{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T08:15:09Z","timestamp":1774944909478,"version":"3.50.1"},"reference-count":32,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T00:00:00Z","timestamp":1718064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Central Guide to Local Science and Technology Development","award":["20221ZDF04048"],"award-info":[{"award-number":["20221ZDF04048"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate determination of the number and location of immature small yellow peaches is crucial for bagging, thinning, and estimating yield in modern orchards. However, traditional methods have faced challenges in accurately distinguishing immature yellow peaches due to their resemblance to leaves and susceptibility to variations in shooting angles and distance. To address these issues, we proposed an improved target-detection model (EMA-YOLO) based on YOLOv8. Firstly, the sample space was enhanced algorithmically to improve the diversity of samples. Secondly, an EMA attention-mechanism module was introduced to encode global information; this module could further aggregate pixel-level features through dimensional interaction and strengthen small-target-detection capability by incorporating a 160 \u00d7 160 detection head. Finally, EIoU was utilized as a loss function to reduce the incidence of missed detections and false detections of the target small yellow peaches under the condition of high density of yellow peaches. Experimental results show that compared with the original YOLOv8n model, the EMA-YOLO model improves mAP by 4.2%, Furthermore, compared with SDD, Objectbox, YOLOv5n, and YOLOv7n, this model\u2019s mAP was improved by 30.1%, 14.2%,15.6%, and 7.2%, respectively. In addition, the EMA-YOLO model achieved good results under different conditions of illumination and shooting distance and significantly reduced the number of missed detections. Therefore, this method can provide technical support for smart management of yellow-peach orchards.<\/jats:p>","DOI":"10.3390\/s24123783","type":"journal-article","created":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T12:11:00Z","timestamp":1718107860000},"page":"3783","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["EMA-YOLO: A Novel Target-Detection Algorithm for Immature Yellow Peach Based on YOLOv8"],"prefix":"10.3390","volume":"24","author":[{"given":"Dandan","family":"Xu","sequence":"first","affiliation":[{"name":"School of Software, Jiangxi Agricultural University, Nanchang 330045, China"}]},{"given":"Hao","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Software, Jiangxi Normal University, Nanchang 330045, China"}]},{"given":"Yue","family":"Liao","sequence":"additional","affiliation":[{"name":"School of Software, Jiangxi Agricultural University, Nanchang 330045, China"}]},{"given":"Hongruo","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Software, Jiangxi Agricultural University, Nanchang 330045, China"}]},{"given":"Zhizhang","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Software, Jiangxi Agricultural University, Nanchang 330045, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4611-8533","authenticated-orcid":false,"given":"Hua","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Software, Jiangxi Agricultural University, Nanchang 330045, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Cao, Z., Zhou, D., Ge, X., Luo, Y., and Su, J. (2022). The role of essential oils in maintaining the postharvest quality and preservation of peach and other fruits. J. Food Biochem., 46.","DOI":"10.1111\/jfbc.14513"},{"key":"ref_2","first-page":"17","article-title":"The introduction performance and supporting cultivation techniques of Jinxiu yellow peach in Hubei Huanggang","volume":"335","author":"Wu","year":"2022","journal-title":"Fruit Tree Pract. Technol. Inf."},{"key":"ref_3","first-page":"231","article-title":"Effects of Pre-harvest Bagging and Non-bagging Treatment on Postharvest Storage Quality of Yellow-Flesh Peach","volume":"21","author":"Huang","year":"2021","journal-title":"J. Chin. Inst. Food Sci. Technol."},{"key":"ref_4","first-page":"174","article-title":"Nursery tree seedling detection and counting based on YOLOv3 network","volume":"7","author":"Yuan","year":"2022","journal-title":"J. For. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rahnemoonfar, M., and Sheppard, C. (2017). Deep Count: Fruit Counting Based on Deep Simulated Learning. Sensors, 17.","DOI":"10.3390\/s17040905"},{"key":"ref_6","first-page":"87","article-title":"Survey of Fruit Object Detection Algorithms in Computer Vision","volume":"322","author":"Li","year":"2022","journal-title":"Comput. Mod."},{"key":"ref_7","first-page":"159","article-title":"Identification of green citrus based on improved YOLOV3 in natural environment","volume":"42","author":"Song","year":"2021","journal-title":"J. Chin. Agric. Mech."},{"key":"ref_8","first-page":"183","article-title":"Detection of green walnut by improved YOLOv3","volume":"38","author":"Hao","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_9","first-page":"234","article-title":"Camellia oleifera Fruit Detection in Natural Scene Based on YOLO v5s","volume":"53","author":"Song","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_10","first-page":"232","article-title":"Cherry Fruit Detection Method in Natural Scene Based on Improved YOLOv5","volume":"53","author":"Zhang","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_11","first-page":"204","article-title":"Detecting bagged citrus using a Semi-Supervised SPM-YOLOv5. Trans","volume":"38","author":"Lv","year":"2022","journal-title":"Chin. Soc. Agric. Eng."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Xie, J., Peng, J., Wang, J., Chen, B., Jing, T., Sun, D., Gao, P., Wang, W., Lu, J., and Yetan, R. (2022). Litchi Detection in a Complex Natural Environment Using the YOLOv5-Litchi Model. Agronomy, 12.","DOI":"10.3390\/agronomy12123054"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"107062","DOI":"10.1016\/j.compag.2022.107062","article-title":"Complete and accurate holly fruits counting using YOLOX object detection","volume":"198","author":"Zhang","year":"2022","journal-title":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Tang, R., Lei, Y., Luo, B., Zhang, J., and Mu, J. (2023). YOLOv7-Plum:Advancing Plum Fruit Detection in Natural Environments with Deep Learing. Plants, 12.","DOI":"10.3390\/plants12152883"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, P., and Yin, H. (2023). YOLOv7-Peach: An Algorithm for Immature Small Yellow Peaches Detection in Complex Natural Environments. Sensors, 23.","DOI":"10.3390\/s23115096"},{"key":"ref_16","first-page":"349","article-title":"Survey on few-shot learning","volume":"32","author":"Zhao","year":"2021","journal-title":"Ruan Jian Xue Bao\/J. Softw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.compag.2018.02.016","article-title":"Deep learning in agriculture: A survey","volume":"147","author":"Kamilaris","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","first-page":"3205","article-title":"G-YOLO v7:Target Detection Algorithm for UAV Aerial Images","volume":"55","author":"Chen","year":"2024","journal-title":"J. Optoelectron. Laser"},{"key":"ref_19","first-page":"231","article-title":"Lightweight Apple Recognition Method in Natural Orchard Environment Based on Improved YOLO v7 Model","volume":"55","author":"Zhang","year":"2024","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_20","first-page":"161","article-title":"Detecting and counting of spring-see citrus using YOLOv4 network model and recursive fusion of features","volume":"37","author":"Yi","year":"2021","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_21","first-page":"890","article-title":"A dense pedestrian detection algorithm with improved YOLOv8","volume":"44","author":"Gao","year":"2023","journal-title":"J. Graph."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ouyang, D., He, S., Zhan, J., Guo, H., Huang, Z., Luo, M., and Zhang, G. (2023, January 4\u201310). Efficient Multi-Scale Attention Module with Cross-Spatial Learning. Proceedings of the ICASSP 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Rhodes Island, Greece.","DOI":"10.1109\/ICASSP49357.2023.10096516"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hou, Q., Zhou, D., and Feng, J. (2021). Coordinate Attention for Efficient Mobile Network Design, IEEE.","DOI":"10.1109\/CVPR46437.2021.01350"},{"key":"ref_24","first-page":"12993","article-title":"Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression","volume":"34","author":"Zheng","year":"2019","journal-title":"AAAI Conf. Artif. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"146","DOI":"10.1016\/j.neucom.2022.07.042","article-title":"Focal and Efficient IOU Loss for Accurate Bounding Box Regression","volume":"506","author":"Zhang","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_26","first-page":"77","article-title":"Pilot Workload Assessment Based on Improved KNN Algorithms","volume":"52","author":"Wu","year":"2022","journal-title":"Aeronaut. Comput. Tech."},{"key":"ref_27","first-page":"221","article-title":"Tomato seedling classification detection using improved YOLOv3-Tiny","volume":"38","author":"Zhang","year":"2022","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_28","first-page":"175","article-title":"Recognizing of camellia oleifera fruits in natural environment using multi-modal images","volume":"39","author":"Zhou","year":"2023","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Redmon, J., Divvala, S., Girshick, R., and Farhadi, A. (2016, January 27\u201330). You Only Look Once: Unified, Real-Time Object Detection. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.91"},{"key":"ref_30","first-page":"390","article-title":"ObjectBox: From Centers to Boxes for Anchor-Free Object Detection","volume":"13670","author":"Zand","year":"2022","journal-title":"Eur. Conf. Comput. Vis."},{"key":"ref_31","first-page":"21","article-title":"SSD: Single Shot MultiBox Detector","volume":"Volume 9905","author":"Liu","year":"2016","journal-title":"Computer Vision-ECCV, Proceedings of the 14th European Conference, Amsterdam, The Netherlands, 11\u201314 October 2016"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1007\/s11263-019-01228-7","article-title":"Grad-CAM: Visual Explanations from Deep Networks via Grdient-based Localization","volume":"128","author":"Selvaraju","year":"2020","journal-title":"Int. J. Comput. Vis."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3783\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:56:52Z","timestamp":1760108212000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/12\/3783"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,11]]},"references-count":32,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["s24123783"],"URL":"https:\/\/doi.org\/10.3390\/s24123783","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,11]]}}}