As agricultural output increases and there is more emphasis placed on developing drought resistance crops, the feasibility to assess crop phenotype on the farm is becoming increasingly difficult. An important component in contemporary breeding programs is not only developing genetically advanced crop varieties, but evaluating their performance when grown in the field to select the most successful lines for further breeding. This is an extremely laborious task for large-scale farms, creating a bottleneck for research and industrial ventures.
Just like high-throughput sequencing technologies opened up the floodgates for genetic research, high-throughput phenotyping is changing the game for farmers and researchers alike. Recently, this has been achieved by using low-altitude, high-resolution aerial imaging with unmanned aerial vehicles (i.e. UAVs or drones) (2).
High-throughout phenotyping using sensors attached to UAVs has been shown to estimate:
- Photosynthetic performance through fluorescence sensing of chlorophyll (3)
- Plant height, density and biomass volume using laser scanning (4), 3D cameras (5) and SONAR (6) etc.
- Drought response due to water and nutrient stresses through various infrared spectrometer and thermal imaging methods (2), and
- Disease prevalence by visible-near infrared spectrometry (7).
Agricultural Drones Infographic:
*Infographic courtesy of DroneFly® – if you’re interested in any drone products, check out their website here.
UP NEXT: Using statistical genomics (study of the structure, function, evolution, and mapping of genomes) incorporated with field phenotyping to improve crop yields and provide food security into the future
(2) Sankaran, S., Khot, L. R., Espinoza, C. Z., Jarolmasjed, S., Sathuvalli, V. R., Vandemark, G. J., … Pavek, M. J. (2015). Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review. European Journal of Agronomy, 70, 112–123. https://doi.org/10.1016/j.eja.2015.07.004
(3) Baker, N. R. (2008). Chlorophyll Fluorescence: A Probe of Photosynthesis In Vivo. Annual Review of Plant Biology, 59(1), 89–113. https://doi.org/10.1146/annurev.arplant.59.032607.092759
(4) Müller-Linow, M., Janssen, B., Steier, A., & Rascher, U. (2015). 3-d field phenotyping of crops using laser scanning and photogrammetric approaches. In Workshop on laser scanning applications. 16 Mar 2015 – 16 Mar 2015, Köln (Germany). Retrieved from http://juser.fz-juelich.de/record/188656
(5) Chéné, Y., Rousseau, D., Lucidarme, P., Bertheloot, J., Caffier, V., Morel, P., … Chapeau-Blondeau, F. (2012). On the use of depth camera for 3D phenotyping of entire plants. Computers and Electronics in Agriculture, 82, 122–127. https://doi.org/https://doi.org/10.1016/j.compag.2011.12.007
(6) D. Tumbo, S., Salyani, M., D. Whitney, J., A. Wheaton, T., & M. Miller, W. (2002). INVESTIGATION OF LASER AND ULTRASONIC RANGING SENSORS FOR MEASUREMENTS OF CITRUS CANOPY VOLUME. Applied Engineering in Agriculture, 18(3), 367. https://doi.org/https://doi.org/10.13031/2013.8587
(7) Naidu, R. A., Perry, E. M., Pierce, F. J., & Mekuria, T. (2009). The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars. Computers and Electronics in Agriculture, 66(1), 38–45. https://doi.org/https://doi.org/10.1016/j.compag.2008.11.007