{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T17:25:40Z","timestamp":1761845140041,"version":"build-2065373602"},"reference-count":36,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T00:00:00Z","timestamp":1584057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-throughput crop phenotyping is harnessing the potential of genomic resources for the genetic improvement of crop production under changing climate conditions. As global food security is not yet assured, crop phenotyping has received increased attention during the past decade. This spectral issue (SI) collects 30 papers reporting research on estimation of crop phenotyping traits using unmanned ground vehicle (UGV) and unmanned aerial vehicle (UAV) imagery. Such platforms were previously not widely available. The special issue includes papers presenting recent advances in the field, with 22 UAV-based papers and 12 UGV-based articles. The special issue covers 16 RGB sensor papers, 11 papers on multi-spectral imagery, and further 4 papers on hyperspectral and 3D data acquisition systems. A total of 13 plants\u2019 phenotyping traits, including morphological, structural, and biochemical traits are covered. Twenty different data processing and machine learning methods are presented. In this way, the special issue provides a good overview regarding potential applications of the platforms and sensors, to timely provide crop phenotyping traits in a cost-efficient and objective manner. With the fast development of sensors technology and image processing algorithms, we expect that the estimation of crop phenotyping traits supporting crop breeding scientists will gain even more attention in the future.<\/jats:p>","DOI":"10.3390\/rs12060940","type":"journal-article","created":{"date-parts":[[2020,3,18]],"date-time":"2020-03-18T08:13:27Z","timestamp":1584519207000},"page":"940","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Editorial for the Special Issue \u201cEstimation of Crop Phenotyping Traits using Unmanned Ground Vehicle and Unmanned Aerial Vehicle Imagery\u201d"],"prefix":"10.3390","volume":"12","author":[{"given":"Xiuliang","family":"Jin","sequence":"first","affiliation":[{"name":"Institute of Crop Sciences, Chinese Academy of Agricultural Sciences\/Key Laboratory of Crop Physiology and Ecology, Ministry of Agriculture, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9878-3274","authenticated-orcid":false,"given":"Zhenhai","family":"Li","sequence":"additional","affiliation":[{"name":"National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2169-8009","authenticated-orcid":false,"given":"Clement","family":"Atzberger","sequence":"additional","affiliation":[{"name":"Institute of Geomatics, University of Natural Resources and Life Sciences (BOKU), Peter Jordan Stra\u00dfe 82, Vienna 1190, Austria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1016\/j.pbi.2018.05.003","article-title":"Breeding to adapt agriculture to climate change: Affordable phenotyping solutions","volume":"45","author":"Araus","year":"2018","journal-title":"Curr. Opin. Plant Biol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.tplants.2015.10.015","article-title":"Machine Learning for High-Throughput Stress Phenotyping in Plants","volume":"21","author":"Singh","year":"2015","journal-title":"Trends Plant Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.tplants.2014.11.006","article-title":"Physiological phenotyping of plants for crop improvement","volume":"20","author":"Ghanem","year":"2015","journal-title":"Trends Plant Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.tplants.2013.09.008","article-title":"Field high-throughput phenotyping: The new crop breeding frontier","volume":"19","author":"Araus","year":"2014","journal-title":"Trends Plant Sci."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"451","DOI":"10.1016\/j.tplants.2018.02.001","article-title":"Translating High-Throughput Phenotyping into Genetic Gain","volume":"23","author":"Araus","year":"2018","journal-title":"Trends Plant Sci."},{"key":"ref_6","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_7","doi-asserted-by":"crossref","unstructured":"Liu, T., Li, R., Jin, X., Ding, J., Zhu, X., Sun, C., and Guo, W. (2017). Evaluation of Seed Emergence Uniformity of Mechanically Sown Wheat with UAV RGB Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9121241"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Patrick, A., and Li, C. (2017). High Throughput Phenotyping of Blueberry Bush Morphological Traits Using Unmanned Aerial Systems. Remote Sens., 9.","DOI":"10.3390\/rs9121250"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yao, X., Wang, N., Liu, Y., Cheng, T., Tian, Y., Chen, Q., and Zhu, Y. (2017). Estimation of Wheat LAI at Middle to High Levels Using Unmanned Aerial Vehicle Narrowband Multispectral Imagery. Remote Sens., 9.","DOI":"10.3390\/rs9121304"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yue, J., Feng, H., Yang, G., and Li, Z. (2018). A Comparison of Regression Techniques for Estimation of Above-Ground Winter Wheat Biomass Using Near-Surface Spectroscopy. Remote Sens., 10.","DOI":"10.3390\/rs10010066"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhou, C., Liang, D., Yang, X., Xu, B., and Yang, G. (2018). Recognition of Wheat Spike from Field Based Phenotype Platform Using Multi-Sensor Fusion and Improved Maximum Entropy Segmentation Algorithms. Remote Sens., 10.","DOI":"10.3390\/rs10020246"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Brocks, S., and Bareth, G. (2018). Estimating Barley Biomass with Crop Surface Models from Oblique RGB Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10020268"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Makanza, R., Zaman-Allah, M., Cairns, J., Magorokosho, C., Tarekegne, A., Olsen, M., and Prasanna, B. (2018). High-Throughput Phenotyping of Canopy Cover and Senescence in Maize Field Trials Using Aerial Digital Canopy Imaging. Remote Sens., 10.","DOI":"10.3390\/rs10020330"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gracia-Romero, A., Vergara-D\u00edaz, O., Thierfelder, C., Cairns, J., Kefauver, S., and Araus, J. (2018). Phenotyping Conservation Agriculture Management Effects on Ground and Aerial Remote Sensing Assessments of Maize Hybrids Performance in Zimbabwe. Remote Sens., 10.","DOI":"10.3390\/rs10020349"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Ma, X., Feng, J., Guan, H., and Liu, G. (2018). Prediction of Chlorophyll Content in Different Light Areas of Apple Tree Canopies based on the Color Characteristics of 3D Reconstruction. Remote Sens., 10.","DOI":"10.3390\/rs10030429"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Joalland, S., Screpanti, C., Varella, H., Reuther, M., Schwind, M., Lang, C., Walter, A., and Liebisch, F. (2018). Aerial and Ground Based Sensing of Tolerance to Beet Cyst Nematode in Sugar Beet. Remote Sens., 10.","DOI":"10.3390\/rs10050787"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Moeckel, T., Dayananda, S., Nidamanuri, R., Nautiyal, S., Hanumaiah, N., Buerkert, A., and Wachendorf, M. (2018). Estimation of Vegetable Crop Parameter by Multi-temporal UAV-Borne Images. Remote Sens., 10.","DOI":"10.3390\/rs10050805"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Hassan, M., Yang, M., Rasheed, A., Jin, X., Xia, X., Xiao, Y., and He, Z. (2018). Time-Series Multispectral Indices from Unmanned Aerial Vehicle Imagery Reveal Senescence Rate in Bread Wheat. Remote Sens., 10.","DOI":"10.3390\/rs10060809"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Johansen, K., Raharjo, T., and McCabe, M. (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_20","doi-asserted-by":"crossref","unstructured":"Khan, Z., Chopin, J., Cai, J., Eichi, V., Haefele, S., and Miklavcic, S. (2018). Quantitative Estimation of Wheat Phenotyping Traits Using Ground and Aerial Imagery. Remote Sens., 10.","DOI":"10.3390\/rs10060950"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Yue, J., Feng, H., Jin, X., Yuan, H., Li, Z., Zhou, C., Yang, G., and Tian, Q. (2018). A Comparison of Crop Parameters Estimation Using Images from UAV-Mounted Snapshot Hyperspectral Sensor and High-Definition Digital Camera. Remote Sens., 10.","DOI":"10.3390\/rs10071138"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Guan, H., Liu, M., Ma, X., and Yu, S. (2018). Three-Dimensional Reconstruction of Soybean Canopies Using Multisource Imaging for Phenotyping Analysis. Remote Sens., 10.","DOI":"10.3390\/rs10081206"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Wan, L., Li, Y., Cen, H., Zhu, J., Yin, W., Wu, W., Zhu, H., Sun, D., Zhou, W., and He, Y. (2018). Combining UAV-Based Vegetation Indices and Image Classification to Estimate Flower Number in Oilseed Rape. Remote Sens., 10.","DOI":"10.3390\/rs10091484"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Duarte-Carvajalino, J., Alzate, D., Ramirez, A., Santa-Sepulveda, J., Fajardo-Rojas, A., and Soto-Su\u00e1rez, M. (2018). Evaluating Late Blight Severity in Potato Crops Using Unmanned Aerial Vehicles and Machine Learning Algorithms. Remote Sens., 10.","DOI":"10.3390\/rs10101513"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Han, L., Yang, G., Feng, H., Zhou, C., Yang, H., Xu, B., Li, Z., and Yang, X. (2018). Quantitative Identification of Maize Lodging-Causing Feature Factors Using Unmanned Aerial Vehicle Images and a Nomogram Computation. Remote Sens., 10.","DOI":"10.3390\/rs10101528"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Thorp, K., Thompson, A., Harders, S., French, A., and Ward, R. (2018). High-Throughput Phenotyping of Crop Water Use Efficiency via Multispectral Drone Imagery and a Daily Soil Water Balance Model. Remote Sens., 10.","DOI":"10.3390\/rs10111682"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Michez, A., Bauwens, S., Brostaux, Y., Hiel, M., Garr\u00e9, S., Lejeune, P., and Dumont, B. (2018). How Far Can Consumer-Grade UAV RGB Imagery Describe Crop Production? A 3D and Multitemporal Modeling Approach Applied to Zea mays. Remote Sens., 10.","DOI":"10.3390\/rs10111798"},{"key":"ref_28","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_29","doi-asserted-by":"crossref","unstructured":"Ziliani, M., Parkes, S., Hoteit, I., and McCabe, M. (2018). Intra-Season Crop Height Variability at Commercial Farm Scales Using a Fixed-Wing UAV. Remote Sens., 10.","DOI":"10.3390\/rs10122007"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wen, W., Wu, S., Wang, C., Yu, Z., Guo, X., and Zhao, C. (2019). Maize Plant Phenotyping: Comparing 3D Laser Scanning, Multi-View Stereo Reconstruction, and 3D Digitizing Estimates. Remote Sens., 11.","DOI":"10.3390\/rs11010063"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Tu, Y., Johansen, K., Phinn, S., and Robson, A. (2019). Measuring Canopy Structure and Condition Using Multi-Spectral UAS Imagery in a Horticultural Environment. Remote Sens., 11.","DOI":"10.3390\/rs11030269"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Lobos, G., Escobar-Opazo, A., Estrada, F., Romero-Bravo, S., Garriga, M., del Pozo, A., Poblete-Echeverr\u00eda, C., Gonzalez-Talice, J., Gonz\u00e1lez-Martinez, L., and Caligari, P. (2019). Spectral Reflectance Modeling by Wavelength Selection: Studying the Scope for Blueberry Physiological Breeding under Contrasting Water Supply and Heat Conditions. Remote Sens., 11.","DOI":"10.3390\/rs11030329"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Wilke, N., Siegmann, B., Klingbeil, L., Burkart, A., Kraska, T., Muller, O., van Doorn, A., Heinemann, S., and Rascher, U. (2019). Quantifying Lodging Percentage and Lodging Severity Using a UAV-Based Canopy Height Model Combined with an Objective Threshold Approach. Remote Sens., 11.","DOI":"10.3390\/rs11050515"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Feng, L., Wu, W., Wang, J., Zhang, C., Zhao, Y., Zhu, S., and He, Y. (2019). Wind Field Distribution of Multi-rotor UAV and Its Influence on Spectral Information Acquisition of Rice Canopies. Remote Sens., 11.","DOI":"10.3390\/rs11060602"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Thompson, A., Thorp, K., Conley, M., Elshikha, D., French, A., Andrade-Sanchez, P., and Pauli, D. (2019). Comparing Nadir and Multi-Angle View Sensor Technologies for Measuring in-Field Plant Height of Upland Cotton. Remote Sens., 11.","DOI":"10.3390\/rs11060700"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ma, X., Zhu, K., Guan, H., Feng, J., Yu, S., and Liu, G. (2019). High-Throughput Phenotyping Analysis of Potted Soybean Plants Using Colorized Depth Images Based on A Proximal Platform. Remote Sens., 11.","DOI":"10.3390\/rs11091085"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/6\/940\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:06:49Z","timestamp":1760173609000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/6\/940"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,13]]},"references-count":36,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["rs12060940"],"URL":"https:\/\/doi.org\/10.3390\/rs12060940","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,3,13]]}}}