Lukas Roth

Last Name

Roth

First Name

Lukas

ORCID

0000-0003-1435-9535

Organisational unit

03894 - Walter, Achim / Walter, Achim

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Publications 1 - 10 of 34

Analyse von perspektivischen Effekten in Drohnen-basierten RGB-Fotos zur Abschätzung der Blattfläche eines Pflanzenbestandes
Roth, Lukas; Aasen, Helge; Walter, Achim; et al. (2018)
Bornimer agrartechnische Berichte
Comparison of PhenoCams and drones for lean phenotyping of phenology and senescence of wheat genotypes in variety testing
Treier, Simon; Vuille-Dit-Bille, Nicolas; Visse-Mansiaux, Margot; et al. (2025)
The Plant Phenome Journal
In variety testing and breeding of wheat (Triticum aestivum L.), it is crucial to know the timing of phenological stages and the senescence behavior of genotypes to select for locally adapted varieties. Sound knowledge of the timing of phenological stages also allows for a more meaningful interpretation of measurements such as yield, quality, or disease ratings. In the presence of stresses, only a combined characterization of phenology and environmental conditions can allow for insights into unraveling stress resistance and stress avoidance. Capturing these traits visually in the field is very time-consuming. Here, a semimobile PhenoCam setup was used to track phenology and senescence from ear emergence to full maturity. PhenoCams mounted on field masts took images of wheat plot trials on a daily basis. In a partial least squares regression analysis, the temporal features of multiple vegetation indices were combined in one model to track phenology and senescence. The method was compared with visual reference methods and repeated drone flights with a multispectral camera. The Pearson’s correlation between visual reference methods and PhenoCam predictions was stronger than 0.8, often above 0.9, for most stages. An economic analysis showed that PhenoCams are economically interesting, especially for observing remote experimental sites. Thus, PhenoCams offer a cost-effective replacement for visual ratings of phenology and senescence, particularly in the context of multienvironmenttrials. 2025 Elsevier B.V., All rights reserved.
Trait spotting: Development of software routines and protocols for reliable remote phenotyping of field experiments and variety trials
Roth, Lukas; Aasen, Helge; Hund, Andreas (2019)
Bulletin SGPW/SSA ~ Diversität in der Forschung für einen vielfältigen Pflanzenbau
Development of drone-based phenotyping methodologies to support physiological plant breeding of wheat and soybean
Roth, Lukas (2021)
Extracting leaf area index using viewing geometry effects—A new perspective on high-resolution unmanned aerial system photography
Roth, Lukas; Aasen, Helge; Walter, Achim; et al. (2018)
ISPRS Journal of Photogrammetry and Remote Sensing
Towards Wheat Yield Estimation in Plant Breeding from Inhomogeneous Lidar Point Clouds Using Stochastic Features
Medic, Tomislav; Manser, Nicole; Kirchgessner, Norbert; et al. (2023)
International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
The world relies heavily on wheat, corn, and rice for nutrition, with global challenges such as population growth and climate change threatening food security. To tackle this, plant breeding, supported by digital technologies, focuses on improving food quality and quantity. Currently, crop yield estimation uses indirect observations through hyperspectral data and spectral indices, such as NDVI, which suffer from low sensitivity in breeding scenarios. Terrestrial laser scanners (TLS) present an alternative, allowing observations of the quantity and morphology of wheat ears from point clouds, which are directly linked to grain yield. However, exploiting these observations under field conditions presents challenges, mainly due to reduced resolution and non-homogenous properties of point clouds. In response, we propose an approach for in-field wheat yield estimation using machine learning and stochastic features of TLS point clouds that are specifically handcrafted to be less sensitive to the abovementioned phenomena. This approach avoids the need for explicit 3D reconstruction of individual plants and plant organs. Our initial results show limited success in yield estimation when posed as a regression problem. However, when framed as a classification problem focusing on detecting top- and bottom-performing plant phenotypes, we achieved a promising accuracy of 84.4% and AUC of 0.93. While encouraging, these are only the first results under relaxed conditions and further work is needed to enhance practical applicability.
Rethinking temperature effects on leaf growth, gene expression and metabolism: Diel variation matters
Kronenberg, Lukas; Yates, Steven; Ghiasi, Shiva; et al. (2021)
Plant, Cell & Environment
Plants have evolved to grow under prominently fluctuating environmental conditions. In experiments under controlled conditions, temperature is often set to artificial, binary regimes with constant values at day and at night. This study investigated how such a diel (24 hr) temperature regime affects leaf growth, carbohydrate metabolism and gene expression, compared to a temperature regime with a field‐like gradual increase and decline throughout 24 hr. Soybean (Glycine max) was grown under two contrasting diel temperature treatments. Leaf growth was measured in high temporal resolution. Periodical measurements were performed of carbohydrate concentrations, carbon isotopes as well as the transcriptome by RNA sequencing. Leaf growth activity peaked at different times under the two treatments, which cannot be explained intuitively. Under field‐like temperature conditions, leaf growth followed temperature and peaked in the afternoon, whereas in the binary temperature regime, growth increased at night and decreased during daytime. Differential gene expression data suggest that a synchronization of cell division activity seems to be evoked in the binary temperature regime. Overall, the results show that the coordination of a wide range of metabolic processes is markedly affected by the diel variation of temperature, which emphasizes the importance of realistic environmental settings in controlled condition experiments.
Phenomics data processing: A plot-level model for repeated measurements to extract the timing of key stages and quantities at defined time points
Roth, Lukas; Rodríguez-Álvarez, María Xosé; van Eeuwijk, Fred; et al. (2021)
Field Crops Research
Decision-making in breeding increasingly depends on the ability to capture and predict crop responses to changing environmental factors. Advances in crop modeling as well as high-throughput field phenotyping (HTFP) hold promise to provide such insights. Processing HTFP data is an interdisciplinary task that requires broad knowledge on experimental design, measurement techniques, feature extraction, dynamic trait modeling, and prediction of genotypic values using statistical models. To get an overview of sources of variation in HTFP, we develop a general plot-level model for repeated measurements. Based on this model, we propose a seamless step-wise procedure that allows for carry on of estimated means and variances from stage to stage. The process builds on the extraction of three intermediate trait categories; (1) timing of key stages, (2) quantities at defined time points or periods, and (3) dose-response curves. In a first stage, these intermediate traits are extracted from low-level traits’ time series (e.g., canopy height) using P-splines and the quarter of maximum elongation rate method (QMER), as well as final height percentiles. In a second and third stage, extracted traits are further processed using a stage-wise linear mixed model analysis. Using a wheat canopy growth simulation to generate canopy height time series, we demonstrate the suitability of the stage-wise process for traits of the first two above-mentioned categories. Results indicate that, for the first stage, the P-spline/QMER method was more robust than the percentile method. In the subsequent two-stage linear mixed model processing, weighting the second and third stage with error variance estimates from the previous stages improved the root mean squared error. We conclude that processing phenomics data in stages represents a feasible approach if estimated means and variances are carried forward from one processing stage to the next. P-splines in combination with the QMER method are suitable tools to extract timing of key stages and quantities at defined time points from HTFP data.
Temporal trends in canopy temperature and greenness are potential indicators of late-season drought avoidance and functional stay-green in wheat
Anderegg, Jonas; Aasen, Helge; Perich, Gregor; et al. (2021)
Field Crops Research
The ability to avoid dehydration is a drought resistance mechanism becoming increasingly more important even in temperate regions. In wheat, dehydration avoidance can be associated with a maintained canopy cooling during dry periods. However, in an average year under temperate conditions, drought periods are rather short which makes it difficult to routinely screen for drought avoidance using canopy temperature (CT). Furthermore, confounding factors such as differences in height, shoot biomass, canopy structure and phenology complicate the interpretation of differences in CT. We aimed to use temporal trends in CT and canopy greenness during short-term drought and heat events in the early grain filling phase to circumvent these problems. During this phase, evaporative demand is high and phenology-driven senescence has yet little effect on CT. Diverse sets of 354 and 71 wheat genotypes where grown in the field phenotyping platform of ETH Zurich in 2018 and 2019, respectively. CT was repeatedly measured during early grain filling by means of drone-based imaging. The temporal trends in CT during early grain filling showed a moderate to high within-year heritability (h2 = 0.35 and h2 = 0.88 in 2018 and 2019, respectively). These trends were largely independent of confounding factors when compared to single time point measurements and likely represent genotype-specific reactions to decreasing water availability more directly than absolute CT. CT trends were also largely independent of the temporal trends in stay-green indices. Therefore, we used a combination of time-resolved CT and stay-green trends to identify genotypes combining both traits. Significant differences were observed in the combined time-resolved index among three replicated check varieties. We therefore propose to use the combined time-resolved index to identify genotypes with improved drought avoidance and functional stay green.
DyMEP: R package for weather data-based phenology prediction for ten crops
Tschurr, Flavian; Walter, Achim; Roth, Lukas (2025)
Computers and Electronics in Agriculture
For most digital agriculture applications, such as in-season yield predictions, information on crop phenology is a prerequisite. Phenology is largely determined by environmental factors, e.g., temperature, precipitation, and global radiation. Consequently, weather data can be used to predict phenology. Here, we introduce the R package Dynamic Multi-Environmental Phenology (DyMEP) that facilitates such predictions. DyMEP was trained for ten crops, among others winter wheat, spring wheat, barley, green peas, beans and oat, with a large dataset representing the Central European climate. DyMEP fills the gap between complex, highly parameterized crop growth models that are difficult to use by non-experts, and extremely simplified models such as the Growing Degree Day approach. By carefully selecting the environmental covariates to use for each phenological phase, the user can reach suitable prediction accuracy for most applications in DyMEP. If temperature, precipitation, relative humidity, and global radiation are available as covariates to select from, the package achieves absolute errors ranging from 0 to 6 days across all applied phenology phases and root mean square errors ranging from 7 to 17 days on an independent test set. Combining DyMEP-based phenology predictions with ground-based or remote sensing observations holds promise to facilitate digital agriculture applications such as large-scale yield forecasting or monitoring of fields for crop insurance.

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Lukas Roth

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