{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T19:15:03Z","timestamp":1770491703287,"version":"3.49.0"},"reference-count":31,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T00:00:00Z","timestamp":1590364800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R&amp;D Program of China","award":["2017YFD0701400"],"award-info":[{"award-number":["2017YFD0701400"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To make canopy information measurements in modern standardized apple orchards, a method for canopy information measurements based on unmanned aerial vehicle (UAV) multimodal information is proposed. Using a modern standardized apple orchard as the study object, a visual imaging system on a quadrotor UAV was used to collect canopy images in the apple orchard, and three-dimensional (3D) point-cloud models and vegetation index images of the orchard were generated with Pix4Dmapper software. A row and column detection method based on grayscale projection in orchard index images (RCGP) is proposed. Morphological information measurements of fruit tree canopies based on 3D point-cloud models are established, and a yield prediction model for fruit trees based on the UAV multimodal information is derived. The results are as follows: (1) When the ground sampling distance (GSD) was 2.13\u20136.69 cm\/px, the accuracy of row detection in the orchard using the RCGP method was 100.00%. (2) With RCGP, the average accuracy of column detection based on grayscale images of the normalized green (NG) index was 98.71\u2013100.00%. The hand-measured values of H, SXOY, and V of the fruit tree canopy were compared with those obtained with the UAV. The results showed that the coefficient of determination R2 was the most significant, which was 0.94, 0.94, and 0.91, respectively, and the relative average deviation (RADavg) was minimal, which was 1.72%, 4.33%, and 7.90%, respectively, when the GSD was 2.13 cm\/px. Yield prediction was modeled by the back-propagation artificial neural network prediction model using the color and textural characteristic values of fruit tree vegetation indices and the morphological characteristic values of point-cloud models. The R2 value between the predicted yield values and the measured values was 0.83\u20130.88, and the RAD value was 8.05\u20139.76%. These results show that the UAV-based canopy information measurement method in apple orchards proposed in this study can be applied to the remote evaluation of canopy 3D morphological information and can yield information about modern standardized orchards, thereby improving the level of orchard informatization. This method is thus valuable for the production management of modern standardized orchards.<\/jats:p>","DOI":"10.3390\/s20102985","type":"journal-article","created":{"date-parts":[[2020,5,25]],"date-time":"2020-05-25T11:42:02Z","timestamp":1590406922000},"page":"2985","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["A Canopy Information Measurement Method for Modern Standardized Apple Orchards Based on UAV Multimodal Information"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7470-5255","authenticated-orcid":false,"given":"Guoxiang","family":"Sun","sequence":"first","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"},{"name":"Jiangsu Province Engineering Lab for Modern Intelligent Facilities of Agriculture Technology &amp; Equipment, Nanjing 210031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaochan","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"},{"name":"Jiangsu Province Engineering Lab for Modern Intelligent Facilities of Agriculture Technology &amp; Equipment, Nanjing 210031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haihui","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianjie","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Engineering, Nanjing Agricultural University, Nanjing 210031, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,5,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.compag.2016.09.014","article-title":"Mapping almond orchard canopy volume, flowers, fruit and yield using lidar and vision sensors","volume":"130","author":"Underwood","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1186\/s13007-017-0205-3","article-title":"Quantifying pruning impacts on olive tree architecture and annual canopy growth by using UAV-based 3D modelling","volume":"13","author":"Serrano","year":"2017","journal-title":"Plant Methods"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Johansen, K., Raharjo, T., and McCabe, M.F. (2018). Using multi-spectral UAV imagery to extract tree crop structural properties and assess pruning effects. Remote Sens., 10.","DOI":"10.20944\/preprints201804.0198.v1"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1016\/j.biosystemseng.2012.09.014","article-title":"Comparison of two different measurement techniques for automated determination of plum tree canopy cover","volume":"113","author":"Pforte","year":"2012","journal-title":"Biosyst. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Cheng, Z., Qi, L., Cheng, Y., Wu, Y., and Zhang, H. (2020). Interlacing orchard canopy separation and assessment using UAV images. Remote Sens., 12.","DOI":"10.3390\/rs12050767"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1016\/j.compag.2018.06.040","article-title":"Machine vision assessment of mango orchard flowering","volume":"151","author":"Wang","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"124","DOI":"10.1016\/j.compag.2011.09.007","article-title":"A review of methods and applications of the geometric characterization of tree crops in agricultural activities","volume":"81","author":"Rosell","year":"2012","journal-title":"Comput. Electron. Agric."},{"key":"ref_8","first-page":"175","article-title":"Apple tree canopy geometric parameters acquirement based on 3D point clouds","volume":"33","author":"Guo","year":"2017","journal-title":"Trans. Chin. Soc. Agric. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Aasen, H., Honkavaara, E., Lucieer, A., and Zarco-Tejada, P. (2018). Quantitative remote sensing at ultra-high resolution with UAV spectroscopy: A review of sensor technology, measurement procedures, and data correction workflows. Remote Sens., 10.","DOI":"10.3390\/rs10071091"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.compag.2017.07.009","article-title":"Multi-modal sensor system for plant water stress assessment","volume":"141","author":"Kim","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_11","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_12","first-page":"155","article-title":"Development of real-time laser-scanning system to detect tree canopy characteristics for variable-rate pesticide application","volume":"10","author":"Jichen","year":"2017","journal-title":"Int. J. Agric. Biol. Eng."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/j.biosystemseng.2016.01.007","article-title":"Detection of red and bicoloured apples on tree with an RGB-D camera","volume":"146","author":"Nguyen","year":"2016","journal-title":"Biosyst. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1016\/j.eja.2014.01.004","article-title":"Tree height quantification using very high resolution imagery acquired from an unmanned aerial vehicle (UAV) and automatic 3D photo-reconstruction methods","volume":"55","author":"Angileri","year":"2014","journal-title":"Eur. J. Agron."},{"key":"ref_15","first-page":"79","article-title":"Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley","volume":"39","author":"Bendig","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.compag.2012.12.002","article-title":"Comparison of two aerial imaging platforms for identification of Huanglongbing-infected citrus trees","volume":"91","author":"Sankaran","year":"2013","journal-title":"Comput. Electron. Agric."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.rse.2009.09.006","article-title":"Detecting water stress effects on fruit quality in orchards with time-series PRI airborne imagery","volume":"114","author":"Berni","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_18","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_19","doi-asserted-by":"crossref","unstructured":"Yeom, J., Jung, J., Chang, A., Maeda, M., and Landivar, J. (2018). Automated open cotton boll detection for yield estimation using unmanned aircraft vehicle (UAV) Data. Remote Sens., 10.","DOI":"10.3390\/rs10121895"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1186\/s13007-018-0338-z","article-title":"Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis","volume":"14","author":"Gong","year":"2018","journal-title":"Plant Methods"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1016\/j.compag.2018.10.005","article-title":"Unsupervised detection of vineyards by 3D point-cloud UAV photogrammetry for precision agriculture","volume":"155","author":"Comba","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Park, S., Ryu, D., Fuentes, S., Chung, H., Hern\u00e1ndez-Montes, E., and O\u2019Connell, M. (2017). Adaptive estimation of crop water stress in nectarine and peach orchards using high-resolution imagery from an unmanned aerial vehicle (UAV). Remote Sens., 9.","DOI":"10.3390\/rs9080828"},{"key":"ref_23","first-page":"31","article-title":"Multitemporal field-based plant height estimation using 3D point clouds generated from small unmanned aerial systems high-resolution imagery","volume":"64","author":"Malambo","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Khan, Z., Chopin, J., Cai, J., Eichi, V.R., Haefele, S., and Miklavcic, S.J. (2018). Quantitative estimation of wheat phenotyping traits using ground and aerial imagery. Remote Sens., 10.","DOI":"10.3390\/rs10060950"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"170006","DOI":"10.2135\/tppj2017.08.0006","article-title":"Temporal estimates of crop growth in sorghum and maize breeding enabled by unmanned aerial systems","volume":"1","author":"Pugh","year":"2018","journal-title":"Plant Phenome J."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.biosystemseng.2015.01.008","article-title":"Multi-temporal imaging using an unmanned aerial vehicle for monitoring a sunflower crop","volume":"132","author":"Vega","year":"2015","journal-title":"Biosyst. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"De Castro, A., Torres-S\u00e1nchez, J., Pe\u00f1a-Barrag\u00e1n, J.M., Jim\u00e9nez-Brenes, F., Csillik, O., and L\u00f3pez-Granados, F. (2018). An automatic random Forest-OBIA algorithm for early weed mapping between and within crop rows using UAV imagery. Remote Sens., 10.","DOI":"10.3390\/rs10020285"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Yang, H., Wang, X., and Sun, G. (2019). Three-dimensional morphological measurement method for a fruit tree canopy based on kinect sensor self-calibration. Agronomy, 9.","DOI":"10.3390\/agronomy9110741"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Han, X., Thomasson, J.A., Bagnall, G.C., Pugh, N.A., Horne, D.W., Rooney, W.L., Jung, J., Chang, A., Malambo, L., and Popescu, S.C. (2018). Measurement and calibration of plant-height from fixed-wing UAV images. Sensors, 18.","DOI":"10.3390\/s18124092"},{"key":"ref_30","first-page":"381","article-title":"Similarity of color images","volume":"2420","author":"Stricker","year":"1970","journal-title":"Proc. SPIE Int. Soc. Opt. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural features for image classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man Cybern."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2985\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:32:09Z","timestamp":1760175129000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/10\/2985"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,5,25]]},"references-count":31,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2020,5]]}},"alternative-id":["s20102985"],"URL":"https:\/\/doi.org\/10.3390\/s20102985","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,5,25]]}}}