VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.
Crop Phenotyping
Last Updated: 2026-02-05 16:38:38
Abstract
Crop phenotyping aims to quantify traits like photosynthesis, development, architecture, biomass or quality of crops using a broad variety of sensors and analysis procedures. The course aims to provide the necessary basic knowledge in agronomy and plant breeding along with knowledge in image acquisition, computer vision, machine learning and crop modelling to improve crops and cropping systems.
Objective
The course aims to get you acquainted with different aspects of crop phenotyping. Hands-on-practice: Learn how crops develop throughout the season using wheat as an example. Learn how to apply different sensing technologies to monitor crop development ranging from your own eyes over multi-view imaging to thermal imaging, chlorophyll fluorescence, hyperspectral sensing and laser scanning. Basics in agronomy, physiology and plant breeding: Acquire a basic understanding about the major factors affecting the genetic gain for yield and quality parameters. Carriers and sensors: Acquire the ability to select the appropriate combination of sensor and carrier system given the targeted traits. Feature extraction: Acquire a basic understanding about methods to extract features from images or hyperspectral data using machine learning techniques. Trait definition: Know how to define a plant trait using crop ontology standards ( https://cropontology.org/ ). Description of phenotyping experiments: Get acquainted with the “Minimum Information About a Plant Phenotyping Experiment” ( https://www.miappe.org/ ). Data handling: Know the basics about how to organize measurements and data using unique identifiers (UIDs) and relational data tables. Statistical processing: Know how use the Breeders’ equation and calculate heritability to judge the benefit of modern phenotyping techniques. Know how to generate sophisticated experimental designs and analyze them to improve the heritability of a trait using mixed linear models. Dynamic modelling: Know how to model the dynamics of growth to characterize the development using penalized splines and non-linear models. Modelling dependence on environmental gradients: Know how to link growth and development with environmental factors to determine a crop-specific response pattern. Target trait prediction: Get a basic understanding how to use all above inputs to improve the prediction of yield and quality using crop models.
Content
Crops are exposed to different abiotic stress factors during their development. Adaptation of crops to extreme environmental conditions (e.g. cold and heat stress or dry soils) is an important breeding aim. Crop phenotyping includes novel sensors to measure plant traits related to stress response and yield. Examples of such sensors are laser scanners, high-resolution cameras, thermal cameras, or hyperspectral sensors. High-throughput phenotyping includes repeated measurements on hundreds or thousands of experimental plots throughout the growing season. This enables us to quantify environmental effects almost in real-time. The course aims to provide the necessary basic knowledge in crop physiology along with knowledge in image acquisition, computer vision, machine learning (random forest, deep learning) and crop modelling (splines, non-linear models, mixed models) to improve crops and cropping systems. It will take place on the field phenotyping platform FIP (kp.ethz.ch/FIP) of the ETH research station in Eschikon. The course will provide a step-by-step introduction into the whole phenotyping workflow from sensors to the prediction of yield. We will follow wheat through a major part of its development and evaluate the development of the genotypes included in the Swiss variety list. Later in the season, we will also look at peas, soybeans, buckwheat, oats and other crops. At a whole field day in June, experts from ETH, Agroscope and Syngenta will provide hands-on experience in the field. At this day there will also be the presentation of the group work.
Resources
Literature
(1) Walter, A.; Liebisch, F.; Hund, A. Plant Phenotyping: From Bean Weighing to Image Analysis. Plant Methods 2015, 11 (1), 14. https://doi.org/10.1186/s13007-015-0056-8 . (2) Araus, J. L.; Kefauver, S. C.; Zaman-Allah, M.; Olsen, M. S.; Cairns, J. E. Translating High-Throughput Phenotyping into Genetic Gain. Trends Plant Sci. 2018, 23 (5), 451–466. https://doi.org/10.1016/j.tplants.2018.02.001 . (3) van Eeuwijk, F. A.; Bustos-Korts, D.; Millet, E. J.; Boer, M. P.; Kruijer, W.; Thompson, A.; Malosetti, M.; Iwata, H.; Quiroz, R.; Kuppe, C.; Muller, O.; Blazakis, K. N.; Yu, K.; Tardieu, F.; Chapman, S. C. Modelling Strategies for Assessing and Increasing the Effectiveness of New Phenotyping Techniques in Plant Breeding. Plant Science 2019, 282, 23–39. https://doi.org/10.1016/j.plantsci.2018.06.018 .
General Information
- Language
- English
- Levels
- MSC
- Frequency
- Yearly recurring
Examination
- Type
- graded semester performance
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture with exercise | Crop Phenotyping |
|
4 h weekly |
Offered In
-
-
-
-
-
Crop- and Grassland Science (This minor is new and in force from the academic year 22/23. The complete courselist for this minor will be published on the website of the Study Programme.)
-
-
-