Phenotyping
The genetic composition, i.e. DNA sequences, of an organism is called Genotype and can be determined by the genotyping process[1]. However, plants with the same genotype might display completely different traits due to the environmental condition in which the plants are cultivated. All the plant's observable characteristics (the effects of genotype and environment combined) are referred to as phenotype[2]. As a result, monitoring the characteristics of as many crops as possible with different combinations of genotype and environment would help determine the most efficient phenotype (the best genotype for a specific environment). The efficiency here can be the yield, size or color of fruits, disease resistance, drought tolerance, adoption to a specific condition such as salinity, or any desirable trait sought after. It is perceived that phenotyping is one of the primary ways to increase the productivity of crops worldwide[3]. Therefore, the necessity for high-throughput data acquisition of trait data is inevitable. All the remote sensing applications discussed in the previous sections can be classified as a sub-set of phenotyping, so the methods used for those applications are applicable for phenotyping as well. For example, a walnut orchard consisting of several cultivars can be monitored for nematode resistance by applying different treatments, and phenotypic data determine the most vigorous variety. Similarly, almond orchards can be monitored to determine the cultivars that has the most desired bloom phenology[4].
References
[1] Scitable, “genotype | Learn Science at Scitable,” 2014. https://www.nature.com/scitable/definition/genotype-234/ (accessed Nov. 29, 2020).
[2] Scitable, “phenotype / phenotypes | Learn Science at Scitable,” 2014. https://www.nature.com/scitable/definition/phenotype-phenotypes-35/ (accessed Nov. 29, 2020).
[3] R. R. Mir, M. Reynolds, F. Pinto, M. A. Khan, and M. A. Bhat, “High-throughput phenotyping for crop improvement in the genomics era,” Plant Science, vol. 282, pp. 60–72, 2019.
[4] B. Chen, Y. Jin, and P. Brown, “An enhanced bloom index for quantifying floral phenology using multi-scale remote sensing observations,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 156, pp. 108–120, 2019.