{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T05:51:03Z","timestamp":1760248263470,"version":"build-2065373602"},"reference-count":43,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T00:00:00Z","timestamp":1639526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100016692","name":"Key Research and Development Program of Ningxia","doi-asserted-by":"publisher","award":["2019BBF02013"],"award-info":[{"award-number":["2019BBF02013"]}],"id":[{"id":"10.13039\/100016692","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Disease and pest detection of grape foliage is essential for grape yield and quality. RGB image (RGBI), multispectral image (MSI), and thermal infrared image (TIRI) are widely used in the health detection of plants. In this study, we collected three types of grape foliage images with six common classes (anthracnose, downy mildew, leafhopper, mites, viral disease, and healthy) in the field. ShuffleNet V2 was used to build up detection models. According to the accuracy of RGBI, MSI, TIRI, and multi-source data concatenation (MDC) models, and a multi-source data fusion (MDF) decision-making method was proposed for improving the detection performance for grape foliage, aiming to enhance the decision-making for RGBI of grape foliage by fusing the MSI and TIRI. The results showed that 40% of the incorrect detection outputs were rectified using the MDF decision-making method. The overall accuracy of MDF model was 96.05%, which had improvements of 2.64%, 13.65%, and 27.79%, compared with the RGBI, MSI, and TIRI models using label smoothing, respectively. In addition, the MDF model was based on the lightweight network with 3.785 M total parameters and 0.362 G multiply-accumulate operations, which could be highly portable and easy to be applied.<\/jats:p>","DOI":"10.3390\/rs13245102","type":"journal-article","created":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T21:47:36Z","timestamp":1639604856000},"page":"5102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Multi-Source Data Fusion Decision-Making Method for Disease and Pest Detection of Grape Foliage Based on ShuffleNet V2"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2602-2542","authenticated-orcid":false,"given":"Rui","family":"Yang","sequence":"first","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4823-7096","authenticated-orcid":false,"given":"Xiangyu","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Jing","family":"Huang","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Jun","family":"Zhou","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Jie","family":"Jiao","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7027-8911","authenticated-orcid":false,"given":"Yufei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0266-6896","authenticated-orcid":false,"given":"Fei","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China"}]},{"given":"Baofeng","family":"Su","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China"}]},{"given":"Peiwen","family":"Gu","sequence":"additional","affiliation":[{"name":"School of Agriculture, Ningxia University, Yinchuan 750021, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,15]]},"reference":[{"doi-asserted-by":"crossref","unstructured":"Jaisakthi, S.M., Mirunalini, P., Thenmozhi, D. (2019, January 21\u201323). Grape Leaf Disease Identification Using Machine Learning Techniques. Proceedings of the 2019 International Conference on Computational Intelligence in Data Science (ICCIDS), Chennai, India.","key":"ref_1","DOI":"10.1109\/ICCIDS.2019.8862084"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.cropro.2016.11.025","article-title":"A Critical Review of Plant Protection Tools for Reducing Pesticide Use on Grapevine and New Perspectives for the Implementation of IPM in Viticulture","volume":"97","author":"Pertot","year":"2017","journal-title":"Crop Prot."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.plantsci.2018.04.027","article-title":"Plant Defense against Aphids, the Pest Extraordinaire","volume":"279","author":"Nalam","year":"2019","journal-title":"Plant Sci."},{"unstructured":"Lu, H. (2020). Computer Vision Method Applied for Detecting Diseases in Grape Leaf System. Cognitive Internet of Things: Frameworks, Tools and Applications, Springer International Publishing. Studies in Computational Intelligence.","key":"ref_4"},{"doi-asserted-by":"crossref","unstructured":"Meunkaewjinda, A., Kumsawat, P., Attakitmongcol, K., and Srikaew, A. (2008, January 14\u201317). Grape Leaf Disease Detection from Color Imagery Using Hybrid Intelligent System. Proceedings of the 2008 5th International Conference on Electrical Engineering\/Electronics, Computer, Telecommunications and Information Technology, Krabi, Thailand.","key":"ref_5","DOI":"10.1109\/ECTICON.2008.4600483"},{"doi-asserted-by":"crossref","unstructured":"Sannakki, S.S., Rajpurohit, V.S., Nargund, V.B., and Kulkarni, P. (2013, January 4\u20136). Diagnosis and Classification of Grape Leaf Diseases Using Neural Networks. Proceedings of the 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT), Tiruchengode, India.","key":"ref_6","DOI":"10.1109\/ICCCNT.2013.6726616"},{"doi-asserted-by":"crossref","unstructured":"Khirade, S.D., and Patil, A.B. (2015, January 26\u201327). Plant Disease Detection Using Image Processing. Proceedings of the 2015 International Conference on Computing Communication Control and Automation, Pune, India.","key":"ref_7","DOI":"10.1109\/ICCUBEA.2015.153"},{"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.","key":"ref_8","DOI":"10.1109\/CASP.2016.7746160"},{"doi-asserted-by":"crossref","unstructured":"Padol, P.B., and Sawant, S.D. (2016, January 22\u201324). Fusion Classification Technique Used to Detect Downy and Powdery Mildew Grape Leaf Diseases. Proceedings of the 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication (ICGTSPICC), Jalgaon, India.","key":"ref_9","DOI":"10.1109\/ICGTSPICC.2016.7955315"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep Learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1082","DOI":"10.3389\/fpls.2020.01082","article-title":"Grape Leaf Disease Identification Using Improved Deep Convolutional Neural Networks","volume":"11","author":"Liu","year":"2020","journal-title":"Front. Plant Sci."},{"key":"ref_12","first-page":"418","article-title":"Automatic Grape Leaf Diseases Identification Via UnitedModel Based on Multiple Convolutional Neural Networks","volume":"7","author":"Ji","year":"2020","journal-title":"Inf. Process. Agric."},{"doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201323). Shufflenet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","key":"ref_13","DOI":"10.1109\/CVPR.2018.00716"},{"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_14"},{"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_15"},{"doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201323). Mobilenetv2: Inverted Residuals and Linear Bottlenecks. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","key":"ref_16","DOI":"10.1109\/CVPR.2018.00474"},{"unstructured":"Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., and Vasudevan, V. (November, January 27). Searching for Mobilenetv3. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea.","key":"ref_17"},{"unstructured":"Tan, M., and Le, Q. (2019, January 9\u201315). Efficientnet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA.","key":"ref_18"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"105735","DOI":"10.1016\/j.compag.2020.105735","article-title":"Grape Disease Image Classification Based on Lightweight Convolution Neural Networks and Channelwise Attention","volume":"178","author":"Tang","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"813","DOI":"10.1038\/11765","article-title":"Presymptomatic Visualization of Plant-Virus Interactions by Thermography","volume":"17","author":"Chaerle","year":"1999","journal-title":"Nat. Biotechnol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.trac.2019.05.022","article-title":"Advanced Spectroscopic Techniques for Plant Disease Diagnostics. A Review","volume":"118","author":"Farber","year":"2019","journal-title":"TrAC Trends Anal. Chem."},{"key":"ref_22","first-page":"115","article-title":"Detection of Rice Sheath Blight for In-Season Disease Management Using Multispectral Remote Sensing","volume":"7","author":"Qin","year":"2005","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s11119-010-9180-7","article-title":"Spectral Signatures of Sugar Beet Leaves for the Detection and Differentiation of Diseases","volume":"11","author":"Mahlein","year":"2010","journal-title":"Precis. Agric."},{"doi-asserted-by":"crossref","unstructured":"Kerkech, M., Hafiane, A., and Canals, R. (2020). VddNet: Vine Disease Detection Network Based on Multispectral Images and Depth Map. Remote Sens., 12.","key":"ref_24","DOI":"10.3390\/rs12203305"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"628","DOI":"10.3389\/fpls.2019.00628","article-title":"Detection of Gray Mold Leaf Infections Prior to Visual Symptom Appearance Using a Five-Band Multispectral Sensor","volume":"10","author":"Fahrentrapp","year":"2019","journal-title":"Front. Plant Sci."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1186\/s13007-019-0389-9","article-title":"Multispectral Imaging for Presymptomatic Analysis of Light Leaf Spot in Oilseed Rape","volume":"15","author":"Veys","year":"2019","journal-title":"Plant Methods"},{"key":"ref_27","first-page":"325","article-title":"A Review on Machine Learning Principles for Multi-View Biological Data Integration","volume":"19","author":"Li","year":"2018","journal-title":"Brief. Bioinform."},{"doi-asserted-by":"crossref","unstructured":"Ouhami, M., Hafiane, A., Es-Saady, Y., El Hajji, M., and Canals, R. (2021). Computer Vision, IoT and Data Fusion for Crop Disease Detection Using Machine Learning: A Survey and Ongoing Research. Remote Sens., 13.","key":"ref_28","DOI":"10.3390\/rs13132486"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.biosystemseng.2009.02.009","article-title":"Image Fusion of Visible and Thermal Images for Fruit Detection","volume":"103","author":"Bulanon","year":"2009","journal-title":"Biosyst. Eng."},{"doi-asserted-by":"crossref","unstructured":"Mahlein, A.-K., Alisaac, E., Al Masri, A., Behmann, J., Dehne, H.-W., and Oerke, E.-C. (2019). Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring Fusarium Head Blight of Wheat on Spikelet Scale. Sensors, 19.","key":"ref_30","DOI":"10.3390\/s19102281"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"577063","DOI":"10.3389\/fpls.2020.577063","article-title":"Investigation on Data Fusion of Multisource Spectral Data for Rice Leaf Diseases Identification Using Machine Learning Methods","volume":"11","author":"Feng","year":"2020","journal-title":"Front. Plant Sci."},{"doi-asserted-by":"crossref","unstructured":"Prince, G., Clarkson, J.P., and Rajpoot, N.M. (2015). Automatic Detection of Diseased Tomato Plants Using Thermal and Stereo Visible Light Images. PLoS ONE, 10.","key":"ref_32","DOI":"10.1371\/journal.pone.0123262"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.isprsjprs.2017.10.011","article-title":"Unmanned Aerial System (UAS)-Based Phenotyping of Soybean Using Multi-Sensor Data Fusion and Extreme Learning Machine","volume":"134","author":"Maimaitijiang","year":"2017","journal-title":"ISPRS-J. Photogramm. Remote Sens."},{"doi-asserted-by":"crossref","unstructured":"Patro, S., and Sahu, K.K. (2015). Normalization: A Preprocessing Stage. arXiv.","key":"ref_34","DOI":"10.17148\/IARJSET.2015.2305"},{"doi-asserted-by":"crossref","unstructured":"Ma, N., Zhang, X., Zheng, H.-T., and Sun, J. (2018, January 8\u201314). Shufflenet v2: Practical Guidelines for Efficient CNN Architecture Design. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","key":"ref_35","DOI":"10.1007\/978-3-030-01264-9_8"},{"unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv.","key":"ref_36"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"112599","DOI":"10.1016\/j.rse.2021.112599","article-title":"Towards Interpreting Multi-Temporal Deep Learning Models in Crop Mapping","volume":"264","author":"Xu","year":"2021","journal-title":"Remote Sens. Environ."},{"unstructured":"Liu, W., Wen, Y., Yu, Z., and Yang, M. (2016, January 19\u201324). Large-Margin Softmax Loss for Convolutional Neural Networks. Proceedings of the 33rd International Conference on Machine Learning, New York, NY, USA.","key":"ref_38"},{"unstructured":"Guo, C., Pleiss, G., Sun, Y., and Weinberger, K.Q. (2017, January 6\u201311). On Calibration of Modern Neural Networks. Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia.","key":"ref_39"},{"unstructured":"M\u00fcller, R., Kornblith, S., and Hinton, G. (2019). When Does Label Smoothing Help?. arXiv.","key":"ref_40"},{"doi-asserted-by":"crossref","unstructured":"Zhou, D., Hou, Q., Chen, Y., Feng, J., and Yan, S. (2020, January 23\u201328). Rethinking Bottleneck Structure for Efficient Mobile Network Design. Proceedings of the 16th European Conference on Computer Vision, Glasgow, UK.","key":"ref_41","DOI":"10.1007\/978-3-030-58580-8_40"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"102578","DOI":"10.1016\/j.jvcir.2019.102578","article-title":"Dimension Reduction of Image Deep Feature Using PCA","volume":"63","author":"Ma","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"3459","DOI":"10.1002\/jsfa.9564","article-title":"Tea Diseases Detection Based on Fast Infrared Thermal Image Processing Technology","volume":"99","author":"Yang","year":"2019","journal-title":"J. Sci. Food Agric."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5102\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:48:54Z","timestamp":1760168934000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/24\/5102"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,15]]},"references-count":43,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13245102"],"URL":"https:\/\/doi.org\/10.3390\/rs13245102","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,12,15]]}}}