Sha Tao

Bio: Sha Tao is an academic researcher from Huazhong Agricultural University. The author has contributed to research in topics: Stomatal conductance. The author has an hindex of 1, co-authored 1 publications receiving 8 citations.

Image-Based Assessment of Drought Response in Grapevines.

Abstract: Introduction Many plants can modify their leaf profile rapidly in response to environmental stress. Image-based data are increasingly used to retrieve reliable information on plant water status in a non-contact manner that has the potential to be scaled to high-throughput and repeated through time. This paper examined the variation of leaf angle as measured by both 3D images and goniometer in progressively drought stressed grapevine. Grapevines, grown in pots, were subjected to a 21-day period of drought stress receiving 100% (CTRL), 60% (IRR60%) and 30% (IRR30%) of maximum soil available water capacity. Leaf angle was (i) measured manually (goniometer) and (ii) computed by a 3D reconstruction method (multi-view stereo and structure from motion). Stomatal conductance, leaf water potential, fluorescence (Fv/Fm), leaf area and 2D RGB data were simultaneously collected during drought imposition. Throughout the experiment values of leaf water potential ranged from -0.4 (CTRL) to -1.1 MPa (IRR30%) and it linearly influenced the leaf angle when measured manually (R2=0.86) and with 3D image (R2=0.73). Drought was negatively related to stomatal conductance and leaf area growth particularly in IRR30% while photosynthetic parameters (i.e. Fv/Fm) were not impaired by water restriction. A model for leaf area estimation based on the number of pixel of 2D RGB images developed at a different phenotyping robotized platform in a closely related experiment was successfully employed (R2=0.78). At the end of the experiment, top view 2D RGB images showed a approx. 50% reduction of greener fraction (GGF) in CTRL and IRR60% vines compared to initial values, while GGF in IRR30% increased by approx. 20%. The influence of juvenile dichromatism of leaf occurring for specific varieties on the determination of colour-based indexes has been discussed.

Crop Phenomics: Current Status and Perspectives.

Abstract: Reliable, automatic, multifunctional, and high-throughput phenotypic technologies are increasingly considered important tools for rapid advancement of genetic gain in breeding programs. With the rapid development in high-throughput phenotyping technologies, research in this area is entering a new era called 'phenomics.' The crop phenotyping community not only needs to build a multi-domain, multi-level, and multi-scale crop phenotyping big database, but also to research technical systems for phenotypic traits identification and develop bioinformatics technologies for information extraction from the overwhelming amounts of omics data. Here, we provide an overview of crop phenomics research, focusing on two parts, from phenotypic data collection through various sensors to phenomics analysis. Finally, we discussed the challenges and prospective of crop phenomics in order to provide suggestions to develop new methods of mining genes associated with important agronomic traits, and propose new intelligent solutions for precision breeding.

New approaches to improve crop tolerance to biotic and abiotic stresses.

Abstract: During the last years, a great effort has been dedicated at the development and employment of diverse approaches for achieving more stress-tolerant and climate-flexible crops and sustainable yield increases to meet the food and energy demands of the future. The ongoing climate change is in fact leading to more frequent extreme events with a negative impact on food production, such as increased temperatures, drought, soil salinization as well as invasive arthropod pests and diseases. In this review, diverse "green strategies" (e.g., chemical priming, root-associated microorganisms), and advanced technologies (e.g., genome editing, high-throughput phenotyping) are described on the basis of the most recent research evidence. Particularly, attention has been focused on the potential use in a context of sustainable and climate-smart agriculture (the so called "next agriculture generation") to improve plant tolerance and resilience to abiotic and biotic stresses. In addition, the gap between the results obtained in controlled experiments and those from application of these technologies in real field conditions (lab to field step) is also discussed. This article is protected by copyright. All rights reserved.

Deep Segmentation of Point Clouds of Wheat.

Abstract: The 3D analysis of plants has become increasingly effective in modeling the relative structure of organs and other traits of interest. In this paper, we introduce a novel pattern-based deep neural network, Pattern-Net, for segmentation of point clouds of wheat. This study is the first to segment the point clouds of wheat into defined organs and to analyse their traits directly in 3D space. Point clouds have no regular grid and thus their segmentation is challenging. Pattern-Net creates a dynamic link among neighbors to seek stable patterns from a 3D point set across several levels of abstraction using the K-nearest neighbor algorithm. To this end, different layers are connected to each other to create complex patterns from the simple ones, strengthen dynamic link propagation, alleviate the vanishing-gradient problem, encourage link reuse and substantially reduce the number of parameters. The proposed deep network is capable of analysing and decomposing unstructured complex point clouds into semantically meaningful parts. Experiments on a wheat dataset verify the effectiveness of our approach for segmentation of wheat in 3D space.