Abstract
Accurately and economically estimated crop above-ground biomass (AGB) and bean yield (BY) are critical for cultivation management in precision agriculture. Unmanned aerial vehicle (UAV) platforms have shown great potential in crop AGB and BY estimation due to their ability to rapidly acquire remote sensing data with high temporal–spatial resolution. In this study, a low-cost and consumer-grade camera mounted on a UAV was adopted to acquire red–green–blue (RGB) images, which were then combined with ensemble learning to estimate faba bean AGB and BY. The following results were obtained: (1) The faba bean plant height derived from UAV RGB images presented a strong correlation with the ground measurement (R2 = 0.84, RMSE = 63.6 mm). (2) The accuracy of BY estimation (R2 = 0.784, RMSE = 0.460 t ha−1, NRMSE = 14.973%) based on RGB images was higher than the accuracy of AGB estimation (R2 = 0.618, RMSE = 0.606 t ha−1, NRMSE = 16.746%). (3) The combination of three variables (vegetation index, structural information, textural information) improved the AGB and BY estimation accuracy. (4) The AGB and BY estimation performance were best for the mid bean-filling stage. (5) The ensemble learning model provided higher AGB and BY estimation accuracy than the five base learners (k-nearest neighbor, support vector machine, ridge regression, random forest and elastic net models). These results indicate that UAV RGB images combined with machine learning algorithms, particularly ensemble learning models, can provide relatively accurate faba bean AGB (R2 = 0.683, RMSE = 0.568 t ha−1, NRMSE = 15.684%) and BY (R2 = 0.854, RMSE = 0.390 t ha−1, NRMSE = 12.693%) estimation and considerably contribute to the high-throughput phenotyping study of food legumes.
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References
Acorsi, M. G., das Dores Abati Miranda, F., Martello, M., Smaniotto, D. A., & Sartor, L. R. (2019). Estimating biomass of black oat using UAV-based RGB imaging. Agronomy, 9(7), 344. https://doi.org/10.3390/agronomy9070344
Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., et al. (2015). Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, 39, 79–87. https://doi.org/10.1016/j.jag.2015.02.012.
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.
Castillo-Martínez, M., Gallegos-Funes, F., Carvajal-G´amez, B., Urriolagoitia-Sosa, G., & Rosales-Silva, A. (2020). Color index based thresholding method for background and foreground segmentation of plant images. Computers and Electronics in Agriculture, 178, 105783. https://doi.org/10.1016/j.compag.2020.105783.
Cheng, M., Jiao, X., Liu, Y., Shao, M., Yu, X., Bai, Y., Wang, Z., Wang, S., Tuohuti, N., Liu, S., & Shi, L. (2022). Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning. Agricultural Water Management, 264, 107530. https://doi.org/10.1016/j.agwat.2022.107530
Cover, T. M., & Hart, P. E. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21–27. https://doi.org/10.1109/TIT.1967.1053964.
Dai, S., Zheng, X., Gao, L., Xu, C., Zuo, S., Chen, Q., et al. (2021). Improving plot-level model of forest biomass: A combined approach using machine learning with spatial statistics. Forests, 12, 1663. https://doi.org/10.3390/f12121663
Du, C., Fan, W., Ma, Y., Jin, H. I., & Zhen, Z. (2021). The effect of synergistic approaches of features and ensemble learning algorithms on aboveground biomass estimation of natural secondary forests based on ALS and Landsat 8. Sensors (Basel, Switzerland), 21, 5974. https://doi.org/10.3390/s21175974
Etemadi, F., Hashemi, M., Zandvakili, O., & Mangan, F. (2018). Phenology, yield and growth pattern of Faba bean varieties. International Journal of Plant Production, 12, 243–250. https://doi.org/10.1007/s42106-018-0023-1
Farid, I. M., El-Ghozoli, M. A., Abbas, M. H. H., El-Atrony, D. S., Abbas, H. H., Elsadek, M., et al. (2021). Organic materials and their chemically extracted humic and fulvic acids as potential soil amendments for Faba Bean cultivation in soils with varying CaCO3 contents. Horticulturae, 7(8), 205. https://doi.org/10.3390/horticulturae7080205.
Fei, S., Hassan, M. A., He, Z., Chen, Z., Shu, M., Wang, J., et al. (2021). Assessment of ensemble learning to predict wheat grain yield based on UAV-multispectral reflectance. Remote Sensing, 13(12), 2338. https://doi.org/10.3390/rs13122338
Fei, S., Hassan, M. A., Ma, Y., Shu, M., Cheng, Q., Li, Z., et al. (2021). Entropy weight ensemble framework for yield prediction of winter wheat under different water stress treatments using unmanned aerial vehicle-based multispectral and thermal data. Frontiers in Plant Science, 12, 730181. https://doi.org/10.3389/fpls.2021.730181
Feng, L., Zhang, Z., Ma, Y., Du, Q., Williams, P., & Drewry, J. (2020). Alfalfa yield prediction using UAV-based hyperspectral imagery and ensemble learning. Remote Sensing, 12, 2028. https://doi.org/10.3390/rs12122028
Fieuzal, R., Sicre, C., & Baup, M., F (2017). Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks. International Journal of Applied Earth Observation and Geoinformation, 57, 14–23. https://doi.org/10.1016/j.jag.2016.12.011.
Fisher, J. R. B., Acosta, E. A., Dennedy-Frank, P. J., Kroeger, T., & Boucher, T. M. (2018). Impact of satellite imagery spatial resolution on land use classification accuracy and modeled water quality. Remote Sensing in Ecology and Conservation, 4(2), 137–149. https://doi.org/10.1002/rse2.61.
Gamon, J. A., & Surfus, J. S. (1999). Assessing leaf pigment content and activity with a reflectometer. New Phytologist, 143(1), 105–117. https://doi.org/10.1046/j.1469-8137.1999.00424.x.
Gitelson, A. A., Kaufman, Y. J., Stark, R., & Rundquist, D. (2002). Novel algorithms for remote estimation of vegetation fraction. Remote Sensing of Environment, 80(1), 76–87. https://doi.org/10.1016/S0034-4257(01)00289-9.
Gnyp, M. L., Bareth, G., Li, F., Lenz-Wiedemann, V. I. S., Koppe, W., Miao, Y., Hennig, S. D., Jia, L., Laudien, R., Chen, X., & Zhang, F. (2014). Development and implementation of a multiscale biomass model using hyperspectral vegetation indices for winter wheat in the North China Plain. International Journal of Applied Earth Observation and Geoinformation, 33, 232–242. https://doi.org/10.1016/j.jag.2014.05.006
Grüner, E., Astor, T., & Wachendorf, M. (2019). Biomass prediction of heterogeneous temperate grasslands using an SFM approach based on UAV imaging. Agronomy, 9(2), 54. https://doi.org/10.3390/agronomy9020054.
Guo, Y., Wang, H., Wu, Z., Wang, S., Sun, H., Senthilnath, J., et al. (2020). Modified red blue vegetation index for chlorophyll estimation and yield prediction of maize from visible images captured by UAV. Sensors (Basel, Switzerland), 20(18), 5055. https://doi.org/10.3390/s20185055
Hague, T., Tillett, N. D., & Wheeler, H. (2006). Automated crop and weed monitoring in widely spaced cereals. Precision Agriculture, 7(1), 21–32. https://doi.org/10.1007/s11119-005-6787-1
Harkel, J., Bartholomeus, H., & Kooistra, L. (2020). Biomass and crop height estimation of different crops using UAV-based LiDAR. Remote Sensing, 12(1), 17. https://doi.org/10.3390/RS12010017.
Holman, F., Riche, A., Michalski, A., Castle, M., Wooster, M., & Hawkesford, M. (2016). High throughput field phenotyping of wheat plant height and growth rate in field plot trials using UAV based remote sensing. Remote Sensing, 8(12), 1031. https://doi.org/10.3390/rs8121031
Ji, Y., Chen, Z., Cheng, Q., Liu, R., Li, M., Yan, X., Li, G., Wang, D., Fu, L., Ma, Y., Jin, X., Zong, X., & Yang, T. (2022). Estimation of plant height and yield based on UAV imagery in faba bean (Vicia faba L.). Plant Methods, 18(1), 26. https://doi.org/10.1186/s13007-022-00861-7
Jin, X., Liu, S., Baret, F., Hemerlé, M., & Comar, A. (2017). Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sensing of Environment, 198, 105–114. https://doi.org/10.1016/j.rse.2017.06.007
Kawashima, S. (1998). An algorithm for estimating chlorophyll content in leaves using a video camera. Annals of Botany, 81(1), 49–54. https://doi.org/10.1006/anbo.1997.0544
Kuhn, M. (2008). Building predictive models in R using the caret package. Journal of Statistical Software, 28(5), 1–26. https://doi.org/10.18637/jss.v028.i05
Leroux, L., Castets, M., Baron, C., Escorihuela, M., Bégué, A., & Seen, D. (2019). Maize yield estimation in West Africa from crop process-induced combinations of multi-domain remote sensing indices. European Journal of Agronomy, 108, 11–26. https://doi.org/10.1016/j.eja.2019.04.007.
Li, J., Shi, Y., Veeranampalayam-Sivakumar, A. N., & Schachtman, D. P. (2018). Elucidating sorghum biomass, nitrogen and chlorophyll contents with spectral and morphological traits derived from unmanned aircraft system. Frontiers in Plant Science, 9, 1406. https://doi.org/10.3389/fpls.2018.01406
Li, B., Xu, X., Zhang, L., Han, J., Bian, C., Li, G., et al. (2020). Above-ground biomass estimation and yield prediction in potato by using UAV-based RGB and hyperspectral imaging. ISPRS Journal of Photogrammetry and Remote Sensing, 162, 161–172. https://doi.org/10.1016/j.isprsjprs.2020.02.013.
Li, J., Schachtman, D., Creech, C., Wang, L., Ge, Y., & Shi, Y. (2022). Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum. The Crop Journal, 10(5), 1363–1375. https://doi.org/10.1016/j.cj.2022.04.005.
Louhaichi, M., Borman, M. M., & Johnson, D. E. (2001). Spatially located platform and aerial photography for documentation of grazing impacts on wheat. Geocarto International, 16(1), 65–70. https://doi.org/10.1080/10106040108542184.
Lu, N., Zhou, J., Han, Z., Li, D., Cao, Q., Yao, X., et al. (2019). Improved estimation of aboveground biomass in wheat from RGB imagery and point cloud data acquired with a low-cost unmanned aerial vehicle system. Plant Methods, 15, 17. https://doi.org/10.1186/s13007-019-0402-3.
Maimaitijiang, M., Sagan, V., Sidike, P., Hartling, S., Esposito, F., & Fritschi, F. B. (2020). Soybean yield prediction from UAV using multimodal data fusion and deep learning. Remote Sensing of Environment, 237, 111599. https://doi.org/10.1016/j.rse.2019.111599.
Meyer, G. E., Hindman, T. W., & Laksmi, K. (1999). Machine vision detection parameters for plant species identification. Precision Agriculture and Biological Quality, Proceedings of SPIE, 3543, 327–335. https://doi.org/10.1117/12.336896
Nichol, J. E., & Sarker, M. L. R. (2011). Improved biomass estimation using the texture parameters of two high-resolution optical sensors. IEEE Transactions on Geoscience and Remote Sensing, 49(3), 930–948. https://doi.org/10.1109/TGRS.2010.2068574.
Niu, Y., Zhang, L., Zhang, H., Han, W., & Peng, X. (2019). Estimating above-ground biomass of maize using features derived from UAV-based RGB imagery. Remote Sensing, 11, 1261. https://doi.org/10.3390/rs11111261.
Nutt, A. T., & Batsell, R. R. (1973). Multiple linear regression: A realistic reflector. Data Analysis, 19, 21.
Pal, M. (2007). Ensemble learning with decision tree for remote sensing classification. Proceedings of World Academy of Science Engineering and Technology, 36, 258–260.
Rischbeck, P., Elsayed, S., Mistele, B., Barmeier, G., Heil, K., & Schmidhalter, U. (2016). Data fusion of spectral, thermal and canopy height parameters for improved yield prediction of drought stressed spring barley. European Journal of Agronomy, 78, 44–59. https://doi.org/10.1016/j.eja.2016.04.013.
Sagan, V., Maimaitijiang, M., Sidike, P., Maimaitiyiming, M., Erkbol, H., Hartling, S., & Early Stress Detection. (2019). UAV/Satellite Multiscale Data Fusion for Crop Monitoring and. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLII-2/W13, 715–722. https://doi.org/10.5194/isprs-archives-XLII-2-W13-715-2019
Senthilnath, J., Varia, N., Dokania, A., Anand, G., & Benediktsson, J. A. (2020). Deep TEC: Deep transfer learning with ensemble classifier for road extraction from UAV imagery. Remote Sensing, 12(2), 245. https://doi.org/10.3390/rs12020245
Shah, S. H., Angel, Y., Houborg, R., Ali, S., & McCabe, M. F. (2019). A random forest machine learning approach for the retrieval of leaf chlorophyll content in wheat. Remote Sensing, 11(8), 920. https://doi.org/10.3390/rs11080920
Shu, M., Shen, M., Zuo, J., Yin, P., Wang, M., & Xie, Z. (2021). The application of UAV-based hyperspectral imaging to estimate crop traits in maize inbred lines. Plant Phenomics. https://doi.org/10.34133/2021/9890745
Stanton, C., Starek, M. J., Elliott, N., Brewer, M., Maeda, M. M., & Chu, T. (2017). Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment. Journal of Applied Remote Sensing, 11(2), 026035. https://doi.org/10.1117/1.JRS.11.026035.
Stavrakoudis, D., Katsantonis, D., Kadoglidou, K., Kalaitzidis, A., & Gitas, I. (2019). Estimating rice agronomic traits using drone-collected multispectral imagery. Remote Sensing, 11(5), 545. https://doi.org/10.3390/rs11050545
Tao, H., Feng, H., Xu, L., Miao, M., Long, H., Yue, J., et al. (2020). Estimation of crop growth parameters using UAV-based hyperspectral remote sensing data. Sensors (Basel, Switzerland), 20(5), 1296. https://doi.org/10.3390/s20051296
Tikhonov, A. N. (1943). On the stability of inverse problems. Doklady Akademii Nauk Sssr, 39, 176–179.
Tucker, C. J. (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sensing of Environment, 8(2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0.
Vapnik, V., & Cortes, C. (1995). Support-vector networks. Machine Learning, 20, 273–297. https://doi.org/10.1023/A:1022627411411.
Wang, J., Shi, T., Yu, D., Teng, D., Ge, X., Zhang, Z., et al. (2020). Ensemble machine-learning-based framework for estimating total nitrogen concentration in water using drone-borne hyperspectral imagery of emergent plants: a case study in an arid oasis, NW China. Environmental Pollution, 266, 115412. https://doi.org/10.1016/j.envpol.2020.115412.
Wang, F., Yi, Q., Hu, J., Xie, L., Yao, X., Xu, T., et al. (2021). Combining spectral and textural information in UAV hyperspectral images to estimate rice grain yield. International Journal of Applied Earth Observation and Geoinformation, 102, 102397. https://doi.org/10.1016/j.jag.2021.102397.
Woebbecke, D. M., Meyer, G. E., Von Bargen, K., & Mortensen, D. A. (1993). Plant species identification, size, and enumeration using machine vision techniques on near-binary images. Optics in Agriculture and Forestry International Society for Optics and Photonics SPIE, 1863, 208–219. https://doi.org/10.1117/12.144030.
Woebbecke, D. M., Meyer, G. E., Von Bargen, K., & Mortensen, D. A. (1995). Color indices for weed identification under various soil, residue, and lighting conditions. Transactions of the Asae, 38(1), 259–269. https://doi.org/10.13031/2013.27838.
Yoosefzadeh-Najafabadi, M., Earl, H. J., Tulpan, D., Sulik, J., & Eskandari, M. (2021). Application of machine learning algorithms in plant breeding: Predicting yield from hyperspectral reflectance in soybean. Frontiers in Plant Science, 11(1), 449–459. https://doi.org/10.3389/fpls.2020.624273
Yu, D., Zha, Y., Shi, L., Jin, X., Hu, S., Yang, Q., et al. (2020). Improvement of sugarcane yield estimation by assimilating UAV-derived plant height observations. European Journal of Agronomy, 121, 126159. https://doi.org/10.1016/j.eja.2020.126159.
Yue, J., Yang, G., Li, C., Li, Z., Wang, Y., Feng, H., et al. (2017). Estimation of winter wheat above-ground biomass using unmanned aerial vehicle-based snapshot hyperspectral sensor and crop height improved models. Remote Sensing, 9(7), 708. https://doi.org/10.3390/rs9070708
Yue, J., Yang, G., Tian, Q., Feng, H., Xu, K., & Zhou, C. (2019). Estimate of winter-wheat above-ground biomass based on UAV ultrahigh-ground-resolution image textures and vegetation indices. ISPRS Journal of Photogrammetry and Remote Sensing, 150, 226–244. https://doi.org/10.1016/j.isprsjprs.2019.02.022.
Zhang, Z., Pasolli, E., & Crawford, M. M. (2020). An adaptive multiview active learning approach for spectral-spatial classification of hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 99, 1–14. https://doi.org/10.1109/TGRS.2019.2952319
Zhou, G., Bao, X., Ye, S., Wang, H., & Yan, H. (2021). Selection of optimal building facade texture images from UAV-based multiple oblique image flows. IEEE Transactions on Geoscience and Remote Sensing, 59(2), 1534–1552. https://doi.org/10.1109/TGRS.2020.3023135
Zou, H., & Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B: Statistical Methodology, 67, 768. https://doi.org/10.1111/j.1467-9868.2005.00503.x.
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We acknowledge TopEdit LLC for the linguistic editing and proofreading during the preparation of this manuscript. This research was funded by China Agriculture Research System of MOF and MARA-Food Legumes (CARS-08), National Crop Genebank project from the Ministry of Science and Technology of China (NCGRC-2022-7) and Agricultural Science and Technology Innovation Program (ASTIP) in CAAS.
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YJ: Data curation, Methodology, Formal analysis, Visualization, Writing—Original Draft. RL: Formal analysis, Investigation. YX: Visualization, Investigation. YC: Investigation, Data curation. ZC: Methodology, Software, Supervision. XZ: Conceptualization, Resources, Funding acquisition. TY: Methodology, Writing—Review & Editing, Supervision.
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Ji, Y., Liu, R., Xiao, Y. et al. Faba bean above-ground biomass and bean yield estimation based on consumer-grade unmanned aerial vehicle RGB images and ensemble learning. Precision Agric 24, 1439–1460 (2023). https://doi.org/10.1007/s11119-023-09997-5
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DOI: https://doi.org/10.1007/s11119-023-09997-5