4‐yr monitoring of soil water content dynamics for different plant types. Horizontal crosshole GPR at the field‐plot scale sampled ∼65 m3. Natural, irrigated, and sheltered treatments in gravelly and clayey–silty soils. Consistent patches of soil water content were observed along horizontal slices using GPR. Results enable improved investigation of soil–plant–atmosphere interactions. Ground penetrating radar (GPR) has shown a high potential to derive soil water content (SWC) at different scales. In this study, we combined multiple horizontal GPR measurements at different depths to investigate the spatial and temporal variability of the SWC under cropped plots. The SWC data were analyzed for four growing seasons between 2014 and 2017, two soil types (gravelly and clayey–silty), two crops (wheat [Triticum aestivum L.] and maize [Zea mays L.]), and three different water treatments. We acquired more than 150 time‐lapse GPR datasets along 6‐m‐long horizontal crossholes at six depths. The GPR SWC distributions are distinct both horizontally and vertically for both soil types. A clear change in SWC can be observed at both sites between the surface layer (>0.3 m) and subsoil. Alternating patches of higher and lower SWC, probably caused by the soil heterogeneity, were observed along the horizontal SWC profiles. To investigate the changes in SWC with time, GPR and time‐domain reflectometry (TDR) data were averaged for each depth and compared with changes in precipitation, treatment, and soil type. The high‐temporal‐resolution TDR and the large‐sampling‐volume GPR show similar trends in SWC for both sites, but because of the different sensing volumes, different responses were obtained due to the spatial heterogeneity. A difference in spatial variation of the crosshole GPR SWC data was detected between maize and wheat. The results for this 4‐yr period indicate the potential of this novel experimental setup to monitor spatial and temporal SWC changes that can be used to study soil–plant–atmosphere interactions.

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.

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