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In plant science, it is an established method to obtain structural parameters of crops using image analysis. In recent years, deep learning techniques have improved the underlying processes significantly. However, since data acquisition is time and resource consuming, reliable training data are currently limited. To overcome this bottleneck, synthetic data are a promising option for not only enabling a higher order of correctness by offering more training data but also for validation of results. However, the creation of synthetic data is complex and requires extensive knowledge in Computer Graphics, Visualization and High-Performance Computing. We address this by introducing Synavis , a framework that allows users to train networks on real-time generated data. We created a pipeline that integrates realistic plant structures, simulated by the functional–structural plant model framework CPlantBox, into the game engine Unreal Engine. For this purpose, we needed to extend CPlantBox by introducing a new leaf geometrization that results in realistic leafs. All parameterized geometries of the plant are directly provided by the plant model. In the Unreal Engine, it is possible to alter the environment. WebRTC enables the streaming of the final image composition, which, in turn, can then be directly used to train deep neural networks to increase parameter robustness, for further plant trait detection and validation of original parameters. We enable user-friendly ready-to-use pipelines, providing virtual plant experiment and field visualizations, a python-binding library to access synthetic data and a ready-to-run example to train models. [ABSTRACT FROM AUTHOR]

Non‐invasive imaging of processes within the soil–plant continuum, particularly root and soil water distributions, can help optimize agricultural practices such as irrigation and fertilization. In this study, in‐situ time‐lapse horizontal crosshole ground penetrating radar (GPR) measurements and root images were collected over three maize crop growing seasons at two minirhizotron facilities (Selhausen, Germany). Root development and GPR permittivity were monitored at six depths (0.1–1.2 m) for different treatments within two soil types. We processed these data in a new way that gave us the information of the "trend‐corrected spatial permittivity deviation of vegetated field," allowing us to investigate whether the presence of roots increases the variability of GPR permittivity in the soil. This removed the main non‐root‐related influencing factors: static influences, such as soil heterogeneities and rhizotube deviations, and dynamic effects, such as seasonal moisture changes. This trend‐corrected spatial permittivity deviation showed a clear increase during the growing season, which could be linked with a similar increase in root volume fraction. Additionally, the corresponding probability density functions of the permittivity variability were derived and cross‐correlated with the root volume fraction, resulting in a coefficient of determination (R2) above 0.5 for 23 out of 46 correlation pairs. Although both facilities had different soil types and compaction levels, they had similar numbers of good correlations. A possible explanation for the observed correlation is that the presence of roots causes a redistribution of soil water, and therefore an increase in soil water variability. Core Ideas: Analying spatial and temporal belowground GPR signal variability and root images for maize crop roots.Root images and GPR data show differences for different treatments.Linking of root volume fraction and GPR permittivity variability. [ABSTRACT FROM AUTHOR]

Background and Aims: The global supply of phosphorus is decreasing. At the same time, climate change reduces the availability of water in most regions of the world. Insights on how decreasing phosphorus availability influences plant architecture are crucial to understanding its influence on plant functional properties, such as the root system's water uptake capacity., Methods: In this study, we investigated the structural and functional responses of Zea mays to varying phosphorus fertilization levels focusing especially on the root system's conductance. A rhizotron experiment with soils ranging from severe phosphorus deficiency to sufficiency was conducted. We measured the architectural parameters of the whole plant and combined them with root hydraulic properties to simulate time-dependent root system conductance of growing plants under different phosphorus levels., Key Results: We observed changes in the root system architecture, characterised by decreasing crown root elongation and reduced axial root radii with declining phosphorus availability. Modeling revealed that only plants with optimal phosphorus availability sustained a high root system conductance, while all other phosphorus levels led to a significantly lower root system conductance, both under light and severe phosphorus deficiency., Conclusion: We postulate that phosphorus deficiency decreases root system conductance, which could mitigate drought conditions through a more conservative water use strategy, but ultimately reduces biomass and impairs root development and overall water uptake capacity. Our results also highlight that the organisation of the root system, rather than its overall size, is critical for estimating important root functions., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Annals of Botany Company.)

Root systems of crops play a significant role in agroecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes, and a good soil structure. Minirhizotrons have shown to be effective to noninvasively investigate the root system. Root traits, like root length, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, is time-consuming and labor-intensive. Therefore, an objective method for high-throughput image analysis that provides data for field root phenotyping is necessary. In this study, we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample, is performed. Training and segmentation are done using "RootPainter." Then, an automated feature extraction from the segments is carried out by "RhizoVision Explorer." To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 36,500 images. Mainly the results show a high correlation ( r = 0.9) between manually and automatically determined root lengths. With respect to the processing time, our new pipeline outperforms manual annotation by 98.1-99.6%. Our pipeline, combining state-of-the-art software tools, significantly reduces the processing time for minirhizotron images. Thus, image analysis is no longer the bottle-neck in high-throughput phenotyping approaches., Competing Interests: The authors declare that there is no conflict of interest regarding the publication of this article., (Copyright © 2022 Felix Maximilian Bauer et al.)