{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T14:59:37Z","timestamp":1770994777556,"version":"3.50.1"},"reference-count":48,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,3]],"date-time":"2023-09-03T00:00:00Z","timestamp":1693699200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Plant health plays an important role in influencing agricultural yields and poor plant health can lead to significant economic losses. Grapes are an important and widely cultivated plant, especially in the southern regions of Russia. Grapes are subject to a number of diseases that require timely diagnosis and treatment. Incorrect identification of diseases can lead to large crop losses. A neural network deep learning dataset of 4845 grape disease images was created. Eight categories of common grape diseases typical of the Black Sea region were studied: Mildew, Oidium, Anthracnose, Esca, Gray rot, Black rot, White rot, and bacterial cancer of grapes. In addition, a set of healthy plants was included. In this paper, a new selective search algorithm for monitoring the state of plant development based on computer vision in viticulture, based on YOLOv5, was considered. The most difficult part of object detection is object localization. As a result, the fast and accurate detection of grape health status was realized. The test results showed that the accuracy was 97.5%, with a model size of 14.85 MB. An analysis of existing publications and patents found using the search \u201cComputer vision in viticulture\u201d showed that this technology is original and promising. The developed software package implements the best approaches to the control system in viticulture using computer vision technologies. A mobile application was developed for practical use by the farmer. The developed software and hardware complex can be installed in any vehicle. Such a mobile system will allow for real-time monitoring of the state of the vineyards and will display it on a map. The novelty of this study lies in the integration of software and hardware. Decision support system software can be adapted to solve other similar problems. The software product commercialization plan is focused on the automation and robotization of agriculture, and will form the basis for adding the next set of similar software.<\/jats:p>","DOI":"10.3390\/computation11090171","type":"journal-article","created":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T02:14:50Z","timestamp":1693793690000},"page":"171","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Intelligent Monitoring System to Assess Plant Development State Based on Computer Vision in Viticulture"],"prefix":"10.3390","volume":"11","author":[{"given":"Marina","family":"Rudenko","sequence":"first","affiliation":[{"name":"Institute of Physics and Technology, V.I. Vernadsky Crimean Federal University, Simferopol 295007, Russia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7678-9210","authenticated-orcid":false,"given":"Anatoliy","family":"Kazak","sequence":"additional","affiliation":[{"name":"Humanitarian Pedagogical Academy, V.I. Vernadsky Crimean Federal University, Simferopol 295007, Russia"}]},{"given":"Nikolay","family":"Oleinikov","sequence":"additional","affiliation":[{"name":"Humanitarian Pedagogical Academy, V.I. Vernadsky Crimean Federal University, Simferopol 295007, Russia"}]},{"given":"Angela","family":"Mayorova","sequence":"additional","affiliation":[{"name":"Humanitarian Pedagogical Academy, V.I. Vernadsky Crimean Federal University, Simferopol 295007, Russia"}]},{"given":"Anna","family":"Dorofeeva","sequence":"additional","affiliation":[{"name":"Humanitarian Pedagogical Academy, V.I. Vernadsky Crimean Federal University, Simferopol 295007, Russia"}]},{"given":"Dmitry","family":"Nekhaychuk","sequence":"additional","affiliation":[{"name":"Sevastopol Branch, Plekhanov Russian University of Economics, Sevastopol 299053, Russia"}]},{"given":"Olga","family":"Shutova","sequence":"additional","affiliation":[{"name":"Institute of Education and Humanities, Sevastopol State University, Sevastopol 299053, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Mavridou, E., Vrochidou, E., Papakostas, G.A., Pachidis, T., and Kaburlasos, V.G. (2019). Machine Vision Systems in Precision Agriculture for Crop Farming. J. Imaging, 5.","DOI":"10.3390\/jimaging5120089"},{"key":"ref_2","first-page":"1","article-title":"Computer vision technology in agricultural automation\u2014A review","volume":"7","author":"Tian","year":"2020","journal-title":"Inf. Process. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.compag.2012.01.004","article-title":"Ripeness estimation of grape berries and seeds by image analysis","volume":"82","author":"Melgosa","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_4","first-page":"344","article-title":"Comparative Analysis of Deep Learning Architectures for Grape Cluster Instance Segmentation","volume":"9","author":"Barbole","year":"2021","journal-title":"Inf. Technol. Ind."},{"key":"ref_5","unstructured":"Bochkovskiy, A., Wang, C.Y., and Liao, H.Y.M. (2020). YOLOv4: Optimal Speed and Accuracy of Object Detection. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Zhang, C., Ding, H., Shi, Q., and Wang, Y. (2022). Grape Cluster Real-Time Detection in Complex Natural Scenes Based on YOLOv5s Deep Learning Network. Agriculture, 12.","DOI":"10.3390\/agriculture12081242"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"012093","DOI":"10.1088\/1742-6596\/1883\/1\/012093","article-title":"Few-Shot Grape Leaf Diseases Classification Based on Generative Adversarial Network","volume":"1883","author":"Zeng","year":"2021","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"779","DOI":"10.5424\/sjar\/2009074-1092","article-title":"Review. Precision Viticulture. Research topics, challenges and opportunities in site-specific vineyard management","volume":"7","author":"Casasnovas","year":"2009","journal-title":"Span. J. Agric. Res."},{"key":"ref_9","first-page":"28","article-title":"A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing","volume":"2","author":"Ireri","year":"2019","journal-title":"Artif. Intell. Agric."},{"key":"ref_10","first-page":"189","article-title":"Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters","volume":"4","author":"Fina","year":"2013","journal-title":"Int. J. Adv. Biotechnol. Res."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"100480","DOI":"10.1109\/ACCESS.2021.3097050","article-title":"Grape leaf spot identification under limited samples by fine grained-GAN","volume":"9","author":"Zhou","year":"2021","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Huang, Z., Qin, A., Lu, J., Menon, A., and Gao, J. (2020, January 2\u20136). Grape Leaf Disease Detection and Classification Using Machine Learning. Proceedings of the 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics), Rhodes Island, Greece.","DOI":"10.1109\/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00150"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Thet, K.Z., Htwe, K.K., and Thein, M.M. (2020, January 4\u20135). Grape leaf diseases classification using convolutional neural network. Proceedings of the 2020 International Conference on Advanced Information Technologies (ICAIT), Yangon, Myanmar.","DOI":"10.1109\/ICAIT51105.2020.9261801"},{"key":"ref_15","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."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1016\/j.compag.2017.03.021","article-title":"Embedded vision detection of defective orange by fast adaptive lightness correction algorithm","volume":"138","author":"Rong","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"110102","DOI":"10.1016\/j.jfoodeng.2020.110102","article-title":"On line detection of defective apples using computer vision system combined with deep learning methods","volume":"286","author":"Fan","year":"2020","journal-title":"J. Food Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"3365","DOI":"10.1007\/s00542-020-05123-x","article-title":"Deep learning based real-time Industrial framework for rotten and fresh fruit detection using semantic segmentation","volume":"27","author":"Roy","year":"2021","journal-title":"Microsyst. Technol."},{"key":"ref_19","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":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"102188","DOI":"10.1109\/ACCESS.2020.2998839","article-title":"A data augmentation method based on generative adversarial networks for grape leaf disease identification","volume":"8","author":"Liu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_21","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_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","unstructured":"Simonyan, K., and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_24","first-page":"84","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"256","author":"Krizhevsky","year":"2012","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Taye, M.M. (2023). Understanding of Machine Learning with Deep Learning: Architectures, Workflow, Applications and Future Directions. Computers, 12.","DOI":"10.3390\/computers12050091"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Apaydin, H., Feizi, H., Sattari, M.T., Colak, M.S., Shamshirband, S., and Chau, K.-W. (2020). Comparative Analysis of Recurrent Neural Network Architectures for Reservoir Inflow Forecasting. Water, 12.","DOI":"10.3390\/w12051500"},{"key":"ref_27","unstructured":"Ahmad, J., Farman, H., and Jan, Z. (2019). Deep Learning: Convergence to Big Data Analytics, Springer. SpringerBriefs in Computer Science."},{"key":"ref_28","unstructured":"Wang, Y., Xu, C., Xu, C., Xu, C., and Tao, D. (arXiv, 2018). Learning versatile filters for efficient convolutional neural networks, arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"9502475","DOI":"10.1155\/2022\/9502475","article-title":"Improved Support Vector Machine and Image Processing Enabled Methodology for Detection and Classification of Grape Leaf Disease","volume":"2022","author":"Ansari","year":"2022","journal-title":"J. Food Qual."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1007\/s42979-021-00815-1","article-title":"Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Alajas, O.J., Concepcion, R., Dadios, E., Sybingco, E., Mendigoria, C.H., and Aquino, H. (2021, January 24\u201326). Prediction of Grape Leaf Black Rot Damaged Surface Percentage Using Hybrid Linear Discriminant Analysis and Decision Tree. Proceedings of the 2021 International Conference on Intelligent Technologies (CONIT), Hubli, India.","DOI":"10.1109\/CONIT51480.2021.9498518"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"69","DOI":"10.2147\/IJWR.S69405","article-title":"Technology in precision viticulture: A state of the art review","volume":"7","author":"Matese","year":"2015","journal-title":"Int. J. Wine Res."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"32504","DOI":"10.1038\/srep32504","article-title":"Ultra-portable, wireless smartphone spectrometer for rapid, non-destructive testing of fruit ripeness","volume":"6","author":"Das","year":"2016","journal-title":"Sci. Rep."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1109\/LRA.2020.2970654","article-title":"In-Field Grape Cluster Size Assessment for Vine Yield Estimation Using a Mobile Robot and a Consumer Level RGB-D Camera","volume":"5","author":"Kurtser","year":"2020","journal-title":"IEEE Robot. Autom. Letters"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. arXiv.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.compag.2017.03.013","article-title":"A computer vision system for early stage grape yield estimation based on shoot detection","volume":"137","author":"Liu","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_37","first-page":"95","article-title":"Efficient identification, localization and quantification of grapevine inflorescences in unprepared field images using Fully Convolutional Networks","volume":"58","author":"Rudolph","year":"2019","journal-title":"J. Grapevine Res."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Rudenko, M., Plugatar, Y., Korzin, V., Kazak, A., Gallini, N., and Gorbunova, N. (2023). The Use of Computer Vision to Improve the Affinity of Rootstock-Graft Combinations and Identify Diseases of Grape Seedlings. Inventions, 8.","DOI":"10.3390\/inventions8040092"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kazak, A., Plugatar, Y., Johnson, J., Grishin, Y., Chetyrbok, P., Korzin, V., Kaur, P., and Kokodey, T. (2022). The Use of Machine Learning for Comparative Analysis of Amperometric and Chemiluminescent Methods for Determining Antioxidant Activity and Determining the Phenolic Profile of Wines. Appl. Syst. Innov., 5.","DOI":"10.3390\/asi5050104"},{"key":"ref_40","unstructured":"Victorino, G., Maia, G., Queiroz, J., Braga, R., Marques, J., and Lopes, C. (2019, January 27\u201329). Grapevine yield prediction using image analysis\u2014Improving the estimation of non-visible bunches. Proceedings of the 12th European Federation for Information Technology in Agriculture, Food and the Environment (EFITA) Conference, Rhodes Island, Greece."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Klodt, M., Herzog, K., T\u00f6pfer, R., and Cremers, D. (2015). Field phenotyping of grapevine growth using dense stereo reconstruction. BMC Bioinform., 16.","DOI":"10.1186\/s12859-015-0560-x"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.biosystemseng.2021.11.011","article-title":"A real-time table grape detection method based on improved YOLOv4-tiny network in complex background","volume":"212","author":"Li","year":"2021","journal-title":"Biosyst. Eng."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.compag.2018.02.021","article-title":"vitisBerry: An Android-smartphone application to early evaluate the number of grapevine berries by means of image analysis","volume":"148","author":"Aquino","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"L\u00fcling, N., Reiser, D., Straub, J., Stana, A., and Griepentrog, H.W. (2023). Fruit Volume and Leaf-Area Determination of Cabbage by a Neural-Network-Based Instance Segmentation for Different Growth Stages. Sensors, 23.","DOI":"10.3390\/s23010129"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Sousa, J.J., Toscano, P., Matese, A., Di Gennaro, S.F., Berton, A., Gatti, M., Poni, S., P\u00e1dua, L., Hru\u0161ka, J., and Morais, R. (2022). UAV-Based Hyperspectral Monitoring Using Push-Broom and Snapshot Sensors: A Multisite Assessment for Precision Viticulture Applications. Sensors, 22.","DOI":"10.3390\/s22176574"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Barriguinha, A., de Castro Neto, M., and Gil, A. (2021). Vineyard Yield Estimation, Prediction, and Forecasting: A Systematic Literature Review. Agronomy, 11.","DOI":"10.3390\/agronomy11091789"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.procs.2017.11.055","article-title":"Very High Resolution Aerial Data to Support Multi-Temporal Precision Agriculture Information Management","volume":"121","author":"Sousa","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.eja.2014.01.004","article-title":"Tree Height Quantification Using Very High Resolution Imagery Acquired from an Unmanned Aerial Vehicle (UAV) and Automatic 3D Photo-Reconstruction Methods","volume":"55","author":"Angileri","year":"2014","journal-title":"Eur. J. Agron."}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/11\/9\/171\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:45:43Z","timestamp":1760129143000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/11\/9\/171"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9,3]]},"references-count":48,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["computation11090171"],"URL":"https:\/\/doi.org\/10.3390\/computation11090171","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,9,3]]}}}