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Data on transgenerational memory effects of photosynthetic efficiency of twelve wheat varieties under elevated carbon dioxide concentration and reduced soil water availability
(2025) Berauer, Bernd J.; Chaudhary, Suraj; Kottmann, Lorenz; Schweiger, Andreas H.
This data represents ACi curves of twelve winter wheat varieties, which were grown under elevated and ambient CO2 concentrations within a FACE experiment and the subsequent F1 generation was exposed to ambient and elevated CO2 concentrations in a highly controlled environment using climate chambers. The 12 winter wheat genotypes (Triticum aestivum L.) were selected based on their susceptibilty to leaf rust (Puccinia triticina Eriks.) and Fusarium head blight (Fusarium graminearum Schwabe) according to the descriptive variety list of the German Federal Office of Plant Varietes (Beschreibende Sortenliste, Bundessortenamt 2024). The aim was to obtain a diverse set of varieties with the widest possible range of susceptibilities to leaf rust and fusarium head blight. Photosynthesis was measured using the novel Dynamic Assimilation Technique, thus not with the common steady-state approach. The individual wheat plants were measured twice, once under saturating soil water availability (θFC) and once under reduced soil water availability (θcsoil). θcsoil represents the gravimetric water content when the soil matric potential drops below the root matric potential, thus the onset of plant drought stress (sensu Cai et al. [2]). The photosynthesis data was used to fit ACi curves and extract the maximum Rubisco carboxylation rate [Vcmax], maximum rate of electron transport [Jmax] and dark respiration [Rd]. At both measurements we determined BBCH and plant height to quantify plant morphological development, as well as leaf water potential to quantify plant ecohydrologic status. At the end of the experiment, biomass was harvested and reported. Further, we provide environmental data of the climate chambers in use. Within the data repository, we provide comprehensive experimental data on the investigation of transgenerational memory effects on photosynthetic efficiency. We provide photosynthetic raw data as well as processed (merged) and derived (extracted ACi fit) data. Additionally, we provide the R-code to reproduce the calculation of the derived parameters. Data on transgenerational memory effects (that is, the influence of the parental environment on offspring phenotype and performance) are scarce, i.e. on the adaptive capacity of the photosynthetic apparatus. Thus, the data provided here can contribute to closing this gap. The highly controlled environment allows to closely investigate cause-effect relationships, thereby contributing to a mechanistic understanding of the transgenerational memory effects on photosynthetic efficiency and how this is altered by reduced soil water availability. By using a recently developed methodological approach, the data contributes to further investigate the quality of the method and establish it within the field of plant ecophysiology.
A novel dataset of annotated oyster mushroom images with environmental context for machine learning applications
(2024) Duman, Sonay; Elewi, Abdullah; Hajhamed, Abdulsalam; Khankan, Rasheed; Souag, Amina; Ahmed, Asma
State-of-the-art technologies such as computer vision and machine learning, are revolutionizing the smart mushroom industry by addressing diverse challenges in yield prediction, growth analysis, mushroom classification, disease and deformation detection, and digital twinning. However, mushrooms have long presented a challenge to automated systems due to their varied sizes, shapes, and surface characteristics, limiting the effectiveness of technologies aimed at mushroom classification and growth analysis. Clean and well-labelled datasets are therefore a cornerstone for developing efficient machine-learning models. Bridging this gap in oyster mushroom cultivation, we present a novel dataset comprising 555 high-quality camera raw images, from which approximately 16.000 manually annotated images were extracted. These images capture mushrooms in various shapes, maturity stages, and conditions, photographed in a greenhouse using two cameras for comprehensive coverage. Alongside the images, we recorded key environmental parameters within the mushroom greenhouse, such as temperature, relative humidity, moisture, and air quality, for a holistic analysis. This dataset is unique in providing both visual and environmental time-point data, organized into four storage folders: “Raw Images”; “Mushroom Labelled Images and Annotation Files”; “Maturity Labelled Images and Annotation Files”; and “Sensor Data”, which includes time-stamped sensor readings in Excel files. This dataset can enable researchers to develop high-quality prediction and classification machine learning models for the intelligent cultivation of oyster mushrooms. Beyond mushroom cultivation, this dataset also has the potential to be utilized in the fields of computer vision, artificial intelligence, robotics, precision agriculture, and fungal studies in general.

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