Biology subjects

Robles-Zazueta, C. A.

Publications and source records attributed to Robles-Zazueta, C. A..

5 recordsLinked to original sources

bioRxiv2026

Leaf and cluster spectral signatures reveal trait-dependent prediction performance for grapevine cluster architecture and juice quality

Grapevine cluster architecture is a key selection target in breeding programs because it influences disease susceptibility, yield stability and juice quality. High-throughput phenotyping offers a rapid and non-destructive approach to capture biochemical and structural variation in these traits, yet the influence of plant organ reflectance and data partitioning strategies on trait prediction remains poorly understood. In this study, we evaluated how hyperspectral reflectance from different grapevine organs contributes to the prediction of cluster architecture and juice quality traits in two clonal populations of Riesling and Pinot. Using partial least squares regression (PLSR), we assessed the prediction accuracy of eight cluster architecture and six juice quality traits under two data partitioning strategies. Models based on cluster reflectance outperformed those using dry leaf reflectance for most traits, except for pH. Partitioning the dataset by cluster type increased trait variance and improved predictions for number of berries (R{superscript 2} = 0.53), berry diameter (R{superscript 2} = 0.79), and total acidity (R{superscript 2} = 0.48). Visible, red-edge and NIR spectra were most informative regions to predict the traits studied. Together, our results highlight the importance of organ-specific data and appropriate calibration strategies to improve phenomic models for the development of scalable proxies for grapevine improvement. HighlightSpectral phenomics reveals that prediction accuracy in grapevine depends on organ spectral signatures and traits, with cluster reflectance outperforming leaves, informing new phenotyping strategies for breeding improvement.

plant biology↗

bioRxiv2025

Challenges and opportunities in detecting leaf water and carotenoid content across biomes from satellite multispectral indices

Climate change is causing vegetation stress across the globe, increasing the need for reliable indicators to monitor plant health. Leaf water and carotenoid content, and the chlorophyll/carotenoid ratio, are established proxies for environmental stress that can be detected by remote sensing. Here, we evaluated the sensitivity of 11 multispectral vegetation indices (VIs) designed to monitor these three stress-related leaf traits across a broad range of environmental and vegetation conditions. For this, we combined radiative transfer modeling with cross-biome field and satellite observations from Sentinel-2, Landsat 8, and MODIS from the National Ecological Observatory Network (NEON), spanning in most major terrestrial ecosystems. Our model-based analysis showed that VIs have a low to moderate sensitivity to their target traits, ranging from water indices with 66% of their variability explained by leaf water content, to carotenoid indices with 27% variability explained by leaf carotenoid content. Surprisingly, our field-based analyses revealed minimal to no sensitivity to leaf water and carotenoid content and chlorophyll/carotenoid ratio across all VIs. In contrast, we showed that leaf area index was the dominant driver of all studied VIs, accounting for 54-74 % of their variability in the field-based analysis. Lastly, we detected that VI[s] sensitivity to atmospheric conditions and field sampling issues contribute to their low performance in validating ground truth observations. These findings show that improvements in the VIs formulation and field sampling strategies are needed to increase the reliability of vegetation stress monitoring from multispectral satellites and support a generalized use of VIs across ecosystems. Highlights: 3-5 bullet points, 85 characters[bullet] Sensitivity of water and carotenoid multispectral indices was evaluated [bullet]Analysis based on cross-biome field data and radiative transfer models [bullet]Field data showed indices had minimal sensitivity to leaf water and carotenoid [bullet]Leaf area index explained most cross-biome variation in water and carotenoid indices [bullet]We propose strategies to improve stress-related index formulation and validation

ecology↗

bioRxiv2025

Yield from the shadows: beyond top layer photosynthesis to enhance crop productivity

Photosynthesis research in crops typically focuses on upper canopy layers, which is partly for convenience and partly for the sake of achieving stable datasets under high light conditions. This neglects significant contributions from light - limited portions of the canopy within the lower layers. This study aimed to provide an empirical quantification of the role of these hidden layers of wheat canopies in the context of canopy scale productivity. We demonstrate that light-saturated photosynthetic rates (Asat) in middle and bottom layers at key growth stages can be strong predictors of grain yield. Despite variability in architecture across layers, light interception remained similar and key associations between biomass accumulation and yield with Asat emerged. Yield showed positive associations with photosynthesis in all canopy layers but was stronger at the top layer during grain filling and at the bottom layer during booting. Whole canopy photosynthetic rates were influenced by top layer architecture, N availability in the middle and bottom layers and leaf angles at the bottom of the canopy. Our findings suggest that measurements within hidden layers are required, and that optimizing middle and bottom layer Asat during the vegetative period and top layer Asat during grain filling can boost food security.

ecology↗

bioRxiv2025

The Global Wheat Full Semantic Organ Segmentation (GWFSS) dataset

Computer vision is increasingly used in farmers fields and agricultural experiments to quantify important traits. Imaging setups with a sub-millimetre ground sampling distance enable the detection and tracking of plant features, including size, shape, and colour. Although todays AI-driven foundation models segment almost any object in an image, they still fail for complex plant canopies. To improve model performance, the global wheat dataset consortium assembled a diverse set of images from experiments around the globe. After the head detection dataset (GWHD), the new dataset targets a full semantic segmentation (GWFSS) of wheat organs (leaves, stems and spikes) covering all developmental stages. Images were collected by 11 institutions using a wide range of imaging setups. Two datasets are provided: i) a set of 1096 diverse images in which all organs were labelled at the pixel level, and (ii) a dataset of 52,078 images without annotations available for additional training. The labelled set was used to train segmentation models based on DeepLabV3Plus and Segformer. Our Segformer model performed slightly better than DeepLabV3Plus with a mIOU for leaves and spikes of ca. 90%. However, the precision for stems with 54% was rather lower. The major advantages over published models are: i) the exclusion of weeds from the wheat canopy, ii) the detection of all wheat features including necrotic and senescent tissues and its separation from crop residues. This facilitates further development in classifying healthy vs. unhealthy tissue to address the increasing need for accurate quantification of senescence and diseases in wheat canopies.

plant biology↗

bioRxiv2024

An ecological, phenotypic and genomic survey of duckweeds with their associated aquatic environments in the United Kingdom

The duckweeds feature global distributions and diverse applications in phytoremediation and nutrition, as well as use in fundamental studies of development. Existing collections have minimal environmental data linked to natural habitats. Thus, there is a lack of understanding of natural variation in the context of native habitats. Here, a novel collection of 124 duckweed accessions from 115 sites across the United Kingdom were characterised by genome sequencing and ionomics. In nutrient-replete conditions all accessions hyperaccumulated P, K, Mg and Ca. Local but not large-scale associations were revealed between elemental composition of duckweed in common, replete conditions and native water profiles. Lemna minor was the most prevalent species in the UK, with a closely related hybrid L. japonica frequently found in waters with higher micronutrient concentrations. Invasive L. minuta was common in the southern and midland regions, but restricted in Scotland. Lemna accessions accumulated heavy metal contaminants typically together with macronutrients, suggesting phytoremediation potential, but some limitations as food. Furthermore, monitoring the ecological interactions between native, hybrid and invasive Lemna species should be ongoing in the interest of biodiversity.

ecology↗