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Site-specific crop management in heterogeneous fields has emerged as a promising avenue towards increasing agricultural productivity whilst safeguarding the environment. However, successful implementation is hampered by insufficient availability of accurate spatial information on crop growth, vigor, and health status at large scales. Challenges persist particularly in interpreting remote sensing signals within commercial crop production due to the variability in canopy appearance resulting from diverse factors. Recently, high-resolution imagery captured from unmanned aerial vehicles has shown significant potential for calibrating and validating methods for remote sensing signal interpretation. We present a comprehensive multi-scale image dataset encompassing 35,000 high-resolution aerial RGB images, ground-based imagery, and Sentinel-2 satellite data from nine on-farm wheat fields in Switzerland. We provide geo-referenced orthomosaics, digital elevation models, and shapefiles, enabling detailed analysis of field characteristics across the growing season. In combination with rich meta data such as detailed records of crop husbandry, crop phenology, and yield maps, this data set enables key challenges in remote sensing-based trait estimation and precision agriculture to be addressed., (© 2024. The Author(s).)

Predicting plant development, a longstanding goal in plant physiology, involves 2 interwoven components: continuous growth and the progression of growth stages (phenology). Current models for winter wheat and soybean assume species-level growth responses to temperature. We challenge this assumption, suggesting that cultivar-specific temperature responses substantially affect phenology. To investigate, we collected field-based growth and phenology data in winter wheat and soybean over multiple years. We used diverse models, from linear to neural networks, to assess growth responses to temperature at various trait and covariate levels. Cultivar-specific nonlinear models best explained phenology-related cultivar-environment interactions. With cultivar-specific models, additional relations to other stressors than temperature were found. The availability of the presented field phenotyping tools allows incorporating cultivar-specific temperature response functions in future plant physiology studies, which will deepen our understanding of key factors that influence plant development. Consequently, this work has implications for crop breeding and cultivation under adverse climatic conditions., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2024 Lukas Roth et al.)

Abiotic stresses such as heat and frost limit plant growth and productivity. Image-based field phenotyping methods allow quantifying not only plant growth but also plant senescence. Winter crops show senescence caused by cold spells, visible as declines in leaf area. We accurately quantified such declines by monitoring changes in canopy cover based on time-resolved high-resolution imagery in the field. Thirty-six winter wheat genotypes were measured in multiple years. A concept termed "frost damage index" (FDI) was developed that, in analogy to growing degree days, summarizes frost events in a cumulative way. The measured sensitivity of genotypes to the FDI correlated with visual scorings commonly used in breeding to assess winter hardiness. The FDI concept could be adapted to other factors such as drought or heat stress. While commonly not considered in plant growth modeling, integrating such degradation processes may be key to improving the prediction of plant performance for future climate scenarios., (Copyright © 2023 Flavian Tschurr et al.)

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., (© 2020 The Authors. Plant, Cell & Environment published by John Wiley & Sons Ltd.)