R., Niveditha, M., Pooja, R., Shashank, N., and Vinod, P.V. (2018, January 25\u201328). Plant disease detection using machine learning. Proceedings of the 2018 International Conference on Design Innovations for 3Cs Compute Communicate Control (ICDI3C), Bangalore, India.","DOI":"10.1109\/ICDI3C.2018.00017"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"3924120","DOI":"10.1155\/2018\/3924120","article-title":"Detection of surface crack in building structures using image processing technique with an improved Otsu method for image thresholding","volume":"2018","author":"Hoang","year":"2018","journal-title":"Adv. Civ. Eng."},{"key":"ref_35","first-page":"441","article-title":"Heart diseases detection using Naive Bayes algorithm","volume":"2","author":"Vembandasamy","year":"2015","journal-title":"Int. J. Innov. Sci. Eng. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"108495","DOI":"10.1016\/j.patcog.2021.108495","article-title":"Adaptive Gabor convolutional networks","volume":"124","author":"Yuan","year":"2022","journal-title":"Pattern Recognit."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Srinivasu, P.N., SivaSai, J.G., Ijaz, M.F., Bhoi, A.K., Kim, W., and Kang, J.J. (2021). Classification of skin disease using deep learning neural networks with MobileNet V2 and LSTM. Sensors, 21.","DOI":"10.3390\/s21082852"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Dang, L., Pang, P., and Lee, J. (2020). Depth-wise separable convolution neural network with residual connection for hyperspectral image classification. Remote Sens., 12.","DOI":"10.3390\/rs12203408"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Fu, H., Song, G., and Wang, Y. (2021). Improved YOLOv4 marine target detection combined with CBAM. Symmetry, 13.","DOI":"10.3390\/sym13040623"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.inffus.2021.11.012","article-title":"Dwarfism computer-aided diagnosis algorithm based on multimodal pyradiomics","volume":"80","author":"Qiu","year":"2022","journal-title":"Inf. Fusion"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"4373","DOI":"10.1007\/s00521-018-3824-3","article-title":"Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization","volume":"32","author":"Selvanambi","year":"2020","journal-title":"Neural Comput. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"101998","DOI":"10.1016\/j.ecoinf.2023.101998","article-title":"A novel multi-head CNN design to identify plant diseases using the fusion of RGB images","volume":"75","author":"Kaya","year":"2023","journal-title":"Ecol. Inform."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1007\/s11334-022-00507-w","article-title":"Optimized classification model for plant diseases using generative adversarial networks","volume":"19","author":"Lamba","year":"2023","journal-title":"Innov. Syst. Softw. Eng."}],"container-title":["Algorithms"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/9\/442\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:50:15Z","timestamp":1760129415000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-4893\/16\/9\/442"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,13]]},"references-count":43,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["a16090442"],"URL":"https:\/\/doi.org\/10.3390\/a16090442","relation":{},"ISSN":["1999-4893"],"issn-type":[{"value":"1999-4893","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,13]]}}}