Background: Improving abiotic stress tolerance in wheat requires large scale screening of yield components such as seed weight, seed number and single seed weight, all of which is very laborious, and a detailed analysis of seed morphology is time-consuming and visually often impossible. Computed tomography offers the opportunity for much faster and more accurate assessment of yield components. Results: An X-ray computed tomographic analysis was carried out on 203 very diverse wheat accessions which have been exposed to either drought or combined drought and heat stress. Results demonstrated that our computed tomography pipeline was capable of evaluating grain set with an accuracy of 95-99%. Most accessions exposed to combined drought and heat stress developed smaller, shrivelled seeds with an increased seed surface. As expected, seed weight and seed number per ear as well as single seed size were significantly reduced under combined drought and heat compared to drought alone. Seed weight along the ear was significantly reduced at the top and bottom of the wheat spike. Conclusions: We were able to establish a pipeline with a higher throughput with scanning times of 7 min per ear and accuracy than previous pipelines predicting a set of agronomical important seed traits and to visualize even more complex traits such as seed deformations. The pipeline presented here could be scaled up to use for high throughput, high resolution phenotyping of tens of thousands of heads, greatly accelerating breeding efforts to improve abiotic stress tolerance.

Background. Computed X-ray tomography (CTX) is a high-end nondestructive approach for the visual assessment of root architecture in soil. Nevertheless, in order to evaluate high-resolution CTX data of root architectures, manual segmentation of the depicted root systems from large-scale volume data is currently necessary, which is both time consuming and error prone. The duration of such a segmentation is of importance, especially for time-resolved growth analysis, where several instances of a plant need to be segmented and evaluated. Specifically, in our application, the contrast between soil and root data varies due to different growth stages and watering situations at the time of scanning. Additionally, the root system itself is expanding in length and in the diameter of individual roots. Objective. For semiautomated and robust root system segmentation from CTX data, we propose the RootForce approach, which is an extension of Frangi’s “multi-scale vesselness” method and integrates a 3D local variance. It allows a precise delineation of roots with diameters down to several μm in pots with varying diameters. Additionally, RootForce is not limited to the segmentation of small below-ground organs, but is also able to handle storage roots with a diameter larger than 40 voxels. Results. Using CTX volume data of full-grown bean plants as well as time-resolved (3D+time) growth studies of cassava plants, RootForce produces similar (and much faster) results compared to manual segmentation of the regarded root architectures. Furthermore, RootForce enables the user to obtain traits not possible to be calculated before, such as total root volume (Vroot), total root length (Lroot), root volume over depth, root growth angles (θmin, θmean, and θmax), root surrounding soil density Dsoil, or form fraction F. Discussion. The proposed RootForce tool can provide a higher efficiency for the semiautomatic high-throughput assessment of the root architectures of different types of plants from large-scale CTX. Furthermore, for all datasets within a growth experiment, only a single set of parameters is needed. Thus, the proposed tool can be used for a wide range of growth experiments in the field of plant phenotyping.

XXL-Computed Tomography (XXL-CT) is able to produce large scale volume datasets of scanned objects such as crash tested cars, sea and aircraft containers or cultural heritage objects. The acquired image data consists of volumes of up to and above $$\hbox {10,000}^{3}$$ 10,000 3 voxels which can relate up to many terabytes in file size and can contain multiple 10,000 of different entities of depicted objects. In order to extract specific information about these entities from the scanned objects in such vast datasets, segmentation or delineation of these parts is necessary. Due to unknown and varying properties (shapes, densities, materials, compositions) of these objects, as well as interfering acquisition artefacts, classical (automatic) segmentation is usually not feasible. Contrarily, a complete manual delineation is error-prone and time-consuming, and can only be performed by trained and experienced personnel. Hence, an interactive and partial segmentation of so-called “chunks” into tightly coupled assemblies or sub-assemblies may help the assessment, exploration and understanding of such large scale volume data. In order to assist users with such an (possibly interactive) instance segmentation for the data exploration process, we propose to utilize delineation algorithms with an approach derived from flood filling networks. We present primary results of a flood filling network implementation adapted to non-destructive testing applications based on large scale CT from various test objects, as well as real data of an airplane and describe the adaptions to this domain. Furthermore, we address and discuss segmentation challenges due to acquisition artefacts such as scattered radiation or beam hardening resulting in reduced data quality, which can severely impair the interactive segmentation results.

As a consequence of climate change, heat waves in combination with extended drought periods will be an increasing threat to crop yield. Therefore, breeding stress tolerant crop plants is an urgent need. Breeding for stress tolerance has benefited from large scale phenotyping, enabling non-invasive, continuous monitoring of plant growth. In case of potato, this is compromised by the fact that tubers grow belowground, making phenotyping of tuber development a challenging task. To determine the growth dynamics of tubers before, during and after stress treatment is nearly impossible with traditional destructive harvesting approaches. In contrast, X-ray Computed Tomography (CT) offers the opportunity to access belowground growth processes. In this study, potato tuber development from initiation until harvest was monitored by CT analysis for five different genotypes under stress conditions. Tuber growth was monitored three times per week via CT analysis. Stress treatment was started when all plants exhibited detectable tubers. Combined heat and drought stress was applied by increasing growth temperature for 2 weeks and simultaneously decreasing daily water supply. CT analysis revealed that tuber growth is inhibited under stress within a week and can resume after the stress has been terminated. After cessation of stress, tubers started growing again and were only slightly and insignificantly smaller than control tubers at the end of the experimental period. These growth characteristics were accompanied by corresponding changes in gene expression and activity of enzymes relevant for starch metabolism which is the driving force for tuber growth. Gene expression and activity of Sucrose Synthase (SuSy) reaffirmed the detrimental impact of the stress on starch biosynthesis. Perception of the stress treatment by the tubers was confirmed by gene expression analysis of potential stress marker genes whose applicability for potato tubers is further discussed. We established a semi-automatic imaging pipeline to analyze potato tuber delevopment in a medium thoughput (5 min per pot). The imaging pipeline presented here can be scaled up to be used in high-throughput phenotyping systems. However, the combination with automated data processing is the key to generate objective data accelerating breeding efforts to improve abiotic stress tolerance of potato genotypes.

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BackgroundThe non-invasive 3D-imaging and successive 3D-segmentation of plant root systems has gained interest within fundamental plant research and selectively breeding resilient crops. Currently the state of the art consists of computed tomography (CT) scans and reconstruction followed by an adequate 3D-segmentation process.ChallengeGenerating an exact 3D-segmentation of the roots becomes challenging due to inhomogeneous soil composition, as well as high scale variance in the root structures themselves.Approach(1) We address the challenge by combining deep convolutional neural networks (DCNNs) with a weakly supervised learning paradigm. Furthermore, (2) we apply a spatial pyramid pooling (SPP) layer to cope with the scale variance of roots. (3) We generate a fine-tuned training data set with a specialized sub-labeling technique. (4) Finally, to yield fast and high-quality segmentations, we propose a specialized iterative inference algorithm, which locally adapts the field of view (FoV) for the network.ExperimentsWe compare our segmentation results against an analytical reference algorithm for root segmentation (RootForce) on a set of roots from Cassava plants and show qualitatively that an increased amount of root voxels and root branches can be segmented.ResultsOur findings show that with the proposed DCNN approach combined with the dynamic inference, much more, and especially fine, root structures can be detected than with a classical analytical reference method.ConclusionWe show that the application of the proposed DCNN approach leads to better and more robust root segmentation, especially for very small and thin roots.

Despite the importance of storage root (SR) organs for cassava and the other root crops yield, their developmental origin is poorly understood. Here we use multiple approaches to shed light on the initial stages of root development demonstrating that SR and fibrous roots (FR) follow different rhizogenic processes. Transcriptome analysis carried out on roots collected before, during and after root bulking highlighted early and specific activation of a number of functions essential for root swelling and identified root‐specific genes able to effectively discriminate emerging FR and SR. Starch and sugars start to accumulate at a higher rate in SR before they swell but only after parenchyma tissue has been produced. Finally, using non‐destructive computed tomography measurements, we show that SR (but not FR) contain, since their emergence from the stem, an inner channel structure in continuity with the stem secondary xylem, indicating that SR derive from a distinct rhizogenic process compared with FR.

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