{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T12:27:41Z","timestamp":1785414461106,"version":"3.56.0"},"reference-count":25,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T00:00:00Z","timestamp":1777507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012190","name":"Ministry of Science and Higher Education of the Russian Federation","doi-asserted-by":"crossref","award":["FZNE2024-0006"],"award-info":[{"award-number":["FZNE2024-0006"]}],"id":[{"id":"10.13039\/501100012190","id-type":"DOI","asserted-by":"crossref"}]},{"award":["FZNE2024-0006"],"award-info":[{"award-number":["FZNE2024-0006"]}],"id":[{"id":"https:\/\/ror.org\/00ghqgy32","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>This paper addresses the problem of automated segmentation of plant green biomass in field crop images aimed at improving the accuracy of crop and weed identification. To construct a training dataset for neural network models, an automatic annotation algorithm is proposed, enabling the generation of polygonal object masks without human intervention. The method is based on adaptive analysis of color characteristics of plant fragments with iterative narrowing of the hue range in the HSV color space, combined with an integral quality metric that accounts for the dynamics of contour area and shape. The proposed method achieved an IoU of 93.22% and a DSC of 96.30%, demonstrating a high level of agreement between automatic and manual annotations. The generated masks are used to train segmentation models of the YOLO11-seg family. Models of different scales (n, s, m, l, x) were trained and evaluated using standard metrics, including Intersection over Union (IoU), mAP@0.5, mAP@0.5\u20130.95, F1-score, and Precision\u2013Recall (PR) curves. Experimental results demonstrate that models trained on automatically generated annotations achieve stable segmentation performance of plant green biomass. The best results were obtained with the YOLO11m-seg model, achieving an F1-score of 0. 772. The results confirm the effectiveness of the proposed approach and demonstrate acceptable segmentation quality, supported by both quantitative metrics and visual analysis. The developed automatic annotation algorithm can be used to expand training datasets in computer vision tasks for agricultural applications.<\/jats:p>","DOI":"10.3390\/jimaging12050192","type":"journal-article","created":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T10:47:28Z","timestamp":1777546048000},"page":"192","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Automatic Polygon Annotation of Plant Objects for Training Dataset Preparation in Green Biomass Segmentation Tasks"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6606-2015","authenticated-orcid":false,"given":"Evgeniy","family":"Ivliev","sequence":"first","affiliation":[{"name":"Laboratory \u201cModeling and Development of Intelligent Agricultural Engineering Systems\u201d, Don State Technical University, Rostov-on-Don 344000, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Valery","family":"Gvindjiliya","sequence":"additional","affiliation":[{"name":"Laboratory \u201cModeling and Development of Intelligent Agricultural Engineering Systems\u201d, Don State Technical University, Rostov-on-Don 344000, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3556-7758","authenticated-orcid":false,"given":"Danila","family":"Donskoy","sequence":"additional","affiliation":[{"name":"Laboratory \u201cModeling and Development of Intelligent Agricultural Engineering Systems\u201d, Don State Technical University, Rostov-on-Don 344000, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yevgeniy","family":"Chayka","sequence":"additional","affiliation":[{"name":"Laboratory \u201cModeling and Development of Intelligent Agricultural Engineering Systems\u201d, Don State Technical University, Rostov-on-Don 344000, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Banerjee, S., Ramos, P., Reberg-Horton, C., Mirsky, S., Locke, A., and Lobaton, E. (2025). A Multiple Instance Learning Approach to Study Leaf Wilt in Soybean Plants. Agriculture, 15.","DOI":"10.3390\/agriculture15060614"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Bumbaca, S., and Borgogno-Mondino, E. (2025). On the Minimum Dataset Requirements for Fine-Tuning an Object Detector for Arable Crop Plant Counting: A Case Study on Maize Seedlings. Remote Sens., 17.","DOI":"10.3390\/rs17132190"},{"key":"ref_3","first-page":"283","article-title":"Development of an Algorithm for Semantic Segmentation of Earth Remote Sensing Data to Determine Phytoplankton Populations","volume":"24","author":"Belova","year":"2024","journal-title":"Adv. Eng. Res."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Pallottino, F., Menesatti, P., Figorilli, S., Antonucci, F., Tomasone, R., Colantoni, A., and Costa, C. (2018). Machine Vision Retrofit System for Mechanical Weed Control in Precision Agriculture Applications. Sustainability, 10.","DOI":"10.3390\/su10072209"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, X., Wang, Q., Qiao, Y., Zhang, X., Lu, C., and Wang, C. (2024). Precision Weed Management for Straw-Mulched Maize Field: Advanced Weed Detection and Targeted Spraying Based on Enhanced YOLO v5s. Agriculture, 14.","DOI":"10.3390\/agriculture14122134"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Donskoy, D., Gvindjiliya, V., and Ivliev, E. (2025). TinyML Classification for Agriculture Objects with ESP32. Digital, 5.","DOI":"10.3390\/digital5040048"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"350","DOI":"10.3390\/agriengineering2020024","article-title":"Mapping and Estimating Weeds in Cotton Using Unmanned Aerial Systems-Borne Imagery","volume":"2","author":"Sapkota","year":"2020","journal-title":"AgriEngineering"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Peerbhay, K., Devsaran, N., Lottering, R., Agjee, N., and Parag, M. (2025). Mapping Commercial Forests Infected by the Novel Variant of Elsino\u00eb masingae, Using Unmanned Aerial Technology in Southern Africa. Forests, 16.","DOI":"10.3390\/f16060966"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Celikkan, E., Kunzmann, T., Yeskaliyev, Y., Itzerott, S., Klein, N., and Herold, M. (2025). WeedsGalore: A Multispectral and Multitemporal UAV-Based Dataset for Crop and Weed Segmentation in Agricultural Maize Fields. Proceedings of the 2025 IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Tucson, AZ, USA, 26 February\u20136 March 2025, IEEE.","DOI":"10.1109\/WACV61041.2025.00467"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"109707","DOI":"10.1016\/j.compag.2024.109707","article-title":"Efficient weed segmentation in maize fields: A semi-supervised approach for precision weed management with reduced annotation overhead","volume":"229","author":"Guo","year":"2025","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"599","DOI":"10.18699\/VJGB-22-74","article-title":"Phenotypic traits differentiating the genetic resources of pea (Pisum sativum L.) by the type of use","volume":"26","author":"Semenova","year":"2022","journal-title":"Vavilov J. Genet. Breed."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Safonova, A., Guirado, E., Maglinets, Y., Alcaraz-Segura, D., and Tabik, S. (2021). Olive Tree Biovolume from UAV Multi-Resolution Image Segmentation with Mask R-CNN. Sensors, 21.","DOI":"10.3390\/s21051617"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Henke, M., Neumann, K., Altmann, T., and Gladilin, E. (2021). Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg). Agriculture, 11.","DOI":"10.3390\/agriculture11111098"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Okyere, F.G., Cudjoe, D., Sadeghi-Tehran, P., Virlet, N., Riche, A.B., Castle, M., Greche, L., Mohareb, F., Simms, D., and Mhada, M. (2023). Machine Learning Methods for Automatic Segmentation of Images of Field- and Glasshouse-Based Plants for High-Throughput Phenotyping. Plants, 12.","DOI":"10.3390\/plants12102035"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kozhekin, M.V., Genaev, M.A., Komyshev, E.G., Zavyalov, Z.A., and Afonnikov, D.A. (2025). Plant Detection in RGB Images from Unmanned Aerial Vehicles Using Segmentation by Deep Learning and an Impact of Model Accuracy on Downstream Analysis. J. Imaging, 11.","DOI":"10.3390\/jimaging11010028"},{"key":"ref_16","first-page":"4","article-title":"Automated sugar beet seedling detection and mapping using a UAV-borne RGB camera and deep learning techniques","volume":"27","author":"Mudarisov","year":"2025","journal-title":"Agric. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"101826","DOI":"10.1016\/j.atech.2026.101826","article-title":"Automatic annotation and performance evaluation of orchard pear fruit using SAM-based zero-shot segmentation","volume":"13","author":"Wang","year":"2026","journal-title":"Smart Agric. Technol."},{"key":"ref_18","first-page":"37","article-title":"Automatic Monitoring of Smart Greenhouse Parameters and Detection of Plant Diseases by Neural Networks","volume":"Volume 247","author":"Shamtsyan","year":"2022","journal-title":"Robotics, Machinery and Engineering Technology for Precision Agriculture; Smart Innovation, Systems and Technologies"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Chen, C., Zang, Y., Jiao, J., Yan, D., Fan, Z., Cui, Z., and Zhang, M. (2025). An Efficient Group Convolution and Feature Fusion Method for Weed Detection. Agriculture, 15.","DOI":"10.3390\/agriculture15010037"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"L\u00f3pez-Correa, J.M., Moreno, H., Ribeiro, A., and And\u00fajar, D. (2022). Intelligent Weed Management Based on Object Detection Neural Networks in Tomato Crops. Agronomy, 12.","DOI":"10.3390\/agronomy12122953"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Sodjinou, S.G., Mahama, A.T.S., and Gouton, P. (2025). Automatic Segmentation of Plants and Weeds in Wide-Band Multispectral Imaging (WMI). J. Imaging, 11.","DOI":"10.3390\/jimaging11030085"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Darbyshire, M., Salazar-Gomez, A., Gao, J., Sklar, E.I., and Parsons, S. (2023). Towards practical object detection for weed spraying in precision agriculture. Front. Plant Sci., 14.","DOI":"10.3389\/fpls.2023.1183277"},{"key":"ref_23","first-page":"112","article-title":"Algorithms for the reduction of the number of points required to represent a digitized line or its caricature","volume":"10","author":"Douglas","year":"1973","journal-title":"Can. Cartogr."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Luo, R., Zhao, R., and Yi, B. (2025). Enhanced YOLO11n-Seg with Attention Mechanism and Geometric Metric Optimization for Instance Segmentation of Ripe Blueberries in Complex Greenhouse Environments. Agriculture, 15.","DOI":"10.3390\/agriculture15151697"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Qiu, Z., Huang, X., Sun, Z., Li, S., and Wang, J. (2025). GS-YOLO-Seg: A Lightweight Instance Segmentation Method for Low-Grade Graphite Ore Sorting Based on Improved YOLO11-Seg. Sustainability, 17.","DOI":"10.3390\/su17125663"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/5\/192\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T04:21:52Z","timestamp":1777695712000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/12\/5\/192"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,30]]},"references-count":25,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,5]]}},"alternative-id":["jimaging12050192"],"URL":"https:\/\/doi.org\/10.3390\/jimaging12050192","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,30]]}}}