{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T19:02:18Z","timestamp":1775070138375,"version":"3.50.1"},"reference-count":52,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T00:00:00Z","timestamp":1600041600000},"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>Assessing plant population of cotton is important to make replanting decisions in low plant density areas, prone to yielding penalties. Since the measurement of plant population in the field is labor intensive and subject to error, in this study, a new approach of image-based plant counting is proposed, using unmanned aircraft systems (UAS; DJI Mavic 2 Pro, Shenzhen, China) data. The previously developed image-based techniques required a priori information of geometry or statistical characteristics of plant canopy features, while also limiting the versatility of the methods in variable field conditions. In this regard, a deep learning-based plant counting algorithm was proposed to reduce the number of input variables, and to remove requirements for acquiring geometric or statistical information. The object detection model named You Only Look Once version 3 (YOLOv3) and photogrammetry were utilized to separate, locate, and count cotton plants in the seedling stage. The proposed algorithm was tested with four different UAS datasets, containing variability in plant size, overall illumination, and background brightness. Root mean square error (RMSE) and R2 values of the optimal plant count results ranged from 0.50 to 0.60 plants per linear meter of row (number of plants within 1 m distance along the planting row direction) and 0.96 to 0.97, respectively. The object detection algorithm, trained with variable plant size, ground wetness, and lighting conditions generally resulted in a lower detection error, unless an observable difference of developmental stages of cotton existed. The proposed plant counting algorithm performed well with 0\u201314 plants per linear meter of row, when cotton plants are generally separable in the seedling stage. This study is expected to provide an automated methodology for in situ evaluation of plant emergence using UAS data.<\/jats:p>","DOI":"10.3390\/rs12182981","type":"journal-article","created":{"date-parts":[[2020,9,14]],"date-time":"2020-09-14T09:04:53Z","timestamp":1600074293000},"page":"2981","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":76,"title":["Plant Counting of Cotton from UAS Imagery Using Deep Learning-Based Object Detection Framework"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2337-9693","authenticated-orcid":false,"given":"Sungchan","family":"Oh","sequence":"first","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, USA"}]},{"given":"Anjin","family":"Chang","sequence":"additional","affiliation":[{"name":"School of Engineering &amp; Computing Sciences, Texas A&amp;M University\u2014Corpus Christi, 6300 Ocean Drive, Corpus Christi, TX 78412, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4050-0301","authenticated-orcid":false,"given":"Akash","family":"Ashapure","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1176-3540","authenticated-orcid":false,"given":"Jinha","family":"Jung","sequence":"additional","affiliation":[{"name":"Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Drive, West Lafayette, IN 47907, USA"}]},{"given":"Nothabo","family":"Dube","sequence":"additional","affiliation":[{"name":"Texas A&amp;M AgriLife Research and Extension Center, 10345 TX-44, Corpus Christi, TX 78406, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6870-3771","authenticated-orcid":false,"given":"Murilo","family":"Maeda","sequence":"additional","affiliation":[{"name":"Texas A&amp;M AgriLife Research and Extension Center, 1102 E Farm to Market Rd 1294, Lubbock, TX 79403, USA"}]},{"given":"Daniel","family":"Gonzalez","sequence":"additional","affiliation":[{"name":"Texas A&amp;M AgriLife Research and Extension Center, 10345 TX-44, Corpus Christi, TX 78406, USA"}]},{"given":"Juan","family":"Landivar","sequence":"additional","affiliation":[{"name":"Texas A&amp;M AgriLife Research and Extension Center, 10345 TX-44, Corpus Christi, TX 78406, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"229","DOI":"10.2134\/agronj1992.00021962008400020021x","article-title":"Temperature effects on early season cotton growth and development","volume":"84","author":"Reddy","year":"1992","journal-title":"Agron. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.2134\/agronj2016.07.0439","article-title":"Temperature effects on cotton seedling emergence, growth, and development","volume":"109","author":"Reddy","year":"2017","journal-title":"Agron. J."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/S0168-1702(00)00195-7","article-title":"Cotton leaf curl virus disease","volume":"71","author":"Briddon","year":"2000","journal-title":"Virus Res."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"159","DOI":"10.1016\/S0167-8809(00)00224-3","article-title":"Temperature variability and the yield of annual crops","volume":"82","author":"Wheeler","year":"2000","journal-title":"Agric. Ecosyst. Environ."},{"key":"ref_5","unstructured":"Hopper, N., Supak, J., and Kaufman, H. (1988, January 5\u20138). Evaluation of several fungicides on seedling emergence and stand establishment of Texas high plains cotton. Proceedings of the Beltwide Cotton Production Research Conference, New Orleans, LA, USA."},{"key":"ref_6","first-page":"1","article-title":"Cotton planting date and plant population effects on yield and fiber quality in the Mississippi Delta","volume":"12","author":"Wrather","year":"2008","journal-title":"J. Cotton Sci."},{"key":"ref_7","unstructured":"(2020, July 03). UC IPM Pest Management Guidelines: Cotton. Available online: http:\/\/ipm.ucanr.edu\/PDF\/PMG\/pmgcotton.pdf."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"5345","DOI":"10.1080\/01431161.2017.1410300","article-title":"What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?","volume":"39","author":"Hunt","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1016\/j.compag.2017.07.008","article-title":"Crop height monitoring with digital imagery from Unmanned Aerial System (UAS)","volume":"141","author":"Chang","year":"2017","journal-title":"Comput. Electron. Agric."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1007\/s11119-017-9501-1","article-title":"Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: An applied photogrammetric approach","volume":"19","author":"Roth","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.isprsjprs.2017.10.011","article-title":"Unmanned Aerial System (UAS)-based phenotyping of soybean using multi-sensor data fusion and extreme learning machine","volume":"134","author":"Maimaitijiang","year":"2017","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"722","DOI":"10.1109\/TGRS.2008.2010457","article-title":"Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle","volume":"47","author":"Berni","year":"2009","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1071\/FP16123","article-title":"Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV","volume":"44","author":"Duan","year":"2017","journal-title":"Funct. Plant Biol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.compag.2018.06.051","article-title":"Unmanned aerial system assisted framework for the selection of high yielding cotton genotypes","volume":"152","author":"Jung","year":"2018","journal-title":"Comput. Electron. Agric."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.isprsjprs.2019.04.003","article-title":"A novel framework to detect conventional tillage and no-tillage cropping system effect on cotton growth and development using multi-temporal UAS data","volume":"152","author":"Ashapure","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.agwat.2019.02.017","article-title":"Applying high-resolution visible-channel aerial imaging of crop canopy to precision irrigation management","volume":"216","author":"Chen","year":"2019","journal-title":"Agric. Water Manag."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"933","DOI":"10.1080\/2150704X.2018.1498600","article-title":"A two-stage classification approach for the detection of spider mite- infested cotton using UAV multispectral imagery","volume":"9","author":"Huang","year":"2018","journal-title":"Remote Sens. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Wang, T., Alex Thomasson, J., Yang, C., and Isakeit, T. (2019, January 7\u201310). Field-region and plant-level classification of cotton root rot based on UAV remote sensing. Proceedings of the 2019 ASABE Annual International Meeting, Boston, MA, USA.","DOI":"10.13031\/aim.201901311"},{"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","first-page":"16","article-title":"Yield estimation: A low-hanging fruit for application of small UAS","volume":"23","author":"Ehsani","year":"2016","journal-title":"Resour. Eng. Technol. Sustain. World"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1007\/s11119-017-9508-7","article-title":"Monitoring cotton (Gossypium hirsutum L.) germination using ultrahigh-resolution UAS images","volume":"19","author":"Chen","year":"2018","journal-title":"Precis. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Feng, A., Sudduth, K.A., Vories, E.D., and Zhou, J. (2019, January 7\u201310). Evaluation of cotton stand count using UAV-based hyperspectral imagery. Proceedings of the 2019 ASABE Annual International Meeting, Boston, MA, USA.","DOI":"10.13031\/aim.201900807"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.rse.2017.06.007","article-title":"Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery","volume":"198","author":"Jin","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Gn\u00e4dinger, F., and Schmidhalter, U. (2017). Digital counts of maize plants by unmanned aerial vehicles (UAVs). Remote Sens., 9.","DOI":"10.3390\/rs9060544"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1007\/s11119-015-9425-6","article-title":"Automated image-processing for counting seedlings in a wheat field","volume":"17","author":"Liu","year":"2016","journal-title":"Precis. Agric."},{"key":"ref_26","unstructured":"Kalantar, B., Mansor, S.B., Shafri, H.Z.M., and Halin, A.A. (2016, January 17\u201321). Integration of template matching and object-based image analysis for semi-Automatic oil palm tree counting in UAV images. Proceedings of the 37th Asian Conference on Remote Sensing, ACRS 2016, Colombo, Sri Lanka."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Salam\u00ed, E., Gallardo, A., Skorobogatov, G., and Barrado, C. (2019). On-the-Fly Olive Tree Counting Using a UAS and Cloud Services. Remote Sens., 11.","DOI":"10.3390\/rs11030316"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gu, J., Grybas, H., and Congalton, R.G. (2020). Individual Tree Crown Delineation from UAS Imagery Based on Region Growing and Growth Space Considerations. Remote Sens., 12.","DOI":"10.3390\/rs12152363"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"De Castro, A.I., Torres-S\u00e1nchez, J., Pe\u00f1a, J.M., Jim\u00e9nez-Brenes, F.M., 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_30","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.marpolbul.2016.02.013","article-title":"Water quality dynamics in an urbanizing subtropical estuary (Oso Bay, Texas)","volume":"104","author":"Wetz","year":"2016","journal-title":"Mar. Pollut. Bull."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Schonberger, J.L., and Frahm, J.M. (2016, January 27\u201330). Structure-from-Motion Revisited. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.445"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.geomorph.2012.08.021","article-title":"\u201cStructure-from-Motion\u201d photogrammetry: A low-cost, effective tool for geoscience applications","volume":"179","author":"Westoby","year":"2012","journal-title":"Geomorphology"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive image features from scale-invariant keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vis."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Bentkowska-Kafel, A., and MacDonald, L. (2017). Structure from motion. Digital Techniques for Documenting and Preserving Cultural Heritage, Arc Humanities Press.","DOI":"10.5040\/9781641899444"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1362","DOI":"10.1109\/TPAMI.2009.161","article-title":"Accurate, dense, and robust multiview stereopsis","volume":"32","author":"Furukawa","year":"2010","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1127\/1432-8364\/2012\/0121","article-title":"Dense multi-stereo matching for high quality digital elevation models","volume":"2012","author":"Haala","year":"2012","journal-title":"Photogramm. Fernerkund. Geoinf."},{"key":"ref_37","unstructured":"(2020, July 03). YOLOv3: An Incremental Improvement. Available online: https:\/\/pjreddie.com\/media\/files\/papers\/YOLOv3.pdf."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Redmon, J., and Farhadi, A. (2017, January 21\u201326). YOLO9000: Better, faster, stronger. Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.690"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"103910","DOI":"10.1016\/j.imavis.2020.103910","article-title":"Recent advances in small object detection based on deep learning: A review","volume":"97","author":"Tong","year":"2020","journal-title":"Image Vis. Comput."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1958","DOI":"10.1109\/TPAMI.2008.128","article-title":"80 million tiny images: A large data set for nonparametric object and scene recognition","volume":"30","author":"Torralba","year":"2008","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_41","first-page":"2826","article-title":"Scikit-learn: Machine learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Varoquaux, G., Buitinck, L., Louppe, G., Grisel, O., Pedregosa, F., and Mueller, A. (2015). Scikit-learn. GetMobile Mob. Comput. Commun., 19.","DOI":"10.1145\/2786984.2786995"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2312","DOI":"10.2134\/agronj15.0150","article-title":"Canopeo: A powerful new tool for measuring fractional green canopy cover","volume":"107","author":"Patrignani","year":"2015","journal-title":"Agron. J."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.biombioe.2017.06.027","article-title":"Case study: Estimation of sorghum biomass using digital image analysis with Canopeo","volume":"105","author":"Chung","year":"2017","journal-title":"Biomass Bioenerg."},{"key":"ref_45","unstructured":"Di Stefano, L., and Bulgarelli, A. (1999, January 27\u201329). A simple and efficient connected components labeling algorithm. Proceedings of the 10th International Conference on Image Analysis and Processing, Venice, Italy."},{"key":"ref_46","unstructured":"(2020, July 03). Image Processing Review, Neighbors, Connected Components, and Distance. Available online: http:\/\/homepages.inf.ed.ac.uk\/rbf\/CVonline\/LOCAL_COPIES\/MORSE\/connectivity.pdf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"197","DOI":"10.13031\/2013.17963","article-title":"Economic analysis of subsurface drip irrigation lateral spacing and installation depth for cotton","volume":"48","author":"Enciso","year":"2005","journal-title":"Trans. Am. Soc. Agric. Eng."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1080\/03650340.2014.947284","article-title":"Impact of tillage and intra-row spacing on cotton yield and quality in wheat\u2013cotton system","volume":"61","author":"Khan","year":"2015","journal-title":"Arch. Agron. Soil Sci."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.biosystemseng.2007.03.013","article-title":"Optimisation of the seed spacing uniformity performance of a vacuum-type precision seeder using response surface methodology","volume":"97","author":"Yazgi","year":"2007","journal-title":"Biosyst. Eng."},{"key":"ref_50","first-page":"1","article-title":"Cotton growth, lint yield, and fiber quality as affected by row spacing and cultivar","volume":"8","author":"Nichols","year":"2004","journal-title":"J. Cotton Sci."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1007\/s00357-014-9161-z","article-title":"Ward\u2019s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward\u2019s Criterion?","volume":"31","author":"Murtagh","year":"2014","journal-title":"J. Classif."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2785","DOI":"10.1016\/j.eswa.2014.09.054","article-title":"Efficient agglomerative hierarchical clustering","volume":"42","author":"Bouguettaya","year":"2015","journal-title":"Expert Syst. Appl."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2981\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:09:42Z","timestamp":1760177382000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/18\/2981"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,14]]},"references-count":52,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["rs12182981"],"URL":"https:\/\/doi.org\/10.3390\/rs12182981","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,14]]}}}