{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:50:09Z","timestamp":1777704609944,"version":"3.51.4"},"reference-count":20,"publisher":"SAGE Publications","issue":"4","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,10,4]]},"abstract":"<jats:p>While deep learning based object detection methods have achieved high accuracy in fruit detection, they rely on large labeled datasets to train the model and assume that the training and test samples come from the same domain. This paper proposes a cross-domain fruit detection method with image and feature alignments. It first converts the source domain image into the target domain through an attention-guided generative adversarial network to achieve the image-level alignment. Then, the knowledge distillation with mean teacher model is fused in the yolov5 network to achieve the feature alignment between the source and target domains. A contextual aggregation module similar to a self-attention mechanism is added to the detection network to improve the cross-domain feature learning by learning global features. A source domain (orange) and two target domain (tomato and apple) datasets are used for the evaluation of the proposed method. The recognition accuracy on the tomato and apple datasets are 87.2% and 89.9%, respectively, with an improvement of 10.3% and 2.4%, respectively, compared to existing methods on the same datasets.<\/jats:p>","DOI":"10.3233\/jifs-232104","type":"journal-article","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T11:47:18Z","timestamp":1690285638000},"page":"5837-5851","source":"Crossref","is-referenced-by-count":1,"title":["Domain adaptive fruit detection method based on multiple alignments"],"prefix":"10.1177","volume":"45","author":[{"given":"An","family":"Guo","sequence":"first","affiliation":[{"name":"School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kaiqiong","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Meng","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"issue":"7","key":"10.3233\/JIFS-232104_ref1","doi-asserted-by":"crossref","first-page":"e318","DOI":"10.1016\/S2542-5196(19)30095-6","article-title":"Gaps between fruit and vegetable production, demand, and recommended consumption at global and national levels: An integrated modelling study","volume":"3","author":"Mason-D\u2019Croz","year":"2019","journal-title":"The Lancet Planetary Health"},{"key":"10.3233\/JIFS-232104_ref2","doi-asserted-by":"crossref","first-page":"106812","DOI":"10.1016\/j.compag.2022.106812","article-title":"Fruit yield prediction and estimation in orchards: A state-of-the-art comprehensive review for both direct and indirect methods","volume":"195","author":"He","year":"2022","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.3233\/JIFS-232104_ref3","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.compag.2019.04.017","article-title":"Deep learning\u2013Method overview and review of use for fruit detection and yield estimation","volume":"162","author":"Koirala","year":"2019","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.3233\/JIFS-232104_ref14","doi-asserted-by":"crossref","first-page":"103649","DOI":"10.1016\/j.cviu.2023.103649","article-title":"SSDA-YOLO: Semi-supervised domain adaptive YOLO for cross-domain object detection","volume":"229","author":"Zhou","year":"2023","journal-title":"Computer Vision and Image Understanding"},{"key":"10.3233\/JIFS-232104_ref15","unstructured":"Tarvainen A. and Valpola H. , Mean teachers are better role models: Weight-averaged consistency targets improve semisupervised deep learning results, NIPS 30 (2017)."},{"issue":"2","key":"10.3233\/JIFS-232104_ref19","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1002\/rob.21902","article-title":"A comparative study of fruitdetection and counting methods for yield mapping in apple orchards","volume":"37","author":"H\u00e4ni","year":"2020","journal-title":"Journal of Field Robotics"},{"key":"10.3233\/JIFS-232104_ref20","doi-asserted-by":"crossref","first-page":"19043","DOI":"10.1007\/s11042-021-10704-7","article-title":"Fruits yield estimation using Faster R-CNN with MIoU","volume":"80","author":"Behera","year":"2021","journal-title":"Multimedia Tools and Applications"},{"key":"10.3233\/JIFS-232104_ref21","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1016\/j.compag.2019.01.012","article-title":"Apple detection during different growth stages in orchards using the improved YOLO-V3 model","volume":"157","author":"Tian","year":"2019","journal-title":"Computers and Electronics in Agriculture"},{"issue":"7","key":"10.3233\/JIFS-232104_ref22","doi-asserted-by":"crossref","first-page":"2145","DOI":"10.3390\/s20072145","article-title":"Mbouembe, YOLO-tomato: A robust algorithm for tomato detection based on YOLOv3","volume":"20","author":"Liu","year":"2020","journal-title":"Sensors"},{"key":"10.3233\/JIFS-232104_ref29","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1007\/s11263-020-01394-z","article-title":"CDTD: A large-scale cross-domain benchmark for instance-level image-to-image translation and domain adaptive object detection","volume":"129","author":"Shen","year":"2021","journal-title":"International Journal of Computer Vision"},{"issue":"12","key":"10.3233\/JIFS-232104_ref30","doi-asserted-by":"crossref","first-page":"2633","DOI":"10.3390\/plants10122633","article-title":"Domain adaptation of synthetic images for wheat head detection","volume":"10","author":"Hartley","year":"2021","journal-title":"Plants"},{"issue":"9","key":"10.3233\/JIFS-232104_ref32","doi-asserted-by":"crossref","first-page":"1619","DOI":"10.3390\/rs13091619","article-title":"A real-time apple targets detection method for picking robot based on improved YOLOv5","volume":"13","author":"Yan","year":"2021","journal-title":"Remote Sensing"},{"key":"10.3233\/JIFS-232104_ref34","doi-asserted-by":"crossref","first-page":"106984","DOI":"10.1016\/j.compag.2022.106984","article-title":"An accurate detection and segmentation model of obscured green fruits","volume":"197","author":"Liu","year":"2022","journal-title":"Computers and Electronics in Agriculture"},{"key":"10.3233\/JIFS-232104_ref35","doi-asserted-by":"crossref","first-page":"107194","DOI":"10.1016\/j.compag.2022.107194","article-title":"A deep learning approach in-corporating YOLO v5 and attention mechanisms for field real-time detection of the invasive weed Solanum rostratum Dunal seedlings","volume":"199","author":"Wang","year":"2022","journal-title":"Computers and Electronics in Agriculture"},{"issue":"11","key":"10.3233\/JIFS-232104_ref36","doi-asserted-by":"crossref","first-page":"1673","DOI":"10.3390\/electronics11111673","article-title":"Improved YOLO v5 wheat ear detection algorithm based on attention mechanism","volume":"11","author":"Li","year":"2022","journal-title":"Electronics"},{"issue":"2","key":"10.3233\/JIFS-232104_ref37","doi-asserted-by":"crossref","first-page":"852","DOI":"10.1109\/LRA.2020.2965061","article-title":"MinneApple: A benchmark dataset for apple detection and segmentation","volume":"5","author":"H\u00e4ni","year":"2020","journal-title":"IEEE Robotics and Automation Letters"},{"issue":"10","key":"10.3233\/JIFS-232104_ref38","doi-asserted-by":"crossref","first-page":"2984","DOI":"10.3390\/s20102984","article-title":"Intact detection of highly occluded immature tomatoes on plants using deep learning techniques","volume":"20","author":"Mu","year":"2020","journal-title":"Sensors"},{"issue":"8","key":"10.3233\/JIFS-232104_ref39","doi-asserted-by":"crossref","first-page":"8574","DOI":"10.1109\/TCYB.2021.3095305","article-title":"Enhancing geometric factors in model learning and inference for object detection and instance segmentation","volume":"52","author":"Zheng","year":"2021","journal-title":"IEEE Transactions on Cybernetics"},{"issue":"8","key":"10.3233\/JIFS-232104_ref41","doi-asserted-by":"crossref","first-page":"3106","DOI":"10.1080\/01431161.2022.2085069","article-title":"Sensitivity examination of YOLOv4 regarding test image distortion and training dataset attribute for apple flower bud classification","volume":"43","author":"Yuan","year":"2022","journal-title":"International Journal of Remote Sensing"},{"key":"10.3233\/JIFS-232104_ref42","unstructured":"Van der Maaten L. and Hinton G. , Visualizing data using t-SNE, Journal of Machine Learning Research 9(11) (2008)."}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-232104","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:41:36Z","timestamp":1777455696000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-232104"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,4]]},"references-count":20,"journal-issue":{"issue":"4"},"URL":"https:\/\/doi.org\/10.3233\/jifs-232104","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,10,4]]}}}