Pridmore, T. | Search bioRxiv

bioRxiv2024

Root metaxylem area influences drought tolerance and transpiration in pearl millet in a soil texture dependent manner

O_LIPearl millet is a key cereal for food security in drylands but its yield is strongly impacted by drought. We investigated how root anatomical traits contribute to mitigating the effects of vegetative drought stress in pearl millet. C_LIO_LIWe examined associations between root anatomical traits and agronomical performance in a pearl millet diversity panel under irrigated and vegetative drought stress treatments in field trials. The impact of associated anatomical traits on transpiration was assessed using subpanels grown in different soil within a greenhouse. C_LIO_LIIn the field, total metaxylem area was positively correlated with grain weight and its maintenance under drought. In the greenhouse, genotypes with larger metaxylem area grown in sandy soil exhibited a consumerist water use strategy under irrigation, which shifted to a conservative strategy under drought. Water savings was mediated by transpiration restriction under high evaporative demand. This mechanism was dependent on soil hydraulics as it was not observed in peat soil with higher hydraulic conductivity upon soil drying. C_LIO_LIWe propose that water savings under drought, mediated by large metaxylem area and its interaction with soil hydraulics, help mitigate vegetative drought stress. Our findings highlight the role of soil hydraulic properties in shaping plant hydraulics and drought tolerance. C_LI

physiology↗

bioRxiv2020

RootNet: A Convolutional Neural Networks for Complex Plant Root Phenotyping from High-Definition Datasets

Plant phenotyping using machine learning and computer vision approaches is a challenging task. Deep learning-based systems for plant phenotyping is more efficient for measuring different plant traits for diverse genetic discoveries compared to the traditional image-based phenotyping approaches. Plant biologists have recently demanded more reliable and accurate image-based phenotyping systems for assessing various features of plants and crops. The core of these image-based phenotyping systems is structural classification and features segmentation. Deep learning-based systems, however, have shown outstanding results in extracting very complicated features and structures of above-ground plants. Nevertheless, the below-ground part of the plant is usually more complicated to analyze due to its complex arrangement and distorted appearance. We proposed a deep convolutional neural networks (CNN) model named "RootNet" that detects and pixel-wise segments plant roots features. The feature of the proposed method is detection and segmentation of very thin (1-3 pixels wide roots). The proposed approach segment high definition images without significantly sacrificing pixel density, it leads to more accurate root type detection and segmentation results. It is hard to train CNNs with high definition images due to GPU memory limitations. The proposed patch-based CNN training setup makes use of the entire image (with maximum pixel desisity) to recognize and segment give root system efficiently. We have used wheat (Triticum aestivum L.) seedlings dataset, which consists of wheat roots grown in visible pouches. The proposed system segments are given root systems and save it to the Root System Markup Language (RSML) for future analysis. RootNet trained on the dataset mentioned above along with popular semantic segmentation architectures, and it achieved a benchmark accuracy.

plant biology↗