{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T03:18:11Z","timestamp":1770520691023,"version":"3.49.0"},"reference-count":68,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2022,11,27]],"date-time":"2022-11-27T00:00:00Z","timestamp":1669507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001872","name":"CDTI","doi-asserted-by":"publisher","award":["IDI-20200822"],"award-info":[{"award-number":["IDI-20200822"]}],"id":[{"id":"10.13039\/501100001872","id-type":"DOI","asserted-by":"publisher"}]},{"name":"FEADER funds","award":["IDI-20200822"],"award-info":[{"award-number":["IDI-20200822"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Interest in pistachios has increased in recent years due to their healthy nutritional profile and high profitability. In pistachio trees, as in other woody crops, the volume of the canopy is a key factor that affects the pistachio crop load, water requirements, and quality. However, canopy\/crown monitoring is time-consuming and labor-intensive, as it is traditionally carried out by measuring tree dimensions in the field. Therefore, methods for rapid tree canopy characterization are needed for providing accurate information that can be used for management decisions. The present study focuses on developing a new, fast, and low-cost technique, based on two main steps, for estimating the canopy volume in pistachio trees. The first step is based on adequately planning the UAV (unmanned aerial vehicle) flight according to light conditions and segmenting the RGB (Red, Green, Blue) imagery using machine learning methods. The second step is based on measuring vegetation planar area and ground shadows using two methodological approaches: a pixel-based classification approach and an OBIA (object-based image analysis) approach. The results show statistically significant linear relationships (p &lt; 0.05) between the ground-truth data and the estimated volume of pistachio tree crowns, with R2 &gt; 0.8 (pixel-based classification) and R2 &gt; 0.9 (OBIA). The proposed methodologies show potential benefits for accurately monitoring the vegetation of the trees. Moreover, the method is compatible with other remote sensing techniques, usually performed at solar noon, so UAV operators can plan a flexible working day. Further research is needed to verify whether these results can be extrapolated to other woody crops.<\/jats:p>","DOI":"10.3390\/rs14236006","type":"journal-article","created":{"date-parts":[[2022,11,28]],"date-time":"2022-11-28T07:01:30Z","timestamp":1669618890000},"page":"6006","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["A Novel Technique Using Planar Area and Ground Shadows Calculated from UAV RGB Imagery to Estimate Pistachio Tree (Pistacia vera L.) Canopy Volume"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9004-2877","authenticated-orcid":false,"given":"Sergio","family":"V\u00e9lez","sequence":"first","affiliation":[{"name":"Information Technology Group, Wageningen University & Research, 6708 PB Wageningen, The Netherlands"}]},{"given":"Rub\u00e9n","family":"Vacas","sequence":"additional","affiliation":[{"name":"Instituto Tecnol\u00f3gico Agrario de Castilla y Le\u00f3n (ITACyL), Unidad de Cultivos Le\u00f1osos y Hort\u00edcolas, 47071 Valladolid, Spain"}]},{"given":"Hugo","family":"Mart\u00edn","sequence":"additional","affiliation":[{"name":"Instituto Tecnol\u00f3gico Agrario de Castilla y Le\u00f3n (ITACyL), Unidad de Cultivos Le\u00f1osos y Hort\u00edcolas, 47071 Valladolid, Spain"}]},{"given":"David","family":"Ruano-Rosa","sequence":"additional","affiliation":[{"name":"Instituto Tecnol\u00f3gico Agrario de Castilla y Le\u00f3n (ITACyL), Unidad de Cultivos Le\u00f1osos y Hort\u00edcolas, 47071 Valladolid, Spain"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1094-8412","authenticated-orcid":false,"given":"Sara","family":"\u00c1lvarez","sequence":"additional","affiliation":[{"name":"Instituto Tecnol\u00f3gico Agrario de Castilla y Le\u00f3n (ITACyL), Unidad de Cultivos Le\u00f1osos y Hort\u00edcolas, 47071 Valladolid, Spain"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"S79","DOI":"10.1017\/S0007114514003250","article-title":"Nutrition Attributes and Health Effects of Pistachio Nuts","volume":"113","year":"2015","journal-title":"Br. J. Nutr."},{"key":"ref_2","unstructured":"Steduto, P., Hsiao, T.C., Fereres, E., and Raes, D. (2012). Crop Yield Response to Water, Food and Agriculture Organization of the United Nations. [1st ed.]."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Mandalari, G., Barreca, D., Gervasi, T., Roussell, M.A., Klein, B., Feeney, M.J., and Carughi, A. (2021). Pistachio Nuts (Pistacia vera L.): Production, Nutrients, Bioactives and Novel Health Effects. Plants, 11.","DOI":"10.3390\/plants11010018"},{"key":"ref_4","unstructured":"Ferguson, L., Polito, V., and Kallsen, C. (2005). The Pistachio Tree; Botany and Physiology and Factors That Affect Yield. Pistachio Production Manual, University of California."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"919","DOI":"10.1093\/oxfordjournals.aob.a084760","article-title":"Light Interception by an Isolated Plant A Simple Model","volume":"37","author":"Thornley","year":"1973","journal-title":"Ann. Bot. New Ser."},{"key":"ref_6","unstructured":"Allen, R.G., Pereira, L.S., Raes, D., and Smith, M. (1998). Crop Evapotranspiration: Guidelines for Computing Crop Water Requirements, Food and Agriculture Organization of the United Nations."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"461","DOI":"10.13031\/2013.31684","article-title":"Evaporation from Soil Influenced by Crop Shading, Crop Residue, and Wetting Regime","volume":"34","author":"Todd","year":"1991","journal-title":"Trans. ASAE"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"33","DOI":"10.1016\/S0378-3774(02)00112-9","article-title":"Actual Evapotranspiration and Crop Coefficients of Wheat (Triticum Aestivum) under Varying Moisture Levels of Humid Tropical Canal Command Area","volume":"59","author":"Bandyopadhyay","year":"2003","journal-title":"Agric. Water Manag."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Jia, Q., and Wang, Y.-P. (2021). Relationships between Leaf Area Index and Evapotranspiration and Crop Coefficient of Hilly Apple Orchard in the Loess Plateau. Water, 13.","DOI":"10.3390\/w13141957"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1007\/s00271-008-0124-1","article-title":"Water Use and the Development of Seasonal Crop Coefficients for Superior Seedless Grapevines Trained to an Open-Gable Trellis System","volume":"27","author":"Netzer","year":"2009","journal-title":"Irrig. Sci."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"109157","DOI":"10.1016\/j.scienta.2019.109157","article-title":"Evaluation of Early Vigor Traits in Wild Olive Germplasm","volume":"264","author":"Belaj","year":"2020","journal-title":"Sci. Hortic."},{"key":"ref_12","first-page":"18","article-title":"Assessing Tree Crown Volume\u2014A Review","volume":"94","author":"Zhu","year":"2021","journal-title":"For. Int. J. For. Res."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Balafoutis, A.T., Beck, B., Fountas, S., Tsiropoulos, Z., Vangeyte, J., G\u00f3mez-Barbero, M., and Pedersen, S.M. (2017). Smart Farming Technologies\u2013Description, Taxonomy and Economic Impact. Precision Agriculture: Technology and Economic Perspectives, Springer. Progress in Precision Agriculture.","DOI":"10.1007\/978-3-319-68715-5_2"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Pierce, F.J., and Clay, D. (2007). GIS Applications in Agriculture, CRC Press.","DOI":"10.1201\/9781420007718"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"828","DOI":"10.1126\/science.1183899","article-title":"Precision Agriculture and Food Security","volume":"327","author":"Gebbers","year":"2010","journal-title":"Science"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.foodchem.2018.11.140","article-title":"Precision Viticulture and Advanced Analytics. A Short Review","volume":"279","author":"Santesteban","year":"2019","journal-title":"Food Chem."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Muruganantham, P., Wibowo, S., Grandhi, S., Samrat, N.H., and Islam, N. (2022). A Systematic Literature Review on Crop Yield Prediction with Deep Learning and Remote Sensing. Remote Sens., 14.","DOI":"10.3390\/rs14091990"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Albetis, J., Duthoit, S., Guttler, F., Jacquin, A., Goulard, M., Poilv\u00e9, H., F\u00e9ret, J.-B., and Dedieu, G. (2017). Detection of Flavescence Dor\u00e9e Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9040308"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"P\u00e1dua, L., Marques, P., Ad\u00e3o, T., Guimar\u00e3es, N., Sousa, A., Peres, E., and Sousa, J.J. (2019). Vineyard Variability Analysis through UAV-Based Vigour Maps to Assess Climate Change Impacts. Agronomy, 9.","DOI":"10.3390\/agronomy9100581"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Barajas, E., \u00c1lvarez, S., Fern\u00e1ndez, E., V\u00e9lez, S., Rubio, J.A., and Mart\u00edn, H. (2020). Sentinel-2 Satellite Imagery for Agronomic and Quality Variability Assessment of Pistachio (Pistacia vera L.). Sustainability, 12.","DOI":"10.3390\/su12208437"},{"key":"ref_21","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_22","doi-asserted-by":"crossref","first-page":"105100","DOI":"10.1109\/ACCESS.2019.2932119","article-title":"Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications","volume":"7","author":"Kim","year":"2019","journal-title":"IEEE Access"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105731","DOI":"10.1016\/j.compag.2020.105731","article-title":"A Review on Plant High-Throughput Phenotyping Traits Using UAV-Based Sensors","volume":"178","author":"Xie","year":"2020","journal-title":"Comput. Electron. Agric."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Matese, A., and Di Gennaro, S. (2018). Practical Applications of a Multisensor UAV Platform Based on Multispectral, Thermal and RGB High Resolution Images in Precision Viticulture. Agriculture, 8.","DOI":"10.3390\/agriculture8070116"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Garc\u00eda-Fern\u00e1ndez, M., Sanz-Ablanedo, E., Pereira-Obaya, D., and Rodr\u00edguez-P\u00e9rez, J.R. (2021). Vineyard Pruning Weight Prediction Using 3D Point Clouds Generated from UAV Imagery and Structure from Motion Photogrammetry. Agronomy, 11.","DOI":"10.3390\/agronomy11122489"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pagliai, A., Ammoniaci, M., Sarri, D., Lisci, R., Perria, R., Vieri, M., D\u2019Arcangelo, M.E.M., Storchi, P., and Kartsiotis, S.-P. (2022). Comparison of Aerial and Ground 3D Point Clouds for Canopy Size Assessment in Precision Viticulture. Remote Sens., 14.","DOI":"10.3390\/rs14051145"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"7236","DOI":"10.1080\/01431161.2013.817715","article-title":"Estimation of Tree Crown Volume from Airborne Lidar Data Using Computational Geometry","volume":"34","author":"Korhonen","year":"2013","journal-title":"Int. J. Remote Sens."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1007\/s13595-011-0040-z","article-title":"Review of Ground-Based Methods to Measure the Distribution of Biomass in Forest Canopies","volume":"68","author":"Seidel","year":"2011","journal-title":"Ann. For. Sci."},{"key":"ref_29","unstructured":"Eltner, A., Hoffmeister, D., Kaiser, A., Karrasch, P., Klingbeil, L., St\u00f6cker, C., and Rovere, A. (2022). UAVs for the Environmental Sciences: Methods and Applications, WBG Academic."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"337","DOI":"10.1016\/j.rse.2003.12.013","article-title":"Hyperspectral Vegetation Indices and Novel Algorithms for Predicting Green LAI of Crop Canopies: Modeling and Validation in the Context of Precision Agriculture","volume":"90","author":"Haboudane","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Towers, P.C., Strever, A., and Poblete-Echeverr\u00eda, C. (2019). Comparison of Vegetation Indices for Leaf Area Index Estimation in Vertical Shoot Positioned Vine Canopies with and without Grenbiule Hail-Protection Netting. Remote Sens., 11.","DOI":"10.3390\/rs11091073"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"V\u00e9lez, S., Barajas, E., Rubio, J.A., Vacas, R., and Poblete-Echeverr\u00eda, C. (2020). Effect of Missing Vines on Total Leaf Area Determined by NDVI Calculated from Sentinel Satellite Data: Progressive Vine Removal Experiments. Appl. Sci., 10.","DOI":"10.3390\/app10103612"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Giovos, R., Tassopoulos, D., Kalivas, D., Lougkos, N., and Priovolou, A. (2021). Remote Sensing Vegetation Indices in Viticulture: A Critical Review. Agriculture, 11.","DOI":"10.3390\/agriculture11050457"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1111\/j.1755-0238.2008.00002.x","article-title":"Low-Resolution Remotely Sensed Images of Winegrape Vineyards Map Spatial Variability in Planimetric Canopy Area Instead of Leaf Area Index","volume":"14","author":"Hall","year":"2008","journal-title":"Aust. J. Grape Wine Res."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s00271-018-0613-9","article-title":"Assessment of Different Methods for Shadow Detection in High-Resolution Optical Imagery and Evaluation of Shadow Impact on Calculation of NDVI, and Evapotranspiration","volume":"37","author":"Aboutalebi","year":"2019","journal-title":"Irrig. Sci."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1013","DOI":"10.1080\/17538947.2018.1495770","article-title":"A Shadow- Eliminated Vegetation Index (SEVI) for Removal of Self and Cast Shadow Effects on Vegetation in Rugged Terrains","volume":"12","author":"Jiang","year":"2019","journal-title":"Int. J. Digit. Earth"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"159","DOI":"10.20870\/oeno-one.2021.55.4.4639","article-title":"Estimation of Leaf Area Index in Vineyards by Analysing Projected Shadows Using UAV Imagery","volume":"55","author":"Rubio","year":"2021","journal-title":"OENO One"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Delrot, S., Medrano, H., Or, E., Bavaresco, L., and Grando, S. (2010). Radiation Balance in Vineyards. Methodologies and Results in Grapevine Research, Springer.","DOI":"10.1007\/978-90-481-9283-0"},{"key":"ref_39","unstructured":"Ferguson, L., and Haviland, D.R. (2016). Pistachio Production Manual, University of California."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1093\/forestry\/42.2.133","article-title":"The Dependence of Volume Increment of Individual Trees on Dominance, Crown Dimensions, and Competition","volume":"42","author":"Hamilton","year":"1969","journal-title":"Forestry"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Caruso, G., Zarco-Tejada, P.J., Gonz\u00e1lez-Dugo, V., Moriondo, M., Tozzini, L., Palai, G., Rallo, G., Hornero, A., Primicerio, J., and Gucci, R. (2019). High-Resolution Imagery Acquired from an Unmanned Platform to Estimate Biophysical and Geometrical Parameters of Olive Trees under Different Irrigation Regimes. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0210804"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"109851","DOI":"10.1016\/j.scienta.2020.109851","article-title":"High-Throughput Analysis of the Canopy Traits in the Worldwide Olive Germplasm Bank of C\u00f3rdoba Using Very High-Resolution Imagery Acquired from Unmanned Aerial Vehicle (UAV)","volume":"278","author":"Belaj","year":"2021","journal-title":"Sci. Hortic."},{"key":"ref_43","unstructured":"Sesar Joint Undertaking (2017). European Drones Outlook Study: Unlocking the Value for Europe, Publications Office."},{"key":"ref_44","unstructured":"DJI Sciences and Technologies Ltd. (2022, October 07). Agricultural Drone Industry Insights Report (2021). Available online: https:\/\/www.dji.com\/newsroom\/news\/agricultural-drone-industry-insights-report-2021."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"V\u00e9lez, S., Vacas, R., Mart\u00edn, H., Ruano-Rosa, D., and \u00c1lvarez, S. (2022). High-Resolution UAV RGB Imagery Dataset for Precision Agriculture and 3D Photogrammetric Reconstruction Captured over a Pistachio Orchard (Pistacia vera L.) in Spain. Data, 7.","DOI":"10.3390\/data7110157"},{"key":"ref_46","unstructured":"Meeus, J. (1998). Astronomical Algorithms, Willmann-Bell. [2nd ed.]."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"5","DOI":"10.18637\/jss.v028.i05","article-title":"Building Predictive Models in R Using the Caret Package","volume":"28","author":"Kuhn","year":"2008","journal-title":"J. Stat. Softw."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.5194\/gmd-8-1991-2015","article-title":"System for Automated Geoscientific Analyses (SAGA) v. 2.1.4","volume":"8","author":"Conrad","year":"2015","journal-title":"Geosci. Model Dev."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Li, J. (2017). Assessing the Accuracy of Predictive Models for Numerical Data: Not r nor R2, Why Not? Then What?. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0183250"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1080\/14620316.1995.11515369","article-title":"Light Distribution in Apple Orchard Systems in Relation to Production and Fruit Quality","volume":"70","author":"Wagenmakers","year":"1995","journal-title":"J. Hortic. Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1173","DOI":"10.1007\/s13595-011-0067-1","article-title":"Comparison of Conventional Eight-Point Crown Projections with LIDAR-Based Virtual Crown Projections in a Temperate Old-Growth Forest","volume":"68","author":"Fleck","year":"2011","journal-title":"Ann. For. Sci."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1145\/235815.235821","article-title":"The Quickhull Algorithm for Convex Hulls","volume":"22","author":"Barber","year":"1996","journal-title":"ACM Trans. Math. Softw."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Yan, Z., Liu, R., Cheng, L., Zhou, X., Ruan, X., and Xiao, Y. (2019). A Concave Hull Methodology for Calculating the Crown Volume of Individual Trees Based on Vehicle-Borne LiDAR Data. Remote Sens., 11.","DOI":"10.3390\/rs11060623"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"3177","DOI":"10.1007\/s11042-014-2429-9","article-title":"Recognition and Localization of Occluded Apples Using K-Means Clustering Algorithm and Convex Hull Theory: A Comparison","volume":"75","author":"Wang","year":"2016","journal-title":"Multimed. Tools Appl."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Kleinn, C., and N\u00f6lke, N. (2020). Towards Tree Green Crown Volume: A Methodological Approach Using Terrestrial Laser Scanning. Remote Sens., 12.","DOI":"10.3390\/rs12111841"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1083","DOI":"10.1080\/01431161.2016.1265690","article-title":"Measurement and Calculation of Crown Projection Area and Crown Volume of Individual Trees Based on 3D Laser-Scanned Point-Cloud Data","volume":"38","author":"Lin","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_57","unstructured":"Raven, P.H., Evert, R.F., and Eichhorn, S.E. (2013). Biology of Plants, W.H. Freeman and Company Publishers. [8th ed.]."},{"key":"ref_58","unstructured":"Keller, M. (2015). The Science of Grapevines: Anatomy and Physiology, Elsevier. [2nd ed.]."},{"key":"ref_59","first-page":"99","article-title":"Lidar Remote Sensing for Forestry and Terrestrial Applications","volume":"74","author":"Faridhouseini","year":"2011","journal-title":"Int. J. Appl. Environ. Sci."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"881","DOI":"10.1007\/s11119-019-09699-x","article-title":"Leaf Area Index Evaluation in Vineyards Using 3D Point Clouds from UAV Imagery","volume":"21","author":"Comba","year":"2020","journal-title":"Precis. Agric."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Jurado, J.M., P\u00e1dua, L., Feito, F.R., and Sousa, J.J. (2020). Automatic Grapevine Trunk Detection on UAV-Based Point Cloud. Remote Sens., 12.","DOI":"10.3390\/rs12183043"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Jurado, J.M., Ortega, L., Cubillas, J.J., and Feito, F.R. (2020). Multispectral Mapping on 3D Models and Multi-Temporal Monitoring for Individual Characterization of Olive Trees. Remote Sens., 12.","DOI":"10.3390\/rs12071106"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ghanbari Parmehr, E., and Amati, M. (2021). Individual Tree Canopy Parameters Estimation Using UAV-Based Photogrammetric and LiDAR Point Clouds in an Urban Park. Remote Sens., 13.","DOI":"10.3390\/rs13112062"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Xie, X., Yang, W., Cao, G., Yang, J., Zhao, Z., Chen, S., Liao, Q., and Shi, G. (2018, January 13\u201316). Real-Time Vehicle Detection from UAV Imagery. Proceedings of the 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM), Xi\u2019an, China.","DOI":"10.1109\/BigMM.2018.8499466"},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Sheppard, C., and Rahnemoonfar, M. (2017, January 23\u201328). Real-Time Scene Understanding for UAV Imagery Based on Deep Convolutional Neural Networks. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127435"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"106226","DOI":"10.1016\/j.compag.2021.106226","article-title":"Complementary Chemometrics and Deep Learning for Semantic Segmentation of Tall and Wide Visible and Near-Infrared Spectral Images of Plants","volume":"186","author":"Mishra","year":"2021","journal-title":"Comput. Electron. Agric."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Chen, C.J., and Zhang, Z. (2020). GRID: A Python Package for Field Plot Phenotyping Using Aerial Images. Remote Sens., 12.","DOI":"10.3390\/rs12111697"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1002\/ppj2.20042","article-title":"Segmentation of Vegetation and Microplots in Aerial Agriculture Images: A Survey","volume":"5","author":"Mardanisamani","year":"2022","journal-title":"Plant Phenome J."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/6006\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:27:39Z","timestamp":1760146059000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/23\/6006"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,27]]},"references-count":68,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14236006"],"URL":"https:\/\/doi.org\/10.3390\/rs14236006","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,27]]}}}