{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,18]],"date-time":"2025-10-18T10:59:42Z","timestamp":1760785182273,"version":"build-2065373602"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T00:00:00Z","timestamp":1712102400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"technology oriented small- and medium-sized enterprise innovation capability enhancement project of Shandong Province","award":["2021TSGC1023"],"award-info":[{"award-number":["2021TSGC1023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurately and effectively detecting the growth position and contour size of apple fruits is crucial for achieving intelligent picking and yield predictions. Thus, an effective fruit edge detection algorithm is necessary. In this study, a fusion edge detection model (RED) based on a convolutional neural network and rough sets was proposed. The Faster-RCNN was used to segment multiple apple images into a single apple image for edge detection, greatly reducing the surrounding noise of the target. Moreover, the K-means clustering algorithm was used to segment the target of a single apple image for further noise reduction. Considering the influence of illumination, complex backgrounds and dense occlusions, rough set was applied to obtain the edge image of the target for the upper and lower approximation images, and the results were compared with those of relevant algorithms in this field. The experimental results showed that the RED model in this paper had high accuracy and robustness, and its detection accuracy and stability were significantly improved compared to those of traditional operators, especially under the influence of illumination and complex backgrounds. The RED model is expected to provide a promising basis for intelligent fruit picking and yield prediction.<\/jats:p>","DOI":"10.3390\/s24072283","type":"journal-article","created":{"date-parts":[[2024,4,3]],"date-time":"2024-04-03T11:01:41Z","timestamp":1712142101000},"page":"2283","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Apple Fruit Edge Detection Model Using a Rough Set and Convolutional Neural Network"],"prefix":"10.3390","volume":"24","author":[{"given":"Junqing","family":"Li","sequence":"first","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}]},{"given":"Ruiyi","family":"Han","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, China University of Petroleum (East China), Changjiang Road No.66, Qingdao 266580, China"}]},{"given":"Fangyi","family":"Li","sequence":"additional","affiliation":[{"name":"College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China"}]},{"given":"Guoao","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}]},{"given":"Yu","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8402-0591","authenticated-orcid":false,"given":"Wei","family":"Yang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Wheat Improvement, College of Life Science, Shandong Agricultural University, Tai\u2019an 271018, China"}]},{"given":"Guanghui","family":"Qi","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}]},{"given":"Liang","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Information Science and Engineering, Shandong Agricultural University, Tai\u2019an 271018, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,4,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"126642","DOI":"10.1016\/j.eja.2022.126642","article-title":"Quantifying the impact of frost damage during flowering on apple yield in Shaanxi province, China","volume":"142","author":"Chen","year":"2023","journal-title":"Eur. J. Agron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"918","DOI":"10.1007\/s40815-020-01030-5","article-title":"Image Edge Detection: A New Approach Based on Fuzzy Entropy and Fuzzy Divergence","volume":"23","author":"Versaci","year":"2021","journal-title":"Int. J. Fuzzy Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"03031","DOI":"10.1051\/matecconf\/202030903031","article-title":"Research on edge detection algorithm based on improved sobel operator","volume":"309","author":"Joo","year":"2020","journal-title":"MATEC Web Conf."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"112421","DOI":"10.1016\/j.enbuild.2022.112421","article-title":"Application and improvement of Canny edge-detection algorithm for exterior wall hollowing detection using infrared thermal images","volume":"274","author":"Lu","year":"2022","journal-title":"Energy Build."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1404","DOI":"10.11591\/eei.v9i4.1837","article-title":"Implementing canny edge detection algorithm for noisy image","volume":"9","author":"Babulak","year":"2020","journal-title":"Bull. Electr. Eng. Inform."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108939","DOI":"10.1016\/j.scienta.2019.108939","article-title":"Automatic image segmentation of oil palm fruits by applying the contour-based approach","volume":"261","author":"Septiarini","year":"2020","journal-title":"Sci. Hortic."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Jiao, Y., Luo, R., Li, Q., Deng, X., Yin, X., Ruan, C., and Jia, W. (2020). Detection and Localization of Overlapped Fruits Application in an Apple Harvesting Robot. Electronics, 9.","DOI":"10.3390\/electronics9061023"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Su, Z., Liu, W., Yu, Z., Hu, D., Liao, Q., Tian, Q., Pietik\u00e4inen, M., and Liu, L. (2021, January 11\u201317). Pixel difference networks for efficient edge detection. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Montreal, BC, Canada.","DOI":"10.1109\/ICCV48922.2021.00507"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17391","DOI":"10.1007\/s11042-018-7106-y","article-title":"Combining SUN-based visual attention model and saliency contour detection algorithm for apple image segmentation","volume":"78","author":"Wang","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ganesan, P., Sathish, B., and Sajiv, G. (March, January 29). Automatic segmentation of fruits in CIELuv color space image using hill climbing optimization and fuzzy C-Means clustering. Proceedings of the 2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare (Startup Conclave), Coimbatore, India.","DOI":"10.1109\/STARTUP.2016.7583960"},{"key":"ref_11","unstructured":"Poma, X.S., Riba, E., and Sappa, A. (2020, January 13\u201319). Dense extreme inception network: Towards a robust cnn model for edge detection. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, Snowmass Village, CO, USA."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1016470","DOI":"10.3389\/fpls.2022.1016470","article-title":"Apple Detection and Instance Segmentation in Natural Environments Using an Improved Mask Scoring R-CNN Model","volume":"13","author":"Wang","year":"2022","journal-title":"Front. Plant Sci."},{"key":"ref_13","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":"Comput. Electron. Agric."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"105900","DOI":"10.1016\/j.compag.2020.105900","article-title":"A Novel Green Apple Segmentation Algorithm Based on Ensemble U-Net under Complex Orchard Environment","volume":"180","author":"Li","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Zhang, C., Zou, K., and Pan, Y. (2020). A Method of Apple Image Segmentation Based on Color-Texture Fusion Feature and Machine Learning. Agronomy, 10.","DOI":"10.3390\/agronomy10070972"},{"key":"ref_16","unstructured":"Ren, S., He, K., Girshick, R., and Sun, J. (2015, January 7\u201312). Faster r-cnn: Towards real-time object detection with region proposal networks. Proceedings of the Advances in Neural Information Processing Systems 28 (NIPS 2015), Montreal, QC, Canada."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bao, J., Wei, S., Lv, J., and Zhang, W. (2020, January 16\u201317). Optimized faster-RCNN in real-time facial expression classification. Proceedings of the IOP Conference Series: Materials Science and Engineering, Chennai, India.","DOI":"10.1088\/1757-899X\/790\/1\/012148"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"101","DOI":"10.17706\/jcp.14.2.101-110","article-title":"Hand Gesture Recognition Based on Faster-RCNN Deep Learning","volume":"14","author":"Yu","year":"2019","journal-title":"J. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Zhang, K., and Shen, H.J.A.S. (2021). Solder joint defect detection in the connectors using improved faster-rcnn algorithm. Appl. Sci., 11.","DOI":"10.3390\/app11020576"},{"key":"ref_20","first-page":"022084","article-title":"Research on ceramic sanitary ware defect detection method based on improved VGG network","volume":"1650","author":"Teng","year":"2020","journal-title":"J. Phys."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.procs.2021.01.025","article-title":"Deep learning in image classification using residual network (ResNet) variants for detection of colorectal cancer","volume":"179","author":"Sarwinda","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_22","first-page":"012089","article-title":"The Study of Locating Diseased Leaves Based on RPN in Complex Environment","volume":"1651","author":"Guo","year":"2020","journal-title":"J. Phys."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"R122","DOI":"10.1016\/j.cub.2020.11.056","article-title":"Color Vision: Decoding Color Space","volume":"31","author":"Retter","year":"2021","journal-title":"Curr. Biol."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Pardede, J., Husada, M.G., Hermana, A.N., and Rumapea, S.A. (2019, January 28\u201329). Fruit ripeness based on RGB, HSV, HSL, L ab color feature using SVM. Proceedings of the 2019 International Conference of Computer Science and Information Technology (ICoSNIKOM), North Sumatera, Indonesia.","DOI":"10.1109\/ICoSNIKOM48755.2019.9111486"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"114622","DOI":"10.1016\/j.eswa.2021.114622","article-title":"Ensemble clustering using extended fuzzy k-means for cancer data analysis","volume":"172","author":"Khan","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_26","first-page":"927","article-title":"A new approach for image segmentation using improved k-means and ROI saliency map","volume":"38","author":"Sharma","year":"2017","journal-title":"J. Inf. Optim. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Yuan, Y., Shi, B., Yost, R., Liu, X., Tian, Y., Zhu, Y., Cao, W., and Cao, Q.J.P. (2022). Optimization of Management Zone Delineation for Precision Crop Management in an Intensive Farming System. Plants, 11.","DOI":"10.3390\/plants11192611"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"107064","DOI":"10.1016\/j.asoc.2020.107064","article-title":"Probability granular distance-based fuzzy rough set model","volume":"102","author":"An","year":"2021","journal-title":"Appl. Soft Comput."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"452","DOI":"10.1016\/j.matcom.2020.12.023","article-title":"On three types of soft fuzzy coverings based rough sets","volume":"185","author":"Atef","year":"2021","journal-title":"Math. Comput. Simul."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"65","DOI":"10.4018\/IJSSCI.2021040104","article-title":"A Scalable Unsupervised Classification Method Using Rough Set for Remote Sensing Imagery","volume":"13","author":"Raj","year":"2021","journal-title":"Int. J. Softw. Sci. Comput. Intell."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"17745","DOI":"10.1007\/s11042-021-10571-2","article-title":"Robust fuzzy rough set based dimensionality reduction for big multimedia data hashing and unsupervised generative learning","volume":"80","author":"Khanzadi","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"e05748","DOI":"10.1016\/j.heliyon.2020.e05748","article-title":"Automated crack segmentation via saturation channel thresholding, area classification and fusion of modified level set segmentation with Canny edge detection","volume":"6","author":"Nnolim","year":"2020","journal-title":"Heliyon"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"106041","DOI":"10.1016\/j.compag.2021.106041","article-title":"Multispectral image based germination detection of potato by using supervised multiple threshold segmentation model and Canny edge detector","volume":"182","author":"Yang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_34","first-page":"130","article-title":"Image segmentation based on GGVF Snake model and Canny operator","volume":"3","author":"Zhang","year":"2021","journal-title":"Sci. J. Intell. Syst. Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"114766","DOI":"10.1016\/j.eswa.2021.114766","article-title":"Opposition-based Laplacian equilibrium optimizer with application in image segmentation using multilevel thresholding","volume":"174","author":"Dinkar","year":"2021","journal-title":"Expert Syst. Appl."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"101746","DOI":"10.1016\/j.artmed.2019.101746","article-title":"Using multi-layer perceptron with Laplacian edge detector for bladder cancer diagnosis","volume":"102","author":"Lorencin","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_37","first-page":"1","article-title":"Engineering. Prewitt Logistic Deep Recurrent Neural Learning for Face Log Detection by Extracting Features from Images","volume":"48","author":"Chinnappan","year":"2023","journal-title":"Arab. J. Sci. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11128-019-2376-5","article-title":"Quantum image edge extraction based on improved Prewitt operator","volume":"18","author":"Zhou","year":"2019","journal-title":"Quantum Inf. Process."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.biosystemseng.2022.02.011","article-title":"A segmentation algorithm incorporating superpixel block and holistically nested edge for sugarcane aphids images under natural light conditions","volume":"216","author":"Xu","year":"2022","journal-title":"Biosyst. Eng."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"e2965","DOI":"10.1002\/stc.2965","article-title":"Concrete crack segmentation based on convolution\u2013deconvolution feature fusion with holistically nested networks","volume":"29","author":"Xu","year":"2022","journal-title":"Struct. Control Health Monit."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1002\/mrm.27920","article-title":"Fully automated patellofemoral MRI segmentation using holistically nested networks: Implications for evaluating patellofemoral osteoarthritis, pain, injury, pathology, and adolescent development","volume":"83","author":"Cheng","year":"2020","journal-title":"Magn. Reason. Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.jocs.2013.10.003","article-title":"Sensitivity, specificity, and accuracy of predictive models on phenols toxicity","volume":"5","year":"2014","journal-title":"J. Comput. Sci."},{"key":"ref_43","first-page":"234","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Navab","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"104846","DOI":"10.1016\/j.compag.2019.06.001","article-title":"Fruit Detection for Strawberry Harvesting Robot in Non-Structural Environment Based on Mask-RCNN","volume":"163","author":"Yu","year":"2019","journal-title":"Comput. Electron. Agric."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2283\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:23:04Z","timestamp":1760106184000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/24\/7\/2283"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,3]]},"references-count":45,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2024,4]]}},"alternative-id":["s24072283"],"URL":"https:\/\/doi.org\/10.3390\/s24072283","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2024,4,3]]}}}