{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T15:47:33Z","timestamp":1725378453008},"reference-count":56,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,23]],"date-time":"2021-03-23T00:00:00Z","timestamp":1616457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011710","name":"Shaanxi Provincial Science and Technology Department","doi-asserted-by":"publisher","award":["2020zdzx03-04-01"],"id":[{"id":"10.13039\/501100011710","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61971005"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. The rapid and accurate identification of apple targets in an illuminated and unstructured natural orchard is still a key challenge for the picking robot\u2019s vision system. In this paper, by combining local image features and color information, we propose a pixel patch segmentation method based on gray-centered red\u2013green\u2013blue (RGB) color space to address this issue. Different from the existing methods, this method presents a novel color feature selection method that accounts for the influence of illumination and shadow in apple images. By exploring both color features and local variation in apple images, the proposed method could effectively distinguish the apple fruit pixels from other pixels. Compared with the classical segmentation methods and conventional clustering algorithms as well as the popular deep-learning segmentation algorithms, the proposed method can segment apple images more accurately and effectively. The proposed method was tested on 180 apple images. It offered an average accuracy rate of 99.26%, recall rate of 98.69%, false positive rate of 0.06%, and false negative rate of 1.44%. Experimental results demonstrate the outstanding performance of the proposed method.<\/jats:p>","DOI":"10.3390\/rs13061211","type":"journal-article","created":{"date-parts":[[2021,3,24]],"date-time":"2021-03-24T03:59:41Z","timestamp":1616558381000},"page":"1211","source":"Crossref","is-referenced-by-count":29,"title":["A Method of Segmenting Apples Based on Gray-Centered RGB Color Space"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-9104-0598","authenticated-orcid":false,"given":"Pan","family":"Fan","sequence":"first","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China"},{"name":"School of Computer Science and Technology, Baoji University of Arts and Science, Baoji 721016, China"},{"name":"Shannxi Key Laboratory of Apple, Yangling 712100, China"}]},{"given":"Guodong","family":"Lang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-2233-0065","authenticated-orcid":false,"given":"Bin","family":"Yan","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China"},{"name":"Shannxi Key Laboratory of Apple, Yangling 712100, China"}]},{"given":"Xiaoyan","family":"Lei","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China"},{"name":"Shannxi Key Laboratory of Apple, Yangling 712100, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-8111-3757","authenticated-orcid":false,"given":"Pengju","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China"},{"name":"Apple Mechanized Research Base, Yangling 712100, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-1699-3468","authenticated-orcid":false,"given":"Zhijie","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China"},{"name":"Apple Mechanized Research Base, Yangling 712100, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0194-0032","authenticated-orcid":false,"given":"Fuzeng","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling 712100, China"},{"name":"Apple Mechanized Research Base, Yangling 712100, China"},{"name":"State Key Laboratory of Soil Erosion and Dryland Farming on Loess Plateau, Yangling 712100, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"105946","DOI":"10.1016\/j.compag.2020.105946","article-title":"Computer vision-based high-quality tea automatic plucking robot using Delta parallel manipulator","volume":"181","author":"Yang","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"105932","DOI":"10.1016\/j.compag.2020.105932","article-title":"Detection of typical obstacles in orchards based on deep convolutional neural network","volume":"181","author":"Li","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1007\/s10514-020-09915-y","article-title":"High precision control and deep learning-based corn stand counting algorithms for agricultural robot","volume":"44","author":"Zhang","year":"2020","journal-title":"Auton. Robot."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1027","DOI":"10.1002\/rob.21937","article-title":"Development of a sweet pepper harvesting robot","volume":"37","author":"Arad","year":"2020","journal-title":"J. Field Robot."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"202","DOI":"10.1002\/rob.21889","article-title":"An autonomous strawberry-harvesting robot: Design, development, integration, and field evaluation","volume":"37","author":"Xiong","year":"2020","journal-title":"J. Field Robot."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1016\/j.biosystemseng.2019.09.006","article-title":"Recognition of green apples in an orchard environment by combining the GrabCut model and Ncut algorithm","volume":"187","author":"Sun","year":"2019","journal-title":"Biosyst. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"105475","DOI":"10.1016\/j.compag.2020.105475","article-title":"Using color and 3D geometry features to segment fruit point cloud and improve fruit recognition accuracy","volume":"174","author":"Wu","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.biosystemseng.2014.10.003","article-title":"Evaluation and stability comparison of different vehicle configurations for robotic agricultural operations on side-slopes","volume":"129","author":"Vidoni","year":"2015","journal-title":"Biosyst. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.biosystemseng.2011.07.005","article-title":"Design and control of an apple harvesting robot","volume":"110","author":"Jidong","year":"2011","journal-title":"Biosyst. Eng. Biosyst. Eng."},{"key":"ref_10","first-page":"203","article-title":"Fruit detection system and an end effector for robotic harvesting of Fuji apples","volume":"12","author":"Bulanon","year":"2010","journal-title":"Agric. Eng. Int. CIGR J."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105254","DOI":"10.1016\/j.compag.2020.105254","article-title":"Automatic cucumber recognition algorithm for harvesting robots in the natural environment using deep learning and multi-feature fusion","volume":"170","author":"Mao","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1186","DOI":"10.1016\/j.compeleceng.2011.11.005","article-title":"Automatic recognition vision system guided for apple harvesting robot","volume":"38","author":"Ji","year":"2012","journal-title":"Comput. Electr. Eng."},{"key":"ref_13","first-page":"65","article-title":"Fast tracing recognition method of target fruit for apple harvesting robot","volume":"45","author":"Zhao","year":"2014","journal-title":"Nongye Jixie Xuebao\/Trans. Chin. Soc. Agric. Mach."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.compag.2016.01.023","article-title":"A method of segmenting apples at night based on color and position information","volume":"122","author":"Liu","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"411","DOI":"10.1016\/j.scienta.2018.11.030","article-title":"A segmentation method of bagged green apple image","volume":"246","author":"Lv","year":"2019","journal-title":"Sci. Hortic."},{"key":"ref_16","first-page":"22","article-title":"Fruits Segmentation Method Based on Superpixel Features for Apple Harvesting Robot","volume":"50","author":"Xiaoyang","year":"2019","journal-title":"Trans. Chin. Soc. Agric. Mach."},{"key":"ref_17","first-page":"26","article-title":"Apple recognition method based on illumination invariant graph","volume":"26","author":"Tu","year":"2010","journal-title":"Nongye Gongcheng Xuebao\/Trans. Chin. Soc. Agric. Eng."},{"key":"ref_18","first-page":"107","article-title":"Apple Recognition in Natural Tree Canopy based on Fuzzy 2-partition Entropy","volume":"7","author":"Huang","year":"2013","journal-title":"Int. J. Digit. Content Technol. Appl."},{"key":"ref_19","first-page":"168","article-title":"Shadow removal method of apples based on illumination invariant image","volume":"30","author":"Song","year":"2014","journal-title":"Nongye Gongcheng Xuebao\/Trans. Chin. Soc. Agric. Eng."},{"key":"ref_20","first-page":"135","article-title":"Shadow removal method of apples based on fuzzy set theory","volume":"30","author":"Song","year":"2014","journal-title":"Nongye Gongcheng Xuebao\/Trans. Chin. Soc. Agric. Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1729881419897473","DOI":"10.1177\/1729881419897473","article-title":"Fruit recognition based on pulse coupled neural network and genetic Elman algorithm application in apple harvesting robot","volume":"17","author":"Jia","year":"2020","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.biosystemseng.2019.06.016","article-title":"Shadow detection and removal in apple image segmentation under natural light conditions using an ultrametric contour map","volume":"184","author":"Xu","year":"2019","journal-title":"Biosyst. Eng."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/j.neucom.2018.07.015","article-title":"Mutually Exclusive-KSVD: Learning a Discriminative Dictionary for Hyperspectral Image Classification","volume":"315","author":"Xie","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2016\/3289801","article-title":"Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification","volume":"2016","author":"Sladojevic","year":"2016","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","article-title":"Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks","volume":"39","author":"Ren","year":"2015","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","first-page":"474","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"79","author":"Long","year":"2014","journal-title":"Arxiv"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Kang, H., Zhou, H., Wang, X., and Chen, C. (2020). Real-Time Fruit Recognition and Grasping Estimation for Robotic Apple Harvesting. Sensors, 20.","DOI":"10.3390\/s20195670"},{"key":"ref_28","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_29","first-page":"277","article-title":"Color-Dependent Diffusion Equations Based on Quaternion Algebra","volume":"21","author":"Li","year":"2012","journal-title":"Chin. J. Electron."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3513","DOI":"10.1002\/mma.6135","article-title":"Algebraic techniques for least squares problems in commutative quaternionic theory","volume":"43","author":"Zhang","year":"2020","journal-title":"Math. Methods Appl. Sci."},{"key":"ref_31","first-page":"1","article-title":"Yang\u2013Mills-like field theories built on division quaternion and octonion algebras","volume":"135","author":"Chanyal","year":"2020","journal-title":"Eur. Phys. J. Plus"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Zhang, X., Xia, J., Tan, X., Zhou, X., and Wang, T. (2019). PolSAR Image Classification via Learned Superpixels and QCNN Integrating Color Features. Remote Sens., 11.","DOI":"10.3390\/rs11151831"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1002\/nla.2245","article-title":"Robust quaternion matrix completion with applications to image inpainting","volume":"26","author":"Jia","year":"2019","journal-title":"Numer. Linear Algebra Appl."},{"key":"ref_34","unstructured":"Evans, C., Sangwine, S., and Ell, T. (2000, January 10\u201313). Hypercomplex color-sensitive smoothing filters. Proceedings of the 2000 International Conference on Image Processing (Cat. No.00CH37101), Vancouver, BC, Canada."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/TIP.2006.884955","article-title":"Hypercomplex Fourier Transforms of Color Images","volume":"16","author":"Ell","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.cviu.2006.11.014","article-title":"Quaternion color texture segmentation","volume":"107","author":"Shi","year":"2007","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_37","unstructured":"Le Cam, L., and Neyman, J. (1967). Some Methods for Classification and Analysis of MultiVariate Observations, University of California Press. [1st ed.]."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11633-020-1221-8","article-title":"Automatic \u201cGround Truth\u201d Annotation and Industrial Workpiece Dataset Generation for Deep Learning","volume":"17","author":"Liu","year":"2020","journal-title":"Int. J. Autom. Comput."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Li, X., Chang, D., Ma, Z., Tan, Z.-H., Xue, J.-H., Cao, J., and Guo, J. (2020). Deep InterBoost networks for small-sample image classification. Neurocomputing.","DOI":"10.1016\/j.neucom.2020.06.135"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Vahidi, H., Klinkenberg, B., Johnson, B.A., Moskal, L.M., and Yan, W. (2018). Mapping the Individual Trees in Urban Orchards by Incorporating Volunteered Geographic Information and Very High Resolution Optical Remotely Sensed Data: A Template Matching-Based Approach. Remote Sens., 10.","DOI":"10.3390\/rs10071134"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1109\/TFUZZ.2018.2889018","article-title":"Superpixel-Based Fast Fuzzy C-Means Clustering for Color Image Segmentation","volume":"27","author":"Lei","year":"2019","journal-title":"IEEE Trans. Fuzzy Syst."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201329). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.neunet.2020.05.017","article-title":"Real-time multiple spatiotemporal action localization and prediction approach using deep learning","volume":"128","author":"Hammam","year":"2020","journal-title":"Neural Netw."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"105380","DOI":"10.1016\/j.compag.2020.105380","article-title":"Detection and segmentation of overlapped fruits based on optimized mask R-CNN application in apple harvesting robot","volume":"172","author":"Jia","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13640-020-0496-6","article-title":"A scale-adaptive object-tracking algorithm with occlusion detection","volume":"2020","author":"Yuan","year":"2020","journal-title":"EURASIP J. Image Video Process."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Yamaguchi, T., Tanaka, Y., Imachi, Y., Yamashita, M., and Katsura, K. (2021). Feasibility of Combining Deep Learning and RGB Images Obtained by Unmanned Aerial Vehicle for Leaf Area Index Estimation in Rice. Remote Sens., 13.","DOI":"10.3390\/rs13010084"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.procir.2020.01.135","article-title":"Deep Learning for Automated Product Design","volume":"91","author":"Krahe","year":"2020","journal-title":"Procedia CIRP"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13638-020-01697-2","article-title":"Learning deep networks with crowdsourcing for relevance evaluation","volume":"2020","author":"Wu","year":"2020","journal-title":"EURASIP J. Wirel. Commun. Netw."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"100297","DOI":"10.1016\/j.imu.2020.100297","article-title":"Deep learning approaches to biomedical image segmentation","volume":"18","author":"Neubert","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.comcom.2020.01.016","article-title":"Deep learning and big data technologies for IoT security","volume":"151","author":"Amanullah","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_51","first-page":"2093","article-title":"Study on image processing using deep learning techniques","volume":"44","author":"Karanam","year":"2020","journal-title":"Mater. Today Proc."},{"key":"ref_52","unstructured":"Arora, S., Du, S., Hu, W., Li, Z., and Wang, R. (2020, July 21). Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks 2019. Available online: https:\/\/ui.adsabs.harvard.edu\/abs\/2019arXiv190108584A."},{"key":"ref_53","unstructured":"Du, S., Zhai, X., Poczos, B., and Singh, A. (2020, July 05). Gradient Descent Provably Optimizes Over-parameterized Neural Networks 2018. Available online: https:\/\/ui.adsabs.harvard.edu\/abs\/2018arXiv181002054D."},{"key":"ref_54","unstructured":"Neyshabur, B., Li, Z., Bhojanapalli, S., LeCun, Y., and Srebro, N. (2020, September 13). Towards Understanding the Role of Over-Parametrization in Generalization of Neural Networks 2018. Available online: https:\/\/ui.adsabs.harvard.edu\/abs\/2018arXiv180512076N."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"105201","DOI":"10.1016\/j.compag.2019.105201","article-title":"Robust index-based semantic plant\/background segmentation for RGB- images","volume":"169","author":"Riehle","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Karaba\u011f, C., Verhoeven, J., Miller, N., and Reyes-Aldasoro, C. (2019). Texture Segmentation: An Objective Comparison between Traditional and Deep-Learning Methodologies, University of London.","DOI":"10.20944\/preprints201908.0001.v1"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/6\/1211\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,7,10]],"date-time":"2024-07-10T01:41:22Z","timestamp":1720575682000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/6\/1211"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,23]]},"references-count":56,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["rs13061211"],"URL":"https:\/\/doi.org\/10.3390\/rs13061211","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,23]]}}}