Crop Phenotyping (ETHZ 751-4106-00L)
| 2026_FS_CropPheno | |
| 20.02.2026 - 29.05.2026 | |
| 13 x 4 h and 1 full day | |
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Start registration period: 10.12.2025 End of registration period: 20.01.2026 |
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| External | |
| 5 | |
| 5 | |
| Free of Charge for all Master and PhD students (PSC, LSZGS) | |
| 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. Arable crops such as wheat or soybean cover a large part of our planet. The efficiency with which they are produced has a major impact on food security and ecology. During their development, crops are exposed to a wide range of biotic and abiotic stresses. Adaptation of crops to a changing climate including extreme environmental conditions (e.g. cold and heat stress or drying soils) and to new diseases are important breeding aims. New cropping systems, such as mixed cropping or intercropping, introduce additional complexity for crop improvement: Breeders need to understand the interaction among plants of different varieties or species to make the right selection decisions. The crop phenotyping community offers tools to improve the selection process by helping to understand how crops achieve high yield and how yield components are affected by stresses. Their phenotyping toolbox consists of: i) sensor carriers (drones, ground-based robots, gantries and hand-held devices), ii) all kinds of active and passive sensors, and iii) an extensive model and data analysis framework. This framework is increasingly deploying 3D information from crop canopies combined with crop models and AI-driven image and data analysis workflows. Due to this complexity the community is divided into different sub disciplines with research specialists such as i) physiological breeders, who bring in a mechanistic understanding how crops function; ii) automation and sensor developers, who bring in new, plant-specific sensing solutions; iii) modellers, who make sure that sensor-derived data is summarized to predict the target traits (mostly yield and quality parameters); iv) data management specialists making sure that data is FAIR and that thousands of small experiments carried out across the globe can be harnessed into big data to understand the interaction between genotypes, the production environment and management practices (GxExM). All these specialists need to be able to collaboratively contribute to the overall data acquisition and data processing workflow. In Crop Phenotyping, we teach the main aspects of this workflow and form teams that collaboratively work on a common project. The course will take place on the field phenotyping platform FIP (kp.ethz.ch/FIP) of the ETH research station in Eschikon, which is part of the International Plant Phenotyping Network (https://www.plant-phenotyping.org), European Research Infrastructure for Plant Phenotyping (https://emphasis.plant-phenotyping.eu/) and the DigiCrop network (https://digicrop.net/). We planted a “variety garden” with the most important crops of Switzerland and an experiment including all varieties listed on the wheat variety list of Switzerland to work on. Both experiments will be used also for teaching and intense observations with the aim to explain how the different crops go through the season. This will include in-field observation of the response towards stresses and diseases. We will look at different types of sensors ranging from active sensing of chlorophyll fluorescence as proxy for photosynthesis over thermography to high-resolution RGB imaging including reconstruction of point clouds from multi-view images. Modelling steps will include the acquisition of image training data by labelling, the extraction of features using deep neural networks, spatial correction of field heterogeneity and dynamic modelling of growth and stress response. In a common project, we will split up into four groups mimicking the specializations of different crop phenotyping experts like working out the basics in crop physiology, utilizing imaging sensors in the field, extracting targeted features from the image and processing the data to compare it with ground truth. The results of this collaborative research will be presented at the final field day after the end of the semester. At this day we will be in the field with experts to learn additional phenotyping aspects and the quantification of diseases. | |
| PD Dr. Andreas Hund, Dr. Beat Keller, PD Dr. J?rg Leipner, Dr. Afef Marzougui & Prof. Dr. Achim Walter | |
| 2 | |
| PhD Students Postdocs if places available | |
| The course will take place in Eschikon as it includes hands-on practice in the field. There will be an additional field day during the summer break in June. The course will be complementary with "PReSens – Proximal and Remote Sensing for Soil and Vegetation" taking place in Eschikon on Friday morning. | |
| English | |
| PhD students will take part in the MSc course 751-4106-00 G Crop Phenotyping. A reduced workload will allow to acquire 2 instead of the 4 ECTS points: Participants enrolled in the PSC are required to i) give a presentation, ii) participate in the group work carried out during the season, and iii) submit one of 5 exercises. | |
By registering you agree to the PSC course terms and conditions AGBs | |
| Cancellation of a course registration should be arranged with the course coordination office psc_phdprogram@ethz.ch and is possible free of charge up to 2 weeks before the course starts. Later cancellations and failure to attend or incomplete attendance without documented justification will incur a fee of 200 CHF. | |
| Dr. Bojan Gujas (psc_phdprogram@ethz.ch) | |
| FS26_Crop Phenotyping.pdfvertical_align_bottom |