{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T07:38:55Z","timestamp":1773301135964,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T00:00:00Z","timestamp":1624579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100004421","name":"World Bank Group","doi-asserted-by":"publisher","award":["792-61187"],"award-info":[{"award-number":["792-61187"]}],"id":[{"id":"10.13039\/100004421","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007637","name":"Departamento Administrativo de Ciencia, Tecnolog\u00eda e Innovaci\u00f3n (COLCIENCIAS)","doi-asserted-by":"publisher","award":["FP44842-217-2018"],"award-info":[{"award-number":["FP44842-217-2018"]}],"id":[{"id":"10.13039\/100007637","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery. The first method is called GFKuts, an approach that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo-based K-means, and a guided image filtering for the extraction of canopy vegetation indices associated with biomass yield. The second method is based on a Graph-Based Data Fusion (GBF) approach that does not depend on calculating vegetation-index image reflectances. Both methods are experimentally tested and compared through rice growth stages: vegetative, reproductive, and ripening. Biomass estimation correlations are calculated and compared against an assembled ground-truth biomass measurements taken by destructive sampling. The proposed GBF-Sm-Bs approach outperformed competing methods by obtaining biomass estimation correlation of 0.995 with R2=0.991 and RMSE=45.358 g. This result increases the precision in the biomass estimation by around 62.43% compared to previous works.<\/jats:p>","DOI":"10.3390\/s21134369","type":"journal-article","created":{"date-parts":[[2021,6,25]],"date-time":"2021-06-25T11:07:40Z","timestamp":1624619260000},"page":"4369","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5403-9547","authenticated-orcid":false,"given":"David Alejandro","family":"Jimenez-Sierra","sequence":"first","affiliation":[{"name":"Department of Electronics and Computer Science, Pontificia Universidad Javeriana Cali, Cali 760031, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1477-6825","authenticated-orcid":false,"given":"Edgar Steven","family":"Correa","sequence":"additional","affiliation":[{"name":"School of Engineering, Pontificia Universidad Javeriana Bogota, Cra. 7 No. 40-62, Bogota 110311, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2661-8867","authenticated-orcid":false,"given":"Hern\u00e1n Dar\u00edo","family":"Ben\u00edtez-Restrepo","sequence":"additional","affiliation":[{"name":"Department of Electronics and Computer Science, Pontificia Universidad Javeriana Cali, Cali 760031, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8681-415X","authenticated-orcid":false,"given":"Francisco Carlos","family":"Calderon","sequence":"additional","affiliation":[{"name":"School of Engineering, Pontificia Universidad Javeriana Bogota, Cra. 7 No. 40-62, Bogota 110311, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7828-6681","authenticated-orcid":false,"given":"Ivan Fernando","family":"Mondragon","sequence":"additional","affiliation":[{"name":"School of Engineering, Pontificia Universidad Javeriana Bogota, Cra. 7 No. 40-62, Bogota 110311, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6925-0126","authenticated-orcid":false,"given":"Julian D.","family":"Colorado","sequence":"additional","affiliation":[{"name":"School of Engineering, Pontificia Universidad Javeriana Bogota, Cra. 7 No. 40-62, Bogota 110311, Colombia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ahmed, M., Ahmad, S., and Ahmad, S. (2017). Climate Variability Impact on Rice Production: Adaptation and Mitigation Strategies. Quantification of Climate Variability, Adaptation and Mitigation for Agricultural Sustainability, Springer.","DOI":"10.1007\/978-3-319-32059-5"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Alebele, Y., Zhang, X., Wang, W., Yang, G., Yao, X., Zheng, H., Zhu, Y., Cao, W., and Cheng, T. (2020). Estimation of Canopy Biomass Components in Paddy Rice from Combined Optical and SAR Data Using Multi-Target Gaussian Regressor Stacking. Remote Sens., 12.","DOI":"10.3390\/rs12162564"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Colorado, J.D., Calderon, F., Mendez, D., Petro, E., Rojas, J.P., Correa, E.S., Mondragon, I.F., Rebolledo, M.C., and Jaramillo-Botero, A. (2020). A novel NIR-image segmentation method for the precise estimation of above-ground biomass in rice crops. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0239591"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Jimenez-Sierra, D.A., Ben\u00edtez-Restrepo, H.D., Vargas-Cardona, H.D., and Chanussot, J. (2020). Graph-Based Data Fusion Applied to: Change Detection and Biomass Estimation in Rice Crops. Remote Sens., 12.","DOI":"10.3390\/rs12172683"},{"key":"ref_5","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_6","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_7","first-page":"173","article-title":"Hyperspectral Features of Rice Canopy and SPAD Values Estimation under the Stress of Rice Leaf Folder","volume":"41","author":"Xiao","year":"2020","journal-title":"Chin. J. Agrometeorol."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Cheng, T., Song, R., Li, D., Zhou, K., Zheng, H., Yao, X., Tian, Y., Cao, W., and Zhu, Y. (2017). Spectroscopic estimation of biomass in canopy components of paddy rice using dry matter and chlorophyll indices. Remote Sens., 9.","DOI":"10.3390\/rs9040319"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Yang, X., Jia, Z., Yang, J., and Kasabov, N. (2019). Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm. Sensors, 19.","DOI":"10.3390\/s19091972"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Li, J., Wu, Z., Hu, Z., Li, Z., Wang, Y., and Molinier, M. (2021). Deep Learning Based Thin Cloud Removal Fusing Vegetation Red Edge and Short Wave Infrared Spectral Information for Sentinel-2A Imagery. Remote Sens., 13.","DOI":"10.3390\/rs13010157"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/S0034-4257(01)00289-9","article-title":"Novel algorithms for remote estimation of vegetation fraction","volume":"80","author":"Gitelson","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Lin, F., Guo, S., Tan, C., Zhou, X., and Zhang, D. (2020). Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images. Sensors, 20.","DOI":"10.3390\/s20216243"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1717","DOI":"10.2134\/agronj2011.0202","article-title":"Estimating rice grain yield potential using normalized difference vegetation index","volume":"103","author":"Harrell","year":"2011","journal-title":"Agron. J."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Campos, J., Garc\u00eda-Ru\u00edz, F., and Gil, E. (2021). Assessment of Vineyard Canopy Characteristics from Vigour Maps Obtained Using UAV and Satellite Imagery. Sensors, 21.","DOI":"10.3390\/s21072363"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"573","DOI":"10.1007\/s10846-019-01001-5","article-title":"High-throughput biomass estimation in rice crops using UAV multispectral imagery","volume":"96","author":"Devia","year":"2019","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Colorado, J.D., Cera-Bornacelli, N., Caldas, J.S., Petro, E., Rebolledo, M.C., Cuellar, D., Calderon, F., Mondragon, I.F., and Jaramillo-Botero, A. (2020). Estimation of Nitrogen in Rice Crops from UAV-Captured Images. Remote Sens., 12.","DOI":"10.3390\/rs12203396"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"15376","DOI":"10.3390\/s121115376","article-title":"Grabcut-based human segmentation in video sequences","volume":"12","author":"Reyes","year":"2012","journal-title":"Sensors"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"309","DOI":"10.1145\/1015706.1015720","article-title":"\u201cGrabCut\u201d interactive foreground extraction using iterated graph cuts","volume":"23","author":"Rother","year":"2004","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Mortensen, E.N., and Barrett, W.A. (1995, January 6\u201311). Intelligent scissors for image composition. Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques, Los Angeles, CA, USA.","DOI":"10.1145\/218380.218442"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Xiong, J., Po, L.M., Cheung, K.W., Xian, P., Zhao, Y., Rehman, Y.A.U., and Zhang, Y. (2021). Edge-Sensitive Left Ventricle Segmentation Using Deep Reinforcement Learning. Sensors, 21.","DOI":"10.3390\/s21072375"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Liu, B., Liu, Z., Li, Y., Zhang, T., and Zhang, Z. (2021). Iterative Min Cut Clustering Based on Graph Cuts. Sensors, 21.","DOI":"10.3390\/s21020474"},{"key":"ref_22","unstructured":"Boykov, Y.Y., and Jolly, M.P. (2001, January 7\u201314). Interactive graph cuts for optimal boundary & region segmentation of objects in ND images. Proceedings of the Eighth IEEE International Conference on Computer Vision (ICCV 2001), Vancouver, BC, Canada."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Castro, W., Marcato Junior, J., Polidoro, C., Osco, L.P., Gon\u00e7alves, W., Rodrigues, L., Santos, M., Jank, L., Barrios, S., and Valle, C. (2020). Deep learning applied to phenotyping of biomass in forages with UAV-based RGB imagery. Sensors, 20.","DOI":"10.3390\/s20174802"},{"key":"ref_24","unstructured":"Kalofolias, V., and Perraudin, N. (2019, January 6\u20139). Large Scale Graph Learning From Smooth Signals. Proceedings of the International Conference on Learning Representations, New Orleans, LA, USA."},{"key":"ref_25","first-page":"554","article-title":"Blue-noise sampling on graphs","volume":"5","author":"Lau","year":"2019","journal-title":"IEEE Trans. Signal Inf. Process. Netw."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"353","DOI":"10.1016\/S0927-0507(03)10006-0","article-title":"Monte Carlo sampling methods","volume":"Volume 10","author":"Shapiro","year":"2003","journal-title":"Handbooks in Operations Research and Management Science"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1145\/1015706.1015777","article-title":"Digital photography with flash and no-flash image pairs","volume":"23","author":"Petschnigg","year":"2004","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Correa, E.S., and Francisco Calderon, J.D.C. (2020, January 23\u201327). GFkuts: A novel multispectral image segmentation method applied to precision agriculture. Proceedings of the Virtual Symposium in Plant Omics Sciences (OMICAS), Cali, Colombia.","DOI":"10.1109\/OMICAS52284.2020.9535659"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"He, K., Sun, J., and Tang, X. (2010, January 5\u201311). Guided image filtering. Proceedings of the European Conference on Computer Vision, Heraklion, Crete, Greece.","DOI":"10.1007\/978-3-642-15549-9_1"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1109\/TPAMI.2004.1262185","article-title":"Spectral grouping using the Nystrom method","volume":"26","author":"Fowlkes","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_31","first-page":"981","article-title":"Sampling methods for the Nystr\u00f6m method","volume":"13","author":"Kumar","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MSP.2018.2887284","article-title":"Learning graphs from data: A signal representation perspective","volume":"36","author":"Dong","year":"2019","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/MSP.2020.3016908","article-title":"Sampling Signals on Graphs: From Theory to Applications","volume":"37","author":"Tanaka","year":"2020","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"4419","DOI":"10.1109\/TGRS.2020.2971395","article-title":"A Graph-Based Approach for Data Fusion and Segmentation of Multimodal Images","volume":"59","author":"Iyer","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Lau, D.L., and Arce, G.R. (2018). Modern Digital Halftoning, CRC Press.","DOI":"10.1201\/9781315219790"},{"key":"ref_36","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Hinton","year":"2008","journal-title":"J. Mach. Learn. Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.chemolab.2018.11.011","article-title":"On the optimization of the support vector machine regression hyperparameters setting for gas sensors array applications","volume":"184","author":"Laref","year":"2019","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.ijmachtools.2012.12.007","article-title":"Nonlinear autoregressive network with exogenous inputs based contour error reduction in CNC machines","volume":"67","author":"Huo","year":"2013","journal-title":"Int. J. Mach. Tools Manuf."},{"key":"ref_39","first-page":"972580","article-title":"Ensemble nonlinear autoregressive exogenous artificial neural networks for short-term wind speed and power forecasting","volume":"2014","author":"Men","year":"2014","journal-title":"Int. Sch. Res. Not."},{"key":"ref_40","unstructured":"and Alfred, R. (2015, January 27\u201328). Performance of modeling time series using nonlinear autoregressive with eXogenous input (NARX) in the network traffic forecasting. Proceedings of the 2015 International Conference on Science in Information Technology (ICSITech), Yogyakarta, Indonesia."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Boussaada, Z., Curea, O., Remaci, A., Camblong, H., and Mrabet Bellaaj, N. (2018). A nonlinear autoregressive exogenous (NARX) neural network model for the prediction of the daily direct solar radiation. Energies, 11.","DOI":"10.3390\/en11030620"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4369\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:24:26Z","timestamp":1760163866000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/13\/4369"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,6,25]]},"references-count":41,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["s21134369"],"URL":"https:\/\/doi.org\/10.3390\/s21134369","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,6,25]]}}}