The Balance Between Resource Sequestration and Retention: A Challenge in Plant Science

Modern aspects of many subjects (e.g., computer science and some aspects of medical science) are now taught in many high schools, but the plant sciences are often given short shrift. A collaboration was therefore established with a high school biology program in which pilot workshops could be developed to enable advanced students to gain insights into modern plant science techniques. A successful example is the workshop on plant biotechnology presented in this report. This workshop is simple and flexible, taking into account that most high school biology laboratories and classrooms are not set up for sophisticated plant science/biotechnology projects. It is suitable for from 10 to 30 students, depending upon space and facilities available. Students work in pairs or trios, and learn simple disinfestation and transfer techniques for micropropagation and potential subsequent transformation treatments. Students gain insights into: sterile technique and hygiene; plant hormones and their physiological effects; plant cell, tissue and organ culture; the influence of environmental factors on response of cells and tissues cultured in vitro; and an understanding of the phenomenon of organogenesis and resulting plant growth and development. This workshop has been tested on several classes of students and following analysis, several refinements were included in subsequent iterations. Results of the students' experiments have been positive and instructive, with student learning outcomes above expectations. Further details of the workshop techniques and approach will be presented.

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Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽

10.1186/s13007-021-00747-0 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Shuo Zhou ◽

Xiujuan Chai ◽

Zixuan Yang ◽

Hongwu Wang ◽

Chenxue Yang ◽

...

Keyword(s):

Computer Vision ◽

Image Analysis ◽

Deep Learning ◽

High Throughput ◽

Batch Processing ◽

Plant Phenotyping ◽

Plant Science ◽

Analysis Software ◽

Image Analysis Software ◽

Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

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3D-surface MALDI mass spectrometry imaging for visualising plant defensive cardiac glycosides in Asclepias curassavica

Analytical and Bioanalytical Chemistry ◽

10.1007/s00216-021-03177-y ◽

2021 ◽

Vol 413 (8) ◽

pp. 2125-2134

Author(s):

Domenic Dreisbach ◽

Georg Petschenka ◽

Bernhard Spengler ◽

Dhaka R. Bhandari

Keyword(s):

Mass Spectrometry ◽

Mass Spectrometry Imaging ◽

Cardiac Glycosides ◽

Molecular Mechanisms ◽

Plant Science ◽

Primary Metabolism ◽

Native Plant ◽

Asclepias Curassavica ◽

3D Surface ◽

3D Surfaces

AbstractMass spectrometry–based imaging (MSI) has emerged as a promising method for spatial metabolomics in plant science. Several ionisation techniques have shown great potential for the spatially resolved analysis of metabolites in plant tissue. However, limitations in technology and methodology limited the molecular information for irregular 3D surfaces with resolutions on the micrometre scale. Here, we used atmospheric-pressure 3D-surface matrix-assisted laser desorption/ionisation mass spectrometry imaging (3D-surface MALDI MSI) to investigate plant chemical defence at the topographic molecular level for the model system Asclepias curassavica. Upon mechanical damage (simulating herbivore attacks) of native A. curassavica leaves, the surface of the leaves varies up to 700 μm, and cardiac glycosides (cardenolides) and other defence metabolites were exclusively detected in damaged leaf tissue but not in different regions of the same leaf. Our results indicated an increased latex flow rate towards the point of damage leading to an accumulation of defence substances in the affected area. While the concentration of cardiac glycosides showed no differences between 10 and 300 min after wounding, cardiac glycosides decreased after 24 h. The employed autofocusing AP-SMALDI MSI system provides a significant technological advancement for the visualisation of individual molecule species on irregular 3D surfaces such as native plant leaves. Our study demonstrates the enormous potential of this method in the field of plant science including primary metabolism and molecular mechanisms of plant responses to abiotic and biotic stress and symbiotic relationships. Graphical abstract

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Engineering Climate-Change-Resilient Crops: New Tools and Approaches

International Journal of Molecular Sciences ◽

10.3390/ijms22157877 ◽

2021 ◽

Vol 22 (15) ◽

pp. 7877

Author(s):

Fahimeh Shahinnia ◽

Néstor Carrillo ◽

Mohammad-Reza Hajirezaei

Keyword(s):

Climate Change ◽

Plant Growth ◽

Sustainable Agriculture ◽

Stress Tolerance ◽

Crop Yield ◽

Plant Science ◽

Chemical Treatments ◽

Coated Nanoparticles ◽

Rapid Climate Change ◽

Protective Resources

Environmental adversities, particularly drought and nutrient limitation, are among the major causes of crop losses worldwide. Due to the rapid increase of the world’s population, there is an urgent need to combine knowledge of plant science with innovative applications in agriculture to protect plant growth and thus enhance crop yield. In recent decades, engineering strategies have been successfully developed with the aim to improve growth and stress tolerance in plants. Most strategies applied so far have relied on transgenic approaches and/or chemical treatments. However, to cope with rapid climate change and the need to secure sustainable agriculture and biomass production, innovative approaches need to be developed to effectively meet these challenges and demands. In this review, we summarize recent and advanced strategies that involve the use of plant-related cyanobacterial proteins, macro- and micronutrient management, nutrient-coated nanoparticles, and phytopathogenic organisms, all of which offer promise as protective resources to shield plants from climate challenges and to boost stress tolerance in crops.

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