Making Decisions | High Throughput Phenotyping in Plant Breeding

The plant breeder can examine the data they have extracted for each plot and decide which breeding lines will be moving forward in the trial. Suppose table 2 contains five varieties you are working with. As a breeder, you are concerned with increasing the amount of biomass produced. Because NDVI is positively correlated with biomass, as a breeder you can select the varieties that have the highest NDVI rather than harvesting each plot for destructive biomass measuring.

Table 2. Five plant varieties ranked from best (1) to worst (5) based on NDVI. Each variety has a unique Plot ID signifying planting location in field.
Plot ID	Variety	NDVI	Ranking
837	N5536	0.666813	1
278	N7236	0.562577	4
094	N2938	0.468008	5
290	N8478	0.564904	2
987	N0293	0.563353	3

Figure 17. Spectrum of NDVI values relative to plant health. An NDVI value closer to +1 indicates a healthy plant. An NDVI value closer to –1 indicates a dead plant or inanimate object.

Created by C. Mick using Microsoft PowerPoint, 2022. Based off the figure from “A Business Model Monitoring Using Autonomous Drones in Smart Agriculture”, by Ata Yalçin et al., 2021. DOI: 10.13140/RG.2.2.33960.08962/1

Figure 17 is a more visual way to understand the spectrum of values you could get when using vegetation indices to assist with selection. Imagine you are a plant breeder trying to select which of these four trials to advance. It may be obvious to not advance option A and B, but what about C and D? Using just your eyes, you may have a hard time distinguishing the difference in vegetative health or density. Calculating vegetation indices allows plant breeders to make selections based on objective measurements. This allows them to advance only the best varieties in an efficient manner.