{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T20:49:18Z","timestamp":1772311758001,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2020,2,28]],"date-time":"2020-02-28T00:00:00Z","timestamp":1582848000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key Research and Development Plan of China","award":["2017YFD0701400"],"award-info":[{"award-number":["2017YFD0701400"]}]},{"name":"the National Key Research and Development Plan of China","award":["2016YFD0200700"],"award-info":[{"award-number":["2016YFD0200700"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>To minimize pesticide dosage and its adverse environmental impact, Unmanned Aerial Vehicle (UAV) spraying requires precise individual canopy information. Branches from neighboring trees may overlap, preventing image-based artificial intelligence analysis from correctly identifying individual trees. To solve this problem, this paper proposes a segmentation and evaluation method for mingled fruit tree canopies with irregular shapes. To extract the individual trees from mingled canopies, the study fitted the projection curve distribution of the interlacing tree with Gaussian Mixture Model (GMM) and solved the matter of segmentation by estimating the GMM parameters. For the intermingling degree assessment, the Gaussian parameters were used to quantify the characteristics of the mingled fruit trees and then as the input for Extreme Gradient Boosting (XGBoost) model training. The proposed method was tested on the aerial images of cherry and apple trees. Results of the experiments show that the proposed method can not only accurately identify individual trees, but also estimate the intermingledness of the interlacing canopies. The root mean squares (R) of the over-segmentation rate (Ro) and under-segmentation rate (Ru) for individual trees counting were less than 10%. Moreover, the Intersection over Union (IoU), used to evaluate the integrity of a single canopy area, was greater than 88%. An 84.3% Accuracy (ACC) with a standard deviation of 1.2% was achieved by the assessment model. This method will supply more accurate data of individual canopy for spray volume assessments or other precision-based applications in orchards.<\/jats:p>","DOI":"10.3390\/rs12050767","type":"journal-article","created":{"date-parts":[[2020,3,3]],"date-time":"2020-03-03T03:13:28Z","timestamp":1583205208000},"page":"767","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Interlacing Orchard Canopy Separation and Assessment using UAV Images"],"prefix":"10.3390","volume":"12","author":[{"given":"Zhenzhen","family":"Cheng","sequence":"first","affiliation":[{"name":"College of Engineering, China Agricultural University, No.17 Qing Hua Dong Lu, Haidian District, Beijing 100083, China"}]},{"given":"Lijun","family":"Qi","sequence":"additional","affiliation":[{"name":"College of Engineering, China Agricultural University, No.17 Qing Hua Dong Lu, Haidian District, Beijing 100083, China"}]},{"given":"Yifan","family":"Cheng","sequence":"additional","affiliation":[{"name":"Beijing Four Dimensions Tuxin Technology Co., Ltd, southeast Siweitu New Building, Intersection of Yongfeng Road and Beiqing Road, Beijing 100048, China"}]},{"given":"Yalei","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Engineering, China Agricultural University, No.17 Qing Hua Dong Lu, Haidian District, Beijing 100083, China"}]},{"given":"Hao","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Engineering, China Agricultural University, No.17 Qing Hua Dong Lu, Haidian District, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compag.2017.08.004","article-title":"Near ground platform development to simulate UAV aerial spraying and its spraying test under different conditions","volume":"148","author":"Zhang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.compag.2010.07.001","article-title":"Current status and future directions of precision aerial application for site-specific crop management in the USA","volume":"74","author":"Lan","year":"2010","journal-title":"Comput. Electron. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"502","DOI":"10.1016\/j.procs.2018.07.063","article-title":"Review on Application of Drone Systems in Precision Agriculture","volume":"133","author":"Mogili","year":"2018","journal-title":"Proced. Comput. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1016\/j.tplants.2018.11.007","article-title":"Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture","volume":"24","author":"Maes","year":"2019","journal-title":"Trends Plant Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.agrformet.2013.02.011","article-title":"Automatic image-based detection technology for two critical growth stages of maize: Emergence and three-leaf stage","volume":"174\u2013175","author":"Yu","year":"2013","journal-title":"Agric. For. Meteorol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"184","DOI":"10.1016\/j.rse.2018.04.002","article-title":"Individual tree crown detection in sub-meter satellite imagery using Marked Point Processes and a geometrical-optical model","volume":"211","author":"Gomes","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Li, W., Dong, R., Fu, H., and Yu, L. (2019). Large-Scale Oil Palm Tree Detection from High-Resolution Satellite Images Using Two-Stage Convolutional Neural Networks. Remote Sens., 11.","DOI":"10.3390\/rs11010011"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.isprsjprs.2018.11.001","article-title":"Estimating forest structural attributes using UAV-LiDAR data in Ginkgo plantations","volume":"146","author":"Liu","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.rse.2018.12.034","article-title":"Individual mangrove tree measurement using UAV-based LiDAR data: Possibilities and challenges","volume":"223","author":"Yin","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.eja.2015.11.026","article-title":"Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots?","volume":"74","author":"Rasmussen","year":"2016","journal-title":"Eur. J. Agron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2392","DOI":"10.1080\/01431161.2016.1264028","article-title":"Determining tree height and crown diameter from high-resolution UAV Determining tree height and crown diameter from high-resolution UAV imagery","volume":"38","author":"Panagiotidis","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1016\/j.ecolind.2018.08.011","article-title":"Detection of forest canopy gaps from very high resolution aerial images","volume":"95","author":"Nyamgeroh","year":"2018","journal-title":"Ecol. Indic."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Durfee, N., and Ochoa, C.G. (2019). The Use of Low-Altitude UAV Imagery to Assess Western Juniper Density and Canopy Cover in Treated and Untreated Stands The Use of Low-Altitude UAV Imagery to Assess Western Juniper Density and Canopy Cover in Treated and Untreated Stands. Forests, 10.","DOI":"10.3390\/f10040296"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.compag.2018.05.001","article-title":"Automatic citrus tree extraction from UAV images and digital surface models using circular Hough transform","volume":"150","author":"Selim","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.compag.2012.10.005","article-title":"Automated extraction of tree and plot-based parameters in citrus orchards from aerial images","volume":"90","author":"Recio","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.rse.2018.11.009","article-title":"Automated detection of individual clove trees for yield quantification in northeastern Madagascar based on multi-spectral satellite data","volume":"221","author":"Roth","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_17","unstructured":"Niu, H., Zhao, T., and Chen, Y. (August, January 29). Tree Canopy Differentiation Using Instance-aware Semantic Segmentation. Proceedings of the 2018 ASABE Annual International Meeting, Detroit, MI, USA."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.biosystemseng.2012.11.008","article-title":"Segmentation of touching insects based on optical flow and NCuts","volume":"114","author":"Yao","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.compag.2014.09.015","article-title":"A novel matching algorithm for splitting touching rice kernels based on contour curvature analysis","volume":"109","author":"Lin","year":"2014","journal-title":"Comput. Electron. Agric."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"302","DOI":"10.1016\/j.compag.2017.08.011","article-title":"Rice and wheat grain counting method and software development based on Android system","volume":"141","author":"Liu","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.compag.2017.05.027","article-title":"Application of UAV imaging platform for vegetation analysis based on spectral-spatial methods","volume":"140","author":"Senthilnath","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1016\/j.compag.2017.11.027","article-title":"A novel approach for vegetation classification using UAV-based hyperspectral imaging","volume":"144","author":"Ishida","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.rse.2012.05.027","article-title":"Multitemporal change detection of urban trees using localized region-based active contours in VHR images","volume":"124","author":"Ardila","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1016\/j.isprsjprs.2018.09.013","article-title":"Individual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images","volume":"145","author":"Wagner","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Bazi, Y., Malek, S., Alajlan, N., and Alhichri, H. (2014, January 13\u201318). An Automatic Approach for Palm Tree Counting in UAV Images. Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec, QC, Canada.","DOI":"10.1109\/IGARSS.2014.6946478"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.ifacol.2016.10.004","article-title":"Precision Forestry: Trees Counting in Urban Areas Using Visible Imagery based on an Unmanned Aerial Vehicle","volume":"49","author":"Hassaan","year":"2016","journal-title":"IFAC-PapersOnLine"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.agrformet.2018.07.028","article-title":"A half-Gaussian fitting method for estimating fractional vegetation cover of corn crops using unmanned aerial vehicle images","volume":"262","author":"Li","year":"2018","journal-title":"Agric. For. Meteorol."},{"key":"ref_28","unstructured":"Cheng, Z., Qi, L., Wu, Y., Cheng, Y., Yang, Z., and Gao, C. (2017). Parameter Optimization on Swing Variable Sprayer of Orchard Based on RSM. Nongye Jixie Xuebao\/Trans. Chin. Soc. Agric. Mach., 48."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"271","DOI":"10.13031\/2013.27839","article-title":"Shape Features for Identifying Young Weeds Using Image Analysis","volume":"38","author":"Woebbecke","year":"1995","journal-title":"Trans. ASAE"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1109\/TSMC.1979.4310076","article-title":"A Tlreshold Selection Method from Gray-Level Histograms","volume":"9","author":"Smith","year":"1979","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","article-title":"Maximum Likelihood from Incomplete Data via the EM Algorithm","volume":"39","author":"Dempster","year":"1977","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"920","DOI":"10.1016\/j.patrec.2010.01.016","article-title":"Segmentation of green vegetation of crop canopy images based on mean shift and Fisher linear discriminant","volume":"31","author":"Zheng","year":"2010","journal-title":"Pattern Recognit. Lett."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.compag.2010.09.013","article-title":"Automatic segmentation of relevant textures in agricultural images","volume":"75","author":"Guijarro","year":"2011","journal-title":"Comput. Electron. Agric."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.compag.2019.02.005","article-title":"A review on weed detection using ground-based machine vision and image processing techniques","volume":"158","author":"Wang","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.biosystemseng.2012.12.006","article-title":"Novel image processing approach for solving the overlapping problem in agriculture","volume":"115","author":"Pastrana","year":"2013","journal-title":"Biosyst. Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.compag.2015.11.008","article-title":"An automatic splitting method for the adhesive piglets\u2019 gray scale image based on the ellipse shape feature","volume":"120","author":"Lu","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Coy, A., Rankine, D., Taylor, M., Nielsen, D.C., and Cohen, J. (2016). Increasing the accuracy and automation of fractionalvegetation cover estimation from digital photographs. Remote Sens., 8.","DOI":"10.3390\/rs8070474"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ramachandran, K.M., and Tsokos, C.P.B.T. (2015). Chapter 6\u2014Hypothesis Testing. Mathematical Statistics with Applications in R, Academic Press. [2nd ed.].","DOI":"10.1016\/B978-0-12-417113-8.00006-0"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Balakrishnan, N., Voinov, V., and Nikulin, M.S.B.T. (2013). Chapter 2\u2014Pearson\u2019s Sum and Pearson-Fisher Test. Chi-Squared Goodness of Fit Tests with Applications, Academic Press.","DOI":"10.1016\/B978-0-12-397194-4.00002-8"},{"key":"ref_40","unstructured":"Gonzalez, R., and Faisal, Z. (2019). Digital Image Processing, Pearson Education. [2nd ed.]."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1111\/j.1654-1103.2011.01373.x","article-title":"A novel method for extracting green fractional vegetation cover from digital images","volume":"23","author":"Liu","year":"2012","journal-title":"J. Veg. Sci."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. (2016, January 13\u201317). XGBoost: A scalable tree boosting system. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1148\/radiology.143.1.7063747","article-title":"The Meaning and Use of the Area Under a Receiver Operating Characteristic (ROC) Curve","volume":"143","author":"Hanley","year":"1982","journal-title":"Radiology"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","article-title":"The Use of the Area Under the ROC Curve in the Evaluation of Machine Learning Algorithms","volume":"30","author":"Bradley","year":"1996","journal-title":"Pattern Recognit."},{"key":"ref_45","unstructured":"Meyer, F. (1990, January 8\u201313). Skeletons and watershed lines in digital spaces. Proceedings of the 34th Annual International Technical Symposium on Optical and Optoelectronic Applied Science and Engineering, San Diego, CA, USA."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., and Girshick, R. (2017, January 22\u201325). Mask R-CNN. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Honolulu, HI, USA.","DOI":"10.1109\/ICCV.2017.322"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Girshick, R. (2015, January 13\u201316). Fast R-CNN. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.169"},{"key":"ref_48","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_49","doi-asserted-by":"crossref","unstructured":"Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Doll\u00e1r, P., and Zitnick, C. (2014). Microsoft COCO: Common Objects in Context. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"He, X., Pan, J., Jin, O., Xu, T., Liu, B., Xu, T., Shi, Y., Atallah, A., Herbrich, R., and Bowers, S. (2014, January 24\u201327). Practical Lessons from Predicting Clicks on Ads at Facebook. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA.","DOI":"10.1145\/2648584.2648589"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.ins.2017.12.059","article-title":"A Pearson\u2019s correlation coefficient based decision tree and its parallel implementation","volume":"435","author":"Mu","year":"2017","journal-title":"Inf. Sci."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/5\/767\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:02:37Z","timestamp":1760173357000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/5\/767"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,28]]},"references-count":51,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["rs12050767"],"URL":"https:\/\/doi.org\/10.3390\/rs12050767","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,28]]}}}