RGB (Red, green, blue) sensor |
400~700 nm |
Sensing visible wavelengths. Most easily accessible sensor. |
Vegetation indices, plant height, plant structure, growth rates, and morphological traits. |
Kim et al. (2018)Kim, D.W.; Yun, H.S.; Jeong, S.J.; Kwon, Y.S.; Kim, S.G.; Lee, W.S.; Kim, H.J. 2018. Modeling and testing of growth status for Chinese cabbage and white radish with UAV-based RGB imagery. Remote Sensing, 10: 563-587; Crimmins and Crimmins (2008)Crimmins, M.A.; Crimmins, T.M. 2008. Monitoring plant phenology using digital repeat photography. Environmental Management 41: 949-958.; Deery et al. (2014)Deery, D.; Jimenez-Berni, J.; Jones, H.; Sirault, X.; Furbank, R. 2014. Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 4: 349-379.; Liu et al. (2017)Liu, S.; Baret, F.; Andrieu, B.; Burger, P.; Hemmerlé, M. 2017. Estimation of wheat plant density at early stages using high resolution imagery. Frontiers in Plant Science 8: 1-10.
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NIR (Near infrared) sensor |
700~1400 nm |
Sensing highest reflectance of plant green area in 700~1300 nm, while beyond 1300 nm shows more absorbance by water than the visible spectrum. |
Chlorophyll conductance, water status, and vegetation indices. |
Bei et al. (2011)Bei, R.; Cozzolino, D.; Sullivan, W.; Cynkar, W.; Fuentes, S.; Dambergs, R.; Pech, J.; Tyerman, S. 2011. Non-destructive measurement of grapevine water potential using near infrared spectroscopy. Australian Journal of Grape and Wine Research 17: 62-71.; Bendig et al. (2015)Bendig, J.; Yu, K.; Aasen, H.; Bolten, A.; Bennertz, S.; Broscheit, J.; Gnyp, M.L.; Bareth, G. 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.; Thiel et al. (2010); Yang et al. (2017)Yang, G.; Liu, J.; Zhao, C.; Li, Z.; Huang, Y.; Yu, H.; Xu, B.; Yang, X.; Zhu, D.; Zhang, X.; Zhang, R.; Feng, H.; Zhao, X.; Li, Z.; Li, H.; Yang, H. 2017. Unmanned aerial vehicle remote sensing for field-based crop phenotyping: current status and perspectives. Frontiers in Plant Science 8: 1111-1136.
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Hyperspectral sensor |
- |
Sensing thousands of bands per pixel. More detailed images can be obtained than the multispectral imaging if the requirements are set. |
Vegetation and water indices, soil cover status, photosynthesis rates, and levels of phytochemicals. |
Hamada et al. (2007)Hamada, Y.; Stow, D.A.; Coulter. L.L.; Jafolla, J.C.; Hendricks, L.W. 2007. Detecting tamarisk species (Tamarix spp.) in riparian habitats of southern California using high spatial resolution hyperspectral imagery. Remote Sensing of Environment 109: 237-248.; Stagakis et al. (2010)Stagakis, S.; Markos, N.; Sykioti, O.; Kyparissis, A. 2010. Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: an application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations. Remote Sensing of Environment 114: 977-994.; Zhao et al. (2013)Zhao, K.; Valle, D.; Popescu, S.; Zhang, X.; Mallick, B. 2013. Hyperspectral remote sensing of plant biochemistry using Bayesian model averaging with variable and band selection. Remote Sensing of Environment 132: 102-119.; El-Hendawy et al. (2019a); El-Hendawy et al. (2019b) |
Thermal sensor |
700~106 nm |
Sensing emitted radiation of object that increases with the object temperature above absolute zero. Suitable to image temperature changes. |
Canopy temperature, transpiration rates, and water stress responses. |
Baluja et al. (2012)Baluja, J.; Diago, M.P.; Balda, P.; Zorer, R.; Meggio, F.; Morales, F.; Tardaguila, J. 2012. Assessment of vineyard water status variability by thermal and multispectral imagery using an Unmanned Aerial Vehicle (UAV). Irrigation Science 30: 511-522.; Berni et al. (2009)Berni, J.A.J.; Zarco-Tejada, P.J.; Suarez, L.; Fereres, E. 2009. Thermal and narrowband multispectral remote sensing for vegetation monitoring from an unmanned aerial vehicle. IEEE Transactions on Geoscience and Remote Sensing 47: 722-738.; Gago et al. (2015)Gago, J.; Douthe, C.; Coopman, R.E.; Gallego, P.P.; Ribas-Carbo, M.; Flexas, J.; Escalona, J.; Medrano, H. 2015. UAVs challenge to assess water stress for sustainable agriculture. Agricultural Water Management 153: 9-19.; Leinonen et al. (2006)Leinonen, I.; Grant, O.M.; Tagliavia, C.P.P.; Chaves, M.M.; Jones, H.G. 2006. Estimating stomatal conductance with thermal imagery. Plant, Cell and Environment 29: 1508-1518.
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Fluorescence sensor |
180~800 nm |
Sensing fluorescence emitted by short wave light absorption of susceptible molecule. |
Chlorophyll conductance, photosynthetic rates, and pigment composition. |
Chaerle et al. (2006)Chaerle, L.; Leinonen, I.; Jones, H.G.; Van Der Straeten, D. 2006. Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging. Journal of Experimental Botany 58: 773-784.
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LiDAR (Light Detection and Ranging) |
250~2,000 nm |
Surface scan of target objects and distance measurement by analyzing the reflected light. |
Canopy and leaves, vegetation cover, plant height, and nitrogen status. |
Lin (2015)Lin, Y. 2015. Lidar: an important tool for next-generation phenotyping technology of high potential for plant phenomics? Computers and Electronics in Agriculture 119: 61-73.; Eitel et al. (2014)Eitel, J.U.H.; Magney, T.S.; Vierling, L.A.; Brown, T.T.; Huggins, D.R. 2014. LiDAR based biomass and crop nitrogen estimates for rapid, non-destructive assessment of wheat nitrogen status. Field Crops Research 159: 21-32.; Madec et al. (2017)Madec, S.; Baret, F.; de Solan, B.; Thomas, S.; Dutartre, D.; Jezequel, S.; Hemmerlé, M.; Colombeau, G.; Comar, A. 2017. High-throughput phenotyping of plant height: comparing unmanned aerial vehicles and ground lidar estimates. Frontiers in Plant Science 8: 2002-2015.; Omasa et al. (2006)Omasa, K.; Hosoi, F.; Konishi, A. 2006. 3D lidar imaging for detecting and understanding plant responses and canopy structure. Journal of Experimental Botany 58: 881-898.; Zhang and Grift (2012)Zhang, L.; Grift, T.E. 2012. A lidar-based crop height measurement system for Miscanthus giganteus. Computers and Electronics in Agriculture 85: 70-76.; |
Others -MRI |
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Feasible to screen underground structures of plant by 3D imaging and transport processes in natural porous media. |
Water contents, stem structures, root structures, transport processes |
Capitani et al. (2009)Capitani, D.; Brilli, F.; Mannina, L.; Proietti, N.; Loreto, F. 2009. In situ investigation of leaf water status by portable unilateral nuclear magnetic resonance. Plant Physiology 149: 1638-1647.; Gosa et al. (2019)Gosa, S.C.; Lupo, Y.; Moshelion, M. 2019. Quantitative and comparative analysis of whole-plant performance for functional physiological traits phenotyping: new tools to support pre-breeding and plant stress physiology studies. Plant Science 282: 49-59.; Pohlmeier et al. (2008)Pohlmeier, A.; Oros-Peusquens, A.; Javaux, M.; Menzel, M.I.; Vanderborght, J.; Kaffanke, J.; Romanzetti, J.; Lindenmair, H.; Shah, N.J. 2008. Changes in soil water content resulting from Ricinus root uptake monitored by magnetic resonance imaging. Vadose Zone Journal 7: 1010-1017.; Van As and Van Dusschoten (1997)Van As, H.; Van Dusschoten, D. 1997. NMR methods for imaging of transport processes in micro-porous systems. Geoderma 80: 389-403.
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Others -Gravimetric senor |
- |
Capable of measuring plant physiological changes by non-imaging process. Requires other sensors for screening. |
Weight, water use efficiency, water status, transpiration rates, biomass. |
Halperin et al. (2017)Halperin, O.; Gebremedhin, A.; Wallach, R.; Moshelion, M. 2017. High-throughput physiological phenotyping and screening system for the characterization of plant-environment interactions. The Plant Journal 89: 839-850.; Iyer-Pascuzzi et al. (2010)Iyer-Pascuzzi, A.S.; Symonova, O.; Mileyko, Y.; Hao, Y.; Belcher, H.; Harer, J.; Weitz, J.S.; Benfey, P.N. 2010. Imaging and analysis platform for automatic phenotyping and trait ranking of plant root systems. Plant Physiology 152: 1148-1157.; Negin and Moshelion (2017)Negin, B.; Moshelion, M. 2017. The advantages of functional phenotyping in pre-field screening for drought-tolerant crops. Functional Plant Biology 44: 107-118.. |