Herrero-Huerta, M., et al. (2023) 'Tree-level Fuel Connectivity to assess Crown Wildfire Potential by UAS-based Photogrammetry'.In International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences (Egypt). https://doi.org/10.5194/isprs-archives-XLVIII-1-W2-2023-1743-2023.

Herrero-Huerta, M., Gonzalez-Aguilera, D., & Yang, Y. (2023). Structural Component Phenotypic Traits from Individual Maize Skeletonization by UAS-Based Structure-from-Motion Photogrammetry. Drones, 7(2), 108.

Herrero-Huerta, M., Raumonen, P., and Gonzalez-Aguilera, D. (2022). Frontiers in Plant Science, 13. DOI: 10.3389/fpls.2022.986856.

Herrero-Huerta, M., Tardy, H., Morcillo-Sanz, A., Gonzalez-Gonzalez, E. and Gonzalez-Aguilera, D. (2022). 'Grape Bunch Architecture by Low-Cost 3D Scanner'. 2022 Frutic, 14th International Symposium.

Herrero-Huerta, M., Meline, V., Iyer-Pascuzzi, A.S. et al. (2021). '4D Structural root architecture modeling from digital twins by X-Ray Computed Tomography'. Plant Methods 17, 123 (2021). DOI: 10.1186/s13007-021-00819-1.

Herrero-Huerta, M., Meline, V., Iyer-Pascuzzi, A. S., Souza, A. M., Tuinstra, M. R., and Yang, Y. (2021). 'Root phenotyping from X-ray Computed Tomography: skeleton extraction'. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2021, 417-422. DOI: 10.5194/isprs-archives-XLIII-B4-2021-417-2021, 2021

Herrero-Huerta, M., Tolley, S., Tuinstra, M. R., and Yang, Y. (2021). 'Individual maize extraction from UAS imagery-based point clouds by 3D deep learning'. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI (Vol. 11747, p. 1174704). International Society for Optics and Photonics.

Herrero-Huerta, M., Bucksch, A., and Rainey, K. (2020) 'Canopy roughness: a new phenotypic trait to estimate above-ground biomass from unmanned aerial system'. Plant Phenomics. In Press. Article ID 6735967

Herrero-Huerta, M., Rodriguez-Gonzalvez, P. & Rainey, K.M. Yield prediction by machine learning from UAS-based multi-sensor data fusion in soybean. Plant Methods 16, 78 (2020). https://doi.org/10.1186/s13007-020-00620-6

Subsurface agriculture tile lines can greatly impact plant phenotypic characteristics through spatial variation of soil moisture, plant nutrient, and plant rooting depth. Therefore, location of subsurface tile lines plays a critical role in supporting the above ground plant phentoyping and needs to be considered in plant phenotyping analysis....