High-throughput field-based phenotyping (HTTP) in maize is driven by the need for more efficient and lower-cost breeding programs. Key criteria for effective HTTP include high spatial and temporal resolution to extract plant and plot-level data. HTTP enables assessment of important traits like plant height, flowering date, disease severity, and yield components. Technologies like drones, sensors, and image processing allow mapping of field variability and estimation of traits like crop cover, flowering, plant height, lodging, senescence, plant population, and ear traits. Deep learning approaches also show potential for multi-trait phenotyping to enable rapid screening for grain yield.

1. High-throughput field-based
phenotyping in maize
Mainassara Zaman-Allah, Jill Cairns,
Amsal Tarekegne, Cosmos Magorokosho,
Mike Olsen, Boddupalli M. Prasanna

2. Trends driving demand for HTTP
Cost of breeding
- Breeding trials are often
extensive and therefore costly
to monitor by conventional
means
- Most national breeding
programs are resource
constrained in Africa
Limited Funding for breeding
Need for the breeding process
to be more resource efficient

3. Trends driving demand for HTTP
Rapid development in
phenomics
- Availability and Affordability of
sensors
- Improved image processing
capacity
- Open-source image
processing options

4. Key Criteria for effective Field-HTTP
Spatial resolution
Extraction of plant/plot level data
within large fields
Temporal resolution
High temporal frequencies to enable
in-season dynamic assessment of
the crop
Speed and flexibility
Significant time saving and
applicable to different crops/stages
Cost of data
collection/extraction
Relatively low-cost

5. Plant/Ear height
Flowering date
Leaf Senescence
Canopy architecture
Disease severity
Stand Count
Yield Components
Many important traits
are still difficult or
costly to evaluate…
What are the target traits?

6. Field variability mapping
Mikrokopter MK Okto XL 6S12
Maize field at CIMMYT-Harare station (Zimbabwe)
- Improved trial mapping
- Improved statistical
analysis

7. OBJECTID PLOT_ID Fraction_Cover
1B 0.469599098
2Plot 1 0.514940983
3Plot 2 0.501934393
4Plot 3 0.531794249
5Plot 4 0.635322204
6Plot 5 0.559072402
7Plot 6 0.618312623
8Plot 7 0.496822014
9Plot 8 0.617791898
10Plot 9 0.543307841
11Plot 10 0.481537099
12Plot 11 0.447446123
13Plot 12 0.559979689
14Plot 13 0.532677165
15Plot 14 0.505721967
16Plot 15 0.580725841
17 Plot 16 0.320792732
Crop Cover/Early Vigor

8. Flowering data
- Tassel development
detection
- Actual anthesis date
requires integration of
machine learning

9. Deep learning for multi-task plant
phenotyping (Wheat)
Pound et al., 2017

10. - LiDAR (Light Detection
and Ranging)
- Hand held Laser Distance
Meter
Plant Height Estimation

11. Canopy surface profiling
- Plant population
- Crop height
- Crop lodging

12. Tianxing Chu et al, 2017
- Number of lodged plants
- Lodging rate within
individual rows.
Crop Lodging

13. Crop Canopy Senescence
Maize trial at CIMMYT-Harare station (Zimbabwe)

14. Crop Canopy Senescence

15. Friederike and Urs 2017
< 5% error compared
to visually counted
plants
Plant Population/ Stand count

16. Makanza et al, 2018
Ear Digital Phenotyping
- Ear Number
- Ear Length/Width
- Kernel Number
- Kernel Size
- Ear Uniformity - Kernel Uniformity
- Total kernel weight

17. Makanza et al, 2018
Ear Digital Phenotyping

18. Rapid Screening for Grain Yield

19. Thank you
for your
interest!