Deep learning-based high-throughput phenotyping accelerates gene discovery for stomatal traits

Zhang, Wei; Calla, Bernarda; Thiruppathi, Dhineshkumar

doi:10.1093/plphys/kiab398

Cited by 6 publications

(2 citation statements)

References 10 publications

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“…Recent studies have demonstrated the effectiveness of deep learning algorithms in identifying small objects, such as the stomata density in sorghum (Bheemanahalli et al., 2021) and maize (Zhang, Calla, et al., 2021). However, the application of deep learning in detecting plant trichome remains relatively limited.…”

Section: Discussionmentioning

confidence: 99%

“…Glandular trichome density and type are widely controlled by various phytohormones, such as gibberellin (Liu et al., 2018), salicylic acid (Es‐sbihi et al., 2020), ethylene (Zhang, Calla, et al., 2021; Zhang, Shen, et al., 2021) and JA (Bosch et al., 2014). Among them, accumulating evidence indicates that JA is a crucial factor in trichome initiation and development (Wang, Liu, et al., 2021).…”

Section: Discussionmentioning

confidence: 99%

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Deep learning‐based quantification and transcriptomic profiling reveal a methyl jasmonate‐mediated glandular trichome formation pathway in Cannabis sativa

Huang,

Chen,

Zhao

et al. 2024

The Plant Journal

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SUMMARYCannabis glandular trichomes (GTs) are economically and biotechnologically important structures that have a remarkable morphology and capacity to produce, store, and secrete diverse classes of secondary metabolites. However, our understanding of the developmental changes and the underlying molecular processes involved in cannabis GT development is limited. In this study, we developed Cannabis Glandular Trichome Detection Model (CGTDM), a deep learning‐based model capable of differentiating and quantifying three types of cannabis GTs with a high degree of efficiency and accuracy. By profiling at eight different time points, we captured dynamic changes in gene expression, phenotypes, and metabolic processes associated with GT development. By integrating weighted gene co‐expression network analysis with CGTDM measurements, we established correlations between phenotypic variations in GT traits and the global transcriptome profiles across the developmental gradient. Notably, we identified a module containing methyl jasmonate (MeJA)‐responsive genes that significantly correlated with stalked GT density and cannabinoid content during development, suggesting the existence of a MeJA‐mediated GT formation pathway. Our findings were further supported by the successful promotion of GT development in cannabis through exogenous MeJA treatment. Importantly, we have identified CsMYC4 as a key transcription factor that positively regulates GT formation via MeJA signaling in cannabis. These findings provide novel tools for GT detection and counting, as well as valuable information for understanding the molecular regulatory mechanism of GT formation, which has the potential to facilitate the molecular breeding, targeted engineering, informed harvest timing, and manipulation of cannabinoid production.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Deep learning‐based quantification and transcriptomic profiling reveal a methyl jasmonate‐mediated glandular trichome formation pathway in Cannabis sativa

Huang,

Chen,

Zhao

et al. 2024

The Plant Journal

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Sorghum segmentation and leaf counting using in silico trained deep neural model

Ostermann,

Benes,

Gaillard

et al. 2024

The Plant Phenome Journal

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This paper introduces a novel deep neural model for segmenting and tracking the number of leaves in sorghum plants in phenotyping facilities. Our algorithm inputs a sequence of images of a sorghum plant and outputs the segmented images and the number of leaves. The key novelty of our approach is in training the deep neural model. Manual annotations are tedious, and we have developed a procedural three‐dimensional (3D) sorghum model that provides detailed geometry and texture to generate photorealistic 3D models. The overall shape of the sorghum leaf geometry is determined by its skeleton, and it is detailed by a procedural model that varies its curvature, width, length, and overall shape. The color is determined by using a Monte Carlo path tracer. We mimic the illumination of the phenotyping facility and use reflectance and transmittance on sorghum surfaces to determine the color of the leaves. The 3D procedural model allows us to generate photorealistic and segmented images that we use to train a deep neural model. Our segmentation provides a mean intersection over union score of 0.51, resulting in leaf counting accuracy within the 90% confidence interval for the human counts.

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Detection and quantification of cotton trichomes by deep learning algorithm

Luo

Wen

et al. 2023

Computers and Electronics in Agriculture

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Deep learning-based high-throughput phenotyping accelerates gene discovery for stomatal traits

Cited by 6 publications

References 10 publications

Deep learning‐based quantification and transcriptomic profiling reveal a methyl jasmonate‐mediated glandular trichome formation pathway in Cannabis sativa

Deep learning‐based quantification and transcriptomic profiling reveal a methyl jasmonate‐mediated glandular trichome formation pathway in Cannabis sativa

Sorghum segmentation and leaf counting using in silico trained deep neural model

Detection and quantification of cotton trichomes by deep learning algorithm

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