A new method for the rapid characterization of root growth and distribution using digital image correlation

Deep learning-based association analysis of root image data and cucumber yield

The root system is important for the absorption of water and nutrients by plants. Cultivating and selecting a root system architecture (RSA) with good adaptability and ultrahigh productivity have become the primary goals of agricultural improvement. Exploring the correlation between the RSA and crop yield is important for cultivating crop varieties with high-stress resistance and productivity. In this study, 277 cucumber varieties were collected for root system image analysis and yield using germination plates and greenhouse cultivation. Deep learning tools were used to train ResNet50 and U-Net models for image classification and segmentation of seedlings and to perform quality inspection and productivity prediction of cucumber seedling root system images. The results showed that U-Net can automatically extract cucumber root systems with high quality (F1_score ≥ 0.95), and the trained ResNet50 can predict cucumber yield grade through seedling root system image, with the highest F1_score reaching 0.86 using 10-day-old seedlings. The root angle had the strongest correlation with yield, and the shallow- and steep-angle frequencies had significant positive and negative correlations with yield, respectively. RSA and nutrient absorption jointly affected the production capacity of cucumber plants. The germination plate planting method and automated root system segmentation model used in this study are convenient for high-throughput phenotypic (HTP) research on root systems. Moreover, using seedling root system images to predict yield grade provides a new method for rapidly breeding high-yield RSA in crops such as cucumbers.

Evaluation of drought-tolerant varieties based on root system architecture in cotton (Gossypium hirsutum L.)

BACKGROUND

Root system architecture (RSA) exhibits significant genetic variability and is closely associated with drought tolerance. However, the evaluation of drought-tolerant cotton cultivars based on RSA in the field conditions is still underexplored.

RESULTS

So, this study conducted a comprehensive analysis of drought tolerance based on physiological and morphological traits (i.e., aboveground and RSA, and yield) within a rain-out shelter, with two water treatments: well-watered (75 ± 5% soil relative water content) and drought stress (50 ± 5% soil relative water content). The results showed that principal component analysis identified six principal components, including highlighting the importance of root traits and canopy parameters in influencing drought tolerance. Moreover, the systematic cluster analysis was used to classify 80 cultivars into 5 categories, including drought-tolerant cultivars, relatively drought-tolerant cultivars, intermediate cultivars, relatively drought-sensitive cultivars, and drought-sensitive cultivars. Further validation of the drought tolerance index showed that the yield drought tolerance index and biomass drought tolerance index of the drought-tolerant cultivars were 8.97 and 5.05 times higher than those of the drought-sensitive cultivars, respectively.

CONCLUSIONS

The RSA of drought-tolerant cultivars was characterised by a significant increase in average length-all lateral roots, a significant decrease in average lateral root emergence angle and a moderate root/shoot ratio. In contrast, the drought-sensitive cultivars showed a significant decrease in average length-all lateral roots and a significant increase in both average lateral root emergence angle and root/shoot ratio. It is therefore more comprehensive and accurate to assess field crop drought tolerance by considering root performance.

Transcriptome and metabolome profiling of interspecific CSSLs reveals general and specific mechanisms of drought resistance in cotton

In order to understand the molecular mechanism of cotton's response to drought during the flowering and boll stage, transcriptomics and metabolomics were carried out for two introgression lines (drought-tolerant line: T307; drought-sensitive line: S48) which were screened from Gossypium hirsutum cv. 'Emian22' with some gene fragments imported from Gossypium barbadense acc. 3-79, under drought stress by withdrawing water at flowering and boll stage. Results showed that the basic drought response in cotton included a series of broad-spectrum responses, such as amino acid synthesis, hormone (abscisic acid, ABA) signal transduction, and mitogen-activated protein kinases signal transduction pathway, which activated in both drought-tolerant and drought-sensitive lines. However, the difference of their imported fragments and diminished sequences triggers endoplasmic reticulum (ER) protein processing, photosynthetic-related pathways (in leaves), and membrane solute transport (in roots) in drought-tolerant line T307, while these are missed or not activated in drought-sensitive line S48, reflecting the different drought tolerance of the two genotypes. Virus-induced gene silencing assay of drought-tolerant differentially expressed heat shock protein (HSP) genes (mainly in leaf) and ATP-binding cassette (ABC) transporter genes (mainly in roots) indicated that those genes play important role in cotton drought tolerant. Combined analysis of transcriptomics and metabolomics highlighted the important roles of ER-stress-related HSP genes and root-specific ABC transporter genes in plants drought tolerance. These results provide new insights into the molecular mechanisms underlying the drought stress adaptation in cotton.

Complete root specimen of plants grown in soil-filled root box: sampling, measuring, and staining method

BACKGROUND

Detailed datasets containing root system and its architecture in soil are required to improve understanding of resource capture by roots. However, most of the root study methods have paid little attention to make and preserve whole root specimens. This study introduces root system sampling equipment that makes the entire root specimen with minimum impairment and without displacement of the spatial arrangement of the root system in root boxes. The objectives are to assess: whether the equipment can rapidly sample the entire root system; whether root surface area is measurable from a scanned digital image of the root specimen; and whether staining of the entire root specimens would provide multidimensional visual information on the interaction between soil and physiological function of root system architecture (RSA). For validation, we examined the root response of two soybean cultivars to arbuscular mycorrhizal (AM) inoculation and the effect of waterlogging stress on the physiological activity of buckwheat RSA.

RESULTS

The root boxes allowed soybean and buckwheat plants to grow uniformly across the replications. Both species showed significant differences between cultivars and/or among treatments in shoot and root traits. The equipment enabled to sample the whole-root specimens of soybean and buckwheat, where the tips of the fine roots were alive (diameter < 0.2 mm). Also, the whole root specimens of soybean were made in about 7 min. The root surface area calculated from the scanned soybean specimens showed a significant correlation with that calculated from the roots spread out in water (a common method). Staining of the soybean root specimens enabled us to observe the localized root proliferation induced by AM colonization. Moreover, staining of the buckwheat root specimens made it possible to examine the respiratory activity of each root at different depths.

CONCLUSIONS

The present method realized: fast and accurate production of the whole root specimen and precise calculation of the specimens' root surface area. Moreover, staining of the root specimens enabled analyzing the interaction between soil and physiological function of RSA. The evaluation of root traits, using our methods, will contribute to developing agronomic management and breeding program for sustainable food production.

Effects of neighbour location and nutrient distributions on root foraging behaviour of the common sunflower

Plants regularly encounter patchily distributed soil nutrients. A common foraging response is to proliferate roots within high-quality patches. The influence of the social environment on this behaviour has been given limited attention, despite important fitness consequences of competition for soil resources among plants. Using the common sunflower (Helianthus annuus L.), we compared localized root proliferation in a high-quality patch by plants grown alone to that of plants in two different social environments: with a neighbouring plant sharing equal access to the high-quality patch, and with a neighbouring plant present but farther from the high-quality patch such that the focal individual was in closer proximity to the high-quality patch. Sunflowers grown alone proliferated more roots within high-nutrient patches than lower-nutrient soil. Plants decreased root proliferation within a high-nutrient patch when it was equidistant to a neighbour. Conversely, plants increased root proliferation when they were in closer proximity to the patch relative to a nearby neighbour. Such contingent responses may allow sunflowers to avoid competition in highly contested patches, but to also pre-empt soil resources from neighbours when they have better access to a high-quality patch. We also compared patch occupancy by sunflowers grown alone with two equidistant high-quality patches to occupancy by sunflowers grown with two high-quality patches and a neighbour. Plants grown with a neighbour decreased root length within shared patches but did not increase root length within high-quality patches they were in closer proximity to, perhaps because resource pre-emption may be less important for individuals when resources are more abundant. These results show that nutrient foraging responses in plants can be socially contingent, and that plants may account for the possibility of pre-empting limited resources in their foraging decisions.