Background: Roots are vital to plant performance because they acquire resources from the soil and provide anchorage. However, it remains difficult to assess root system size and distribution because roots are inaccessible in the soil. Existing methods to phenotype entire root systems range from slow, often destructive, methods applied to relatively small numbers of plants in the field to rapid methods that can be applied to large numbers of plants in controlled environment conditions. Much has been learned recently by extensive sampling of the root crown portion of field-grown plants. But, information on large-scale genetic and environmental variation in the size and distribution of root systems in the field remains a key knowledge gap. Minirhizotrons are the only established, non-destructive technology that can address this need in a standard field trial. Prior experiments have used only modest numbers of minirhizotrons, which has limited testing to small numbers of genotypes or environmental conditions. This study addressed the need for methods to install and collect images from thousands of minirhizotrons and thereby help break the phenotyping bottleneck in the field. Results: Over three growing seasons, methods were developed and refined to install and collect images from up to 3038 minirhizotrons per experiment. Modifications were made to four tractors and hydraulic soil corers mounted to them. High quality installation was achieved at an average rate of up to 84.4 minirhizotron tubes per tractor per day. A set of four commercially available minirhizotron camera systems were each transported by wheelbarrow to allow collection of images of mature maize root systems at an average rate of up to 65.3 tubes per day per camera. This resulted in over 300,000 images being collected in as little as 11 days for a single experiment. Conclusion: The scale of minirhizotron installation was increased by two orders of magnitude by simultaneously using four tractor-mounted, hydraulic soil corers with modifications to ensure high quality, rapid operation. Image collection can be achieved at the corresponding scale using commercially available minirhizotron camera systems. Along with recent advances in image analysis, these advances will allow use of minirhizotrons at unprecedented scale to address key knowledge gaps regarding genetic and environmental effects on root system size and distribution in the field.

Gibberellins (GA) are the decisive players in seed germination whose functionality could be adversely affected by the presence of cadmium (Cd); however, the underlying mechanisms are unclear. Eco-toxicological effects of Cd (0, 25, 50, 75, and 100 µM) on the early stages of ontogenesis in a mung bean variety (ML-2056) were investigated. Seed germination characteristics along with Cd-tolerance index were recorded at the seventh day of germination. Additionally, endogenous gibberellic acid (GA3) level, amylase activity, oxidative stress, and the antioxidant defense system were also investigated in Cd-stressed germinating seedlings. Results revealed that Cd reduced seed germination and interfered with GA synthesis in a concentration-dependent manner. Further, to validate the role of GA in Cd-tolerance, experiments were executed to explore the effect of seed priming with GA3 and its biosynthesis inhibitor paclobutrazol (PBZ) on ML-2056 under Cd stress. Application of GA3 improved the activities of amylase and carbohydrate-metabolizing enzymes, the antioxidant defense system, and sustained lower H₂O₂ and TBARS contents, whereas PBZ caused a significant reduction in growth and decreased endogenous GA3 content in Cd-stressed ML-2056, suggesting a crucial role of GA synthesis in reversing Cd-induced negative effects. Overall, GA synthesis played a crucial role in mitigating Cd toxicity in mung bean, which might be used as a criterion for developing Cd-stress-tolerant genotypes.

Image segmentation is commonly used to estimate the location and shape of plants and their external structures. Segmentation masks are then used to localize landmarks of interest and compute other geometric features that correspond to the plant’s phenotype. Despite its prevalence, segmentation-based approaches are laborious (requiring extensive annotation to train) and error-prone (derived geometric features are sensitive to instance mask integrity). Here, we present a segmentation-free approach that leverages deep learning-based landmark detection and grouping, also known as pose estimation. We use a tool originally developed for animal motion capture called SLEAP (Social LEAP Estimates Animal Poses) to automate the detection of distinct morphological landmarks on plant roots. Using a gel cylinder imaging system across multiple species, we show that our approach can reliably and efficiently recover root system topology at high accuracy, few annotated samples, and faster speed than segmentation-based approaches. In order to make use of this landmark-based representation for root phenotyping, we developed a Python library (sleap-roots) for trait extraction directly comparable to existing segmentation-based analysis software. We show that pose-derived root traits are highly accurate and can be used for common downstream tasks including genotype classification and unsupervised trait mapping. Altogether, this work establishes the validity and advantages of pose estimation-based plant phenotyping. To facilitate adoption of this easy-to-use tool and to encourage further development, we make sleap-roots, all training data, models, and trait extraction code available at: https://github.com/talmolab/sleap-roots and https://osf.io/k7j9g/.

• Consistent QTLs linked to phenology, yield traits were detected using rice lines derived from a local landrace, Nootripathu under rainfed TPE.• Consistent yield QTL on chromosome 1 was further re... Drought stress is a major constraint for rice production in rainfed ecosystems. Landraces are reservoir of genes that can help in breeding drought tolerant cultivars. Utilizing the genetic potentia...

A total of 36 colocated QTLs associated with plant production traits were mapped using IR62266/Norungan recombinant inbred lines under drought and non-stress conditions in target populations of environment. Multi-model composite interval mapping and multi-environment analysis detected seven consistent QTLs for plant production traits on chromosomes 1, 2, 3, 6, 10 and 11. QTLs for plant height, number of tillers, biomass, spikelet fertility and grain yield under drought identified on chr. 1 (RM5389-RM11943-RM5794), chr. 2 (RM324-RM5390 and RM12460-RM423-RM5345), chr. 3 (RM6329-RM5475-RM16030), chr. 6 (RM508-RM585-RM217), chr. 10 (RM7361-RM8207) and chr. 11 (RM209-RM6499) in this study have been reported as meta-QTLs for yield under drought. Consistent positive allelic contribution from the landrace, Norungan was shown at the two QTLs on chr. 2, while IR62266 showed consistent positive allelic effect at the QTL on chr. 10. At other four regions the effect was confounded with the stress condition, for instance the region on chr. 3 identified for straw yield showed additive effect from Norungan in all stress trials, while in the non-stress trials the positive allele came from IR62266. Epistasis for grain yield was detected in the regions of non-additive effects and its relative contribution was small. Relatively small percentage of grain yield variation was explained by additive x environment interaction (0.28 - 1.77%), but was larger than that explained by additive effect QTL across environments (0.05 - 0.35%). These consistent QTLs may have genes evolutionarily conserved in response to drought and could be ideal candidates for rice yield improvement in water-limited environments. GRAPHICAL ABSTRACT