Sorghum, a close relative of corn, tested for disease resistance on Pennsylvania farms

PENN STATE

Research News

 

IMAGE: DINAKARAN ELANGO, RECENT PLANT SCIENCE STUDENT, WITH BIOMASS SORGHUM LINES GROWING IN A RESEARCH PLOT AT PENN STATE'S RUSSELL E. LARSON AGRICULTURAL RESEARCH CENTER, ROCK SPRINGS, PA. RESEARCHERS CHARACTERIZED ANTHRACNOSE... view more 

CREDIT: SURINDER CHOPRA/PENN STATE

With sorghum poised to become an important crop grown by Pennsylvania farmers, Penn State researchers, in a new study, tested more than 150 germplasm lines of the plant for resistance to a fungus likely to hamper its production.

Sorghum, a close relative to corn, is valuable for yielding human food, animal feed and biofuels. Perhaps its most notable attribute is that the grain it produces is gluten free. Drought resistant and needing a smaller amount of nutrients than corn to thrive, sorghum seems to be a crop that would do well in the Keystone State's climate in a warming world. But its susceptibility to fungal disease is problematic.

"In other locations where sorghum has been grown for a long time, it is attacked by a fungal pathogen that causes a disease called anthracnose leaf blight, which diminishes its yield," said study co-author Surinder Chopra, professor of maize genetics in the College of Agricultural Sciences. "We conducted a three-part experiment designed to evaluate the likelihood that anthracnose will be a problem with sorghum production in Pennsylvania, and what plants might resist the disease."

First, researchers carried out field surveys in 2011, 2012 and 2016 in six Pennsylvania locations to monitor the presence of the Colletotrichum fungus that causes anthracnose in commercial sorghum fields. They collected soil samples, plant samples and samples of the debris left by sorghum or corn, looking for the fungus at sites in Blair, Lancaster, Dauphin, Centre, Bedford and Lebanon counties.

Next, researchers grew 158 sorghum lines at Penn State's Russell E. Larson Agricultural Research Center at Rock Springs and tested them for vulnerability and resistance to the natural strains of anthracnose fungus. They obtained plant material for many of the sorghum lines from the International Crops Research Institute for the Semi-Arid Tropics, better known as ICRISAT, India.

Other sorghum lines came from varieties Chopra's research group has been breeding in plots at Rock Springs for years and are being tested for stress tolerance in another study. Still others came from sources such as the U.S. Department of Agriculture's Agricultural Research Service stations in Griffin, Georgia, Lincoln, Nebraska, and Lubbock, Texas; the Grain, Forage and Bioenergy Research Center, Texas A&M Agrilife Sorghum Breeding Program; and the National Plant Germplasm System.

Lastly, researchers conducted experiments in greenhouses on the University Park campus. They chose 35 sorghum lines that demonstrated resistance to the fungus in field trials and tested their responses after inoculating them with the pathogen. The team evaluated and scored those plants for the severity of anthracnose leaf blight that developed.

In findings recently published in Crop Science, Chopra and colleagues reported that the anthracnose leaf blight symptoms were observed on the older and senescent leaves in Pennsylvania. After evaluating, in field and greenhouse tests, the performance of the 158 experimental lines and commercial hybrids, the researchers noted that they discovered sources of resistance to anthracnose leaf blight.

"Many of those sorghum lines we tested had been improved in several states in the U.S. and in other parts of the world," Chopra said. "These should be useful in breeding programs targeted for Pennsylvania and for northeastern U.S. climatic conditions. Several lines received from ICRISAT showed the high level of resistance in the field."

The research was done in preparation for widespread cultivation of sorghum in Pennsylvania, at which time anthracnose leaf blight is expected to become a problem for farmers, Chopra explained.

"Our study is the first to investigate the frequency, diversity and distribution of Colletotrichum fungi species on sorghum in Pennsylvania, and the first to look for disease-tolerant strains that will grow best in the Northeast," he said. "Our findings will help develop better recommendations for sorghum growers so they can manage and proactively prevent the buildup of inoculum and resulting disease outbreaks."

Also involved in the research were Iffa Gaffoor, former postdoctoral scholar in the Department of Plant Science at Penn State, advised by Chopra; Germán Sandoya, Everglades Research and Education Center/Horticultural Sciences Department, University of Florida; Katia Xavier, Lisa Vaillancourt and Etta Nuckles, Department of Plant Pathology, University of Kentucky; and Srinivasa R Pinnamaneni, Sorghum Breeding Program, ICRISAT, Patancheru, India.

The U.S. Department of Agriculture's National Institute of Food and Agriculture and the Fundacion Alfonso Martin Escudero for postdoctoral research provided funding for this work.

CAPTION

Sorghum, a close relative to corn, is valuable for yielding human food, animal feed and biofuels. Perhaps its most notable attribute is that the grain it produces is gluten free.

CREDIT

Surinder Chopra, Penn State

CAPTION

Researchers cultured Colletotrichum strains recovered from sorghum from various Pennsylvania farms (top and middle rows). Spores (bottom row) were collected from the culture plates at 14 days after inoculation.

CREDIT

Chopra Lab, Penn State

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Sweet sorghum: Sweet promise for the environment

IMAGE: THE KIT1 SORGHUM VARIETY DEVELOPED BY KIT ACCUMULATES A HIGH AMOUNT OF SUGAR AND THRIVES PARTICULARLY WELL UNDER TEMPERATE CLIMATE CONDITIONS. view more 

CREDIT: BOTANICAL INSTITUTE, KIT

New sorghum variety developed at KIT shows increased sugar accumulation and can be used for energy and materials production -- scientists report in Industrial Crops & Products

KARLSRUHER INSTITUT FÜR TECHNOLOGIE (KIT)

Research News

 

Sweet sorghum can be used to produce biogas, biofuels, and novel polymers. In addition, it can help replace phosphate fertilizers. A new sweet sorghum variety developed at Karlsruhe Institute of Technology (KIT) accumulates particularly high amounts of sugar and thrives under local conditions. As the scientists reported in the Industrial Crops & Products journal, sugar transport and sugar accumulation are related to the structure of the plants' vessels. This was the result of a comparison between sweet and grain sorghums. (DOI: 10.1016/j.indcrop.2021.113550)

As the world's population grows, the demand for food, raw materials, and energy is also on the rise. This increases the burden on the environment and the climate. One strategy to reduce greenhouse gas emissions is to grow so-called C4 crops. These carry out photosynthesis particularly efficiently, are therefore more effective in fixing carbon dioxide (CO2), and build up more biomass than other plants. Usually, they are native to sunny and warm places. One of the C4 plants is sorghum, also known as great millet, a species of the sorghum genus in the sweet grass family. The varieties that are particularly rich in sugar are called sweet sorghum (Sorghum bicolor L. Moench). Other varieties include grain sorghum used as animal feed. Sorghum can be grown on so-called marginal land, which is difficult to cultivate, so it does not compete with other food or forage crops.

A new sweet sorghum variety called KIT1 has been developed by Dr. Adnan Kanbar in the Molecular Cell Biology Division research group headed by Professor Peter Nick at the Botanical Institute of KIT. KIT1 accumulates particularly high amounts of sugar and thrives especially well under temperate climate conditions. It can be used both energetically, i.e. for the production of biogas and biofuels, and as a base material for the production of novel polymers. The estimated sugar yield per hectare is over 4.4 tons, which would correspond to almost 3,000 liters of bioethanol. In addition, the digestate produced during biogas production can be used for fertilizers to replace phosphate fertilizer, which will soon be in short supply.

The Plant Stem Anatomy is What Matters

Researchers at Nick's laboratory, which is part of the Institute for Applied Biosciences, and their colleagues at the Institute for Technical Chemistry at KIT and at ARCUS Greencycling Technology in Ludwigsburg compared the KIT1 sweet sorghum and Razinieh grain sorghum varieties in order to investigate the different sugar accumulation behaviors in the plant stem. For the study, published in the Industrial Crops & Products journal, the team looked at the stem anatomy. This includes the thickened areas (nodes) and the narrow areas or spaces between nodes (internodes), but also transcripts of important sucrose transporter genes as well as stress responses of plants to high salt concentrations in the soil. Sugar accumulation was highest in the central internodes in both genotypes. However, a relationship was found between sugar accumulation and the structure of the vessels that serve to transport water, solutes, and organic substances. The vessels are grouped into vascular bundles. These consist of the phloem (bast part) and the xylem (wood part). The phloem mainly transports sugars and amino acids, while the xylem's primary function is to transport water and inorganic salts; in addition, the xylem has a supporting function. The study revealed that in KIT1 and five other sweet sorghum varieties, the phloem cross-sectional area in the stem is much larger than the xylem cross-sectional area - the difference is much more pronounced than in the Razinieh grain sorghum variety. "Our study is the first one to look at the relationship between the structure of the vascular bundles and sugar accumulation in the stem," Nick says.

Sweet Sorghum Copes Better with Salinity Stress

As the study further revealed, salinity stress led to higher sugar accumulation in KIT1 than in Razinieh. The expression of sucrose transporter genes was higher in KIT1 leaves under normal conditions, and increased significantly under salinity stress. "Besides anatomical factors, there also some molecular factors that might contribute to regulating sugar accumulation in the stem," Kanbar explains. "In any case, KIT1 responds better to salinity stress." (or)

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Original Publication

Adnan Kanbar, Ehsan Shakeri, Dema Alhajturki, Michael Riemann, Mirko Bunzel, Marco Tomasi Morgano, Dieter Stapf, Peter Nick: Sweet versus grain sorghum: Differential sugar transport and accumulation are linked with vascular bundle architecture. Industrial Crops & Products, 2021. DOI: 10.1016/j.indcrop.2021.113550

Abstract at https://doi.org/10.1016/j.indcrop.2021.113550

Contact for this press release:

Sandra Wiebe, Press Officer, phone: +49 721 608-41172, e-mail: sandra.wiebe@kit.edu

Being "The Research University in the Helmholtz Association", KIT creates and imparts knowledge for the society and the environment. It is the objective to make significant contributions to the global challenges in the fields of energy, mobility, and information. For this, about 9,600 employees cooperate in a broad range of disciplines in natural sciences, engineering sciences, economics, and the humanities and social sciences. KIT prepares its 23,300 students for responsible tasks in society, industry, and science by offering research-based study programs. Innovation efforts at KIT build a bridge between important scientific findings and their application for the benefit of society, economic prosperity, and the preservation of our natural basis of life. KIT is one of the German universities of excellence.

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When plants flower: Scientists ID genes, mechanism in sorghum

Study points to genetic strategies for altering crop-plant flowering time to increase production of fuel-generating biomass

Peer-Reviewed Publication

DOE/BROOKHAVEN NATIONAL LABORATORY

 

IMAGE: 

BROOKHAVEN LAB BIOLOGIST MENG XIE AND POSTDOCTORAL FELLOW DIMIRU TADESSE WITH SORGHUM PLANTS LIKE THOSE USED IN THIS STUDY. NOTE THAT THESE PLANTS ARE FLOWERING, UNLIKE THOSE THE SCIENTISTS ENGINEERED TO DELAY FLOWERING INDEFINITELY TO MAXIMIZE THEIR ACCUMULATION OF BIOMASS.

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CREDIT: KEVIN COUGHLIN/BROOKHAVEN NATIONAL LABORATORY

UPTON, NY — Scientists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory and Oklahoma State University have identified key genes and the mechanism by which they control flowering in sorghum, an important bioenergy crop. The findings, just published in the journal New Phytologist, suggest strategies to delay sorghum flowering to maximize plant growth and the amount of biomass available for generating biofuels and bioproducts.

“Our studies elucidate the gene regulatory network controlling sorghum flowering and provide new insights into how these genes could be leveraged to improve sorghum for achieving bioenergy goals,” said Brookhaven Lab biologist Meng Xie, one of the leaders of the research.

Sorghum is particularly well suited for sustainable agriculture because it can grow on marginal lands in semiarid regions and can tolerate relatively high temperatures. Like many plants, its growth and flowering (reproductive) cycles are regulated by the duration of daily sunlight. And once plants start to flower, they stop growing, which has important implications for the accumulation of biomass.

For example, one natural sorghum variety can reach nearly 20 feet in height, only transitioning to the reproductive flowering phase near the end of the summer growing season when the duration of daylight diminishes. Other “day-neutral” lines flower earlier, after reaching about three feet in height, producing less vegetation but more grain.

“While these earlier flowering varieties might be preferable when growing sorghum as a food source, for bioenergy production, we prefer sorghum to have later flowering. That gives the plants more time to grow and accumulate biomass in the stems and leaves,” Xie said.

Understanding the genes that control these different flowering times — a long-sought goal for plant scientists — might point to ways to optimize sorghum for either desired outcome.

With the bioenergy production goal in mind, the Brookhaven team started by exploring a gene that had been previously identified as associated with later flowering, known as SbGhd7. The association between this gene and later flowering was based on statistical predictions from genome-wide studies, but it had not been validated with experimental data — and its mechanism of action was completely unknown.

“Our study provided direct evidence to support this gene’s function in flowering control and also helped us understand its molecular mechanism,” said Brookhaven Lab postdoctoral fellow Dimiru Tadesse, first author on the study.

Overexpression eliminates flowering

The first evidence came from transgenic sorghum plants engineered at Oklahoma State to overexpress the purported flowering-control gene. Sorghum varieties that overexpressed this gene — that is, made its protein product in abundance — didn’t just delay flowering; they never flowered at all.

“This was a dramatic difference from what happens in rice plants when they overexpress their version of this same gene,” Xie noted. “In rice, overexpression of this gene delays flowering for eight to 20 days — not forever!”

The transformed sorghum plants had more than twice the biomass of control plants.

To find out why, Xie and his team wanted to unravel the details of how this gene operated within cells.  Their goal was to see how the protein that is coded for by the flowering-repressor gene interacted with other genes.

Doing these studies in actual plants would have taken months or years. So, instead, Xie and his colleagues at Brookhaven worked with individual plant cells.

Transforming naked plant cells

They used plant cells whose outer cell walls had been removed. The “naked” plant cells, known as protoplasts, could easily absorb a plasmid, a small bit of DNA added to their growth medium. By putting the gene or genes they wanted to test into that plasmid, the scientists could get the plant cells to make the desired protein.

“The plasmid will get into the cell and incubate overnight and the protein will have a very high level of expression,” Xie said. “It’s just a one-day procedure.”

To track what the protein made by the flowering-repressor gene was doing in the cells, the scientists attached another small protein to it to act as a sort of tag. Then they added antibodies designed to bind to the tag. If the flowering-repressor protein bound to other gene regions in the plant’s genomic DNA, the scientists could pull the whole antibody-protein-DNA complex out of the solution to sequence those gene regions.

“This method, called ‘transient chromatin immunoprecipitation-sequencing’ or ‘Transient ChIP-seq,’ showed us where the protein that eliminates flowering binds to on sorghum genomic DNA,” Xie said. “It identifies the targets of this regulator protein in the sorghum genome.”

Master regulator

The scientists found that their flowering-repressor protein was binding to a lot of targets. These included other genes involved in turning flowering on.

“There were some genes that were found previously to regulate flowering in other plant species, but their functions in sorghum, many of them, were still not fully studied,” Xie said.

When collaborators at Oklahoma State produced sorghum plants that overexpressed those target genes, they found that the target genes induced early flowering. The repressor protein, the team reasoned, must therefore work by turning off those early flowering genes.

With their precision sequencing technique, the Brookhaven scientists identified the regulator protein’s specific binding site: a very short DNA sequence within the “on” switch, or promoter, for each individual target gene.

“The promoter of each target gene is different, but they all contain this same short sequence,” Xie said. By binding, the repressor protein flips these on switches off.

The idea that the repressor protein could impact multiple targets was somewhat new.

“Others had speculated that the original regulator protein only regulated one flowering activator. But we found it is much more complicated. In addition to regulating one suspected target activator, this protein also regulates several others — some directly and some indirectly,” Xie said. “It’s like a master regulator for turning off flowering.”

The practical application of these findings in making sorghum that doesn’t flower could have additional benefits for engineered sorghum. In addition to having increased biomass for biofuel production, these plants — with no flowers and no pollen — would be unable to share their altered genes with other closely related plants. This built-in gene containment might help potential growers meet the regulatory requirements for implementing such a strategy in real-world agricultural environments.

This work was funded by the DOE Office of Science through Brookhaven Lab’s Quantitative Plant Sciences Initiative and by the U.S. Department of Agriculture via collaborators at Oklahoma State University. The Transient ChIP-seq method for optimizing protoplast transformation, sample preparation, and sequencing was developed with Laboratory Directed Research and Development funding at Brookhaven Lab.

Brookhaven National Laboratory is supported by the Office of Science of the U.S. Department of Energy. The Office of Science is the single largest supporter of basic research in the physical sciences in the United States and is working to address some of the most pressing challenges of our time. For more information, visit science.energy.gov.

Follow @BrookhavenLab on social media. Find us on Instagram, LinkedIn, X, and Facebook.

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JOURNAL

New Phytologist

DOI

10.1111/nph.19591 

ARTICLE TITLE

Sorghum SbGhd7 is a major regulator of floral transition and directly represses genes crucial for flowering activation

ARTICLE PUBLICATION DATE

7-Mar-2024

 

Making gluten-free, sorghum-based beers easier to brew and enjoy

Peer-Reviewed Publication

AMERICAN CHEMICAL SOCIETY

Though beer is a popular drink worldwide, it’s usually made from barley, which leaves those with a gluten allergy or intolerance unable to enjoy the frothy beverage. Sorghum, a naturally gluten-free grain, could be an alternative, but complex preparation steps have hampered its widespread adoption by brewers. Now, researchers reporting the molecular basis behind sorghum brewing in ACS’ Journal of Proteome Research have uncovered an enzyme that could improve the future of sorghum-based beers.

Traditionally, beer brewers start with barley grains, which they malt, mash, boil and ferment to create the bubbly beverage. Barley contains gluten — a group of proteins found in several cereal grains. Sorghum, on the other hand, lacks these proteins and behaves differently than barley during brewing. For example, strong molecular bonds make it difficult to release starches from the grains during the mash stage. And fewer enzymes are present in sorghum wort — the liquid extracted from the mashing process — to transform the starches into simple sugars, such as glucose, which ferments into alcohol. Even when brewers adjust the reaction conditions during these steps, the resulting beverages are still less alcoholic than barley-based beers. To help bring the alcohol content up to expected standards, Edward Kerr, Glen Fox and Benjamin Schulz investigated the molecular processes that occur during sorghum brewing and found ways to improve the final product.

The team brewed both barley and sorghum beverages, taking them through malting, mashing and fermentation steps at varying temperatures and lengths of time. At the malting stage, the samples were analyzed via mass spectrometry proteomics, which revealed the presence of many of the same enzymes in barley malt and sorghum malt; those enzymes included amylases that break down starches into maltose. After the malts were steeped with water, the resulting sorghum wort contained less maltose than barley wort, but considerably more glucose. The team attributed these differences to the enzyme compositions: Sorghum wort contains fewer amylase enzymes than barley wort but more α‑glucosidase, an enzyme that breaks down starches into glucose instead. By optimizing brewing parameters to favor the activity of α‑glucosidase, the researchers say that brewers could create sorghum wort with higher concentrations of fermentable glucose, resulting in sorghum-based beers with higher alcohol content and better overall quality.

The authors do not acknowledge a funding source for this study.

 

The American Chemical Society (ACS) is a nonprofit organization chartered by the U.S. Congress. ACS’ mission is to advance the broader chemistry enterprise and its practitioners for the benefit of Earth and all its people. The Society is a global leader in promoting excellence in science education and providing access to chemistry-related information and research through its multiple research solutions, peer-reviewed journals, scientific conferences, eBooks and weekly news periodical Chemical & Engineering News. ACS journals are among the most cited, most trusted and most read within the scientific literature; however, ACS itself does not conduct chemical research. As a leader in scientific information solutions, its CAS division partners with global innovators to accelerate breakthroughs by curating, connecting and analyzing the world’s scientific knowledge. ACS’ main offices are in Washington, D.C., and Columbus, Ohio.

To automatically receive news releases from the American Chemical Society, contact newsroom@acs.org.

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JOURNAL

Journal of Proteome Research

DOI

10.1021/acs.jproteome.3c00436 

ARTICLE TITLE

Proteomics and Metabolomics Reveal that an Abundant α‑Glucosidase Drives Sorghum Fermentability for Beer Brewing

 

Illinois researchers develop near-infrared spectroscopy models to analyze corn kernels, biomass

University of Illinois College of Agricultural, Consumer and Environmental Sciences

 

image: 

University of Illinois researchers developed a global model for corn kernel analysis with NIR spectroscopy.

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Credit: College of ACES

URBANA, Ill. – In the agricultural and food industry, determining the chemical composition of raw materials is important for production efficiency, application, and price. Traditional laboratory testing is time-consuming, complicated, and expensive. New research from the University of Illinois Urbana-Champaign demonstrates that near-infrared (NIR) spectroscopy and machine learning can provide quick, accurate, and cost-effective product analysis.

In two studies, the researchers explore the use of NIR spectroscopy for analyzing characteristics of corn kernels and sorghum biomass.

“NIR spectroscopy has many advantages over traditional methods. It is fast, accurate, and inexpensive. Unlike lab analysis, it does not require the use of chemicals, so it’s more environmentally sustainable. It does not destroy the samples, and you can analyze multiple features at the same time. Once the system is set up, anyone can run it with minimal training,” said Mohammed Kamruzzaman, assistant professor in the Department of Agricultural and Biological Engineering (ABE), part of the College of Agricultural, Consumer and Environmental Sciences and The Grainger College of Engineering at U. of I. He is a co-author on both papers.

In the first study, the researchers created a global model for corn kernel analysis. Moisture and protein content impact nutritional value, processing efficiency, and price of corn, so the information is crucial for the grain processing industry. 

NIR and other spectroscopic techniques are indirect methods. They measure how a material absorbs or emits light at different wavelengths, then construct a unique spectrum that is translated into product characteristics with machine learning models. Many food and agricultural processing facilities already have NIR equipment, but models need to be trained for specific purposes.

“Corn grown in different locations varies because of soil, environment, management, and other factors. If you train the model with corn from one location, it will not be accurate elsewhere,” Kamruzzaman said.

To address this issue and develop a model that applies in many different locations, the researchers collected corn samples from seven countries – Argentina, Brazil, India, Indonesia, Serbia, Tunisia, and the USA. 

“To analyze moisture and protein in the corn kernels, we combined gradient-boosting machines with partial least squares regression. This is a novel approach that yields accurate, reliable results,” said Runyu Zheng, a doctoral student in ABE and lead author on the first study.

While the model is not 100% global, it provides considerable variability in the data and will work in many locations. If needed, it can be updated with additional samples from new locations, Kamruzzaman noted.

In the second study, the researchers focused on sorghum biomass, which can serve as a renewable, cost-effective, and high-yield feedstock for biofuel.

Biomass conversion into biofuels depends on chemical composition, so a rapid and efficient method of sorghum biomass characterization could assist biofuel, breeding, and other relevant industries, the researchers explained.

Using sorghum from the University of Illinois Energy Farm, they were able to accurately and reliably predict moisture, ash, lignin, and other features. 

“We first scanned the samples and obtained NIR spectra as an output. This is like a fingerprint that is unique to different chemical compositions and structural properties. Then we used chemometrics – a mathematical-statistical approach – to develop the prediction models and applications,” said Md Wadud Ahmed, a doctoral student in ABE and lead author on the second paper.

While NIR spectroscopy is not as accurate as lab analysis, it is more than sufficient for practical purposes and can provide fast, efficient screening methods for industrial use, Kamruzzaman said.

“A major advantage of this technology is that you don’t need to remove and destroy products. You can simply take samples for measurement, scan them, and then return them to the production stream. In some cases, you can even scan the samples directly in the production line. NIR spectroscopy provides a lot of flexibility for industrial usage,” he concluded. 

The first paper, “Optimizing feature selection with gradient boosting machines in PLS regression for predicting moisture and protein in multi-country corn kernels via NIR spectroscopy,” is published in Food Chemistry [DOI: 10.1016/j.foodchem.2024.140062].

The second paper, “Rapid and high-throughput determination of sorghum (Sorghum bicolor) biomass composition using near infrared spectroscopy and chemometrics,” is published in Biomass and Bioenergy [DOI:10.1016/j.biombioe.2024.107276]. This work was funded by the DOE Center for Advanced Bioenergy and Bioproducts Innovation (U.S. Department of Energy, Office of Science, Biological and Environmental Research Program under Award Number DE-SC0018420).

Md Wadud Ahmed, a doctoral student at the University of Illinois, used NIR spectroscopy and machine learning to analyze the composition of sorghum biomass.

Credit

College of ACES

Journal

Biomass and Bioenergy

DOI

10.1016/j.biombioe.2024.107276 

Method of Research

Data/statistical analysis

Article Title

Rapid and high-throughput determination of sorghum (Sorghum bicolor) biomass composition using near infrared spectroscopy and chemometrics

 

MRIs on crop roots open new doors for agriculture

Scientists examine plant roots to make improvements, enhance water-use efficiency

Grant and Award Announcement

TEXAS A&M AGRILIFE COMMUNICATIONS

A team of scientists led by Texas A&M AgriLife is taking a page from the medical imaging world and using MRI to examine crop roots in a quest to develop crops with stronger and deeper root systems.

Nithya Rajan, Ph.D., stands with the low-field MRI rhizotron in the greenhouse at Texas A&M University. (Texas A&M AgriLife photo by Laura McKenzie)

The team from Texas A&M AgriLife Research, Harvard Medical School, ABQMR Inc. and Soil Health Institute developed a novel MRI-based root phenotyping system to nondestructively acquire high-resolution images of plant roots growing in soil and established the Texas A&M Roots Lab to further develop this technology as a new tool for assessing crop root traits.

The “Field-Deployable Magnetic Resonance Imaging Rhizotron for Modeling and Enhancing Root Growth and Biogeochemical Function” is a part of the Rhizosphere Observations Optimizing Terrestrial Sequestration, ROOTS, program funded through U.S. Department of Energy’s Advanced Research Projects Agency-Energy program.

Nithya Rajan, Ph.D., AgriLife Research crop physiologist/agroecologist in the College of Agriculture and Life Sciences Department of Soil and Crop Sciences, Bryan-College Station, is leading this multidisciplinary project team.

“We are applying this technology to see if we can sense roots growing in agricultural soils and characterize them,” she said. “To date, imaging roots in soil has been challenging because the soil is complex, with solids, moisture and roots. We just want to image the roots.”

We need to develop crop root systems that store more carbon in soil. In addition, deeper root systems can take up more water from soil profiles, increasing crop drought resilience.

John Mullet, Ph.D., biochemist and Perry L. Adkisson Chair in Agricultural Biology in the Department of Biochemistry and Biophysics

From concept to applications, in sorghum and beyond

The project was initially funded for three years with a $4.6 million grant. The second phase of funding was approved this year at $4.4 million.

 

Will Wheeler, post-doctoral researcher with Texas A&M AgriLife Research, is lowering sorghum plants into the MRI rhizotron for root imaging. Steve Altobelli from ABQMR is on the right. (Texas A&M AgriLife photo by Nithya Rajan)

“In the first phase, we developed the proof of concept and initial prototypes, and in the second phase we developed a low-field MRI rhizotron for high throughput imaging and applications in a wide variety of crops in addition to energy sorghum,” Rajan said.

Also on the team with AgriLife Research are Bill Rooney, Ph.D., sorghum breeder and Borlaug-Monsanto Chair for Plant Breeding and International Crop Improvement in the Department of Soil and Crop Sciences, and John Mullet, Ph.D., biochemist and Perry L. Adkisson Chair in Agricultural Biology in the Department of Biochemistry and Biophysics.

Rooney and Mullet are using the MRI system to advance bioenergy sorghum genetics. Brock Weers, Ph.D., and Will Wheeler, Ph.D., are support scientists working with the AgriLife Research team.

“We need to develop crop root systems that store more carbon in soil,” Mullet said. “In addition, deeper root systems can take up more water from soil profiles, increasing crop drought resilience.”

From a crop improvement perspective, Rooney added, this technology is essential to effectively screen crop germplasm for specific genotypes with enhanced root systems.

Getting to the root of the matter, without disturbing the soil

A 3D Image of the sorghum root system generated using the low-field MRI rhizotron. (Photo provided by ABQMR Inc.)

Using MRI allows researchers to gather root images without damaging plants, unlike traditional methods such as trenching, soil coring and root excavation, Rajan said.

The AgriLife Research team is working with ABQMR Inc., a group of MRI scientists in Albuquerque, New Mexico, who are involved in designing and building the system.

“With low magnetic fields, MRI can be used to image roots in natural soils,” said Hilary Fabich, Ph.D., president of ABQMR. “The low magnetic fields also mean there is less of a safety risk working with the sensor in an agricultural setting.”

Using “machine learning” to see through the noise

Matt Rosen, Ph.D., is the co-principal investigator of the project. He is director of the Low-field MRI and Hyperpolarized Media Laboratory and co-director of the Center for Machine Learning at the Martinos Center for Biomedical Imaging at Harvard. Rosen and his team bring their experience with both low-field MRI physics and state-of-the-art artificial intelligence techniques to the project.

MRI 3D seGmentation and Analysis for Root Description — MIDGARD — software rendering of MRI sorghum root image. (Image provided by Bragi Sveinsson)

The Rosen lab pioneered the use of deep learning for processing MRI data. Neha Koonjoo, Ph.D., a postdoctoral fellow in the Rosen lab, has been leveraging the AUTOMAP — Automated TransfOrm by Manifold Approximation — deep learning-based image reconstruction approach to reduce the influence of environmental noise in root MRI images. Her approach was described in a recent research article.  

Bragi Sveinsson, Ph.D., a postdoctoral fellow working with Rosen, developed the first prototype of a software named “MIDGARD” — MRI 3D seGmentation and Analysis for Root Description — for extracting quantitative root trait information from MRI images of roots.

The team plans to release MIDGARD as an open-source software after further testing.

“Using MIDGARD, we can extract quantitative root trait information, and this data will be used for selection of ideal plant characteristics,” Rosen said. “In the future, MIDGARD will also have the ability to three-dimensionally image soil water content, a key property that drives root growth and exploration.”

Technology to market

Technology-to-market activities of this project are led by Cristine Morgan, Ph.D., chief scientific officer of Soil Health Institute, Research Triangle Park, North Carolina, and principal investigator of the first phase of the project when she was at Texas A&M. To foster collaborations with industry partners, the Soil Health Institute established the company Intact Data Services.

“I am excited to translate this technology for phenotyping at scale, as well as the ability to use MRI to 3D-image soil water intact,” Morgan said.

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Media Inquiries to Laura Muntean, laura.muntean@ag.tamu.edu, 6012481891

Written by Kay Ledbetter, 806-547-0002, skledbetter@ag.tamu.edu

Counting heads: how deep learning can simplify tedious agricultural tasks

Scientists show how machine learning models can be used to automatically detect the heads of sorghum plants in drone images to derive agricultural metrics

Peer-Reviewed Publication

NANJING AGRICULTURAL UNIVERSITY THE ACADEMY OF SCIENCE

The selective breeding of grain crops is one of the main reasons why domesticated plants produce such excellent yields. Selecting the best candidates for breeding is, however, a remarkably complex task. On one hand, it requires a skilled breeder with trained eyes to assess plant resistance to disease and pests, crop growth, and other factors. On the other hand, it also requires precise tool-assisted measurements such as grain size, mass, and quality.

Although all these standard measures are useful, none of them takes into account the number of panicles or ‘heads’ per plant. Head density is closely related to crop yield in most cases, and it could easily be a staple characteristic to measure in breeding programs. However, estimating the number of heads per plant and per unit area is very time consuming and requires tedious manual work.

To address this issue, many researchers have developed machine learning models that can automatically detect individual heads on grain crops in images taken either at ground level or by drones. While these models are aimed at simplifying the otherwise manual counting process in the field, the reality is that they are usually trained in limited testing conditions and focus exclusively on head detection without providing more metrics. In other words, using these models outside of the context in which they were developed and trained can be difficult, tedious, and even yield poor results.

Against this backdrop, a research team including Professor Scott Chapman from The University of Queensland, Australia, sought to promote deep-learning models for head counting by providing a detailed pipeline outlining their use. As explained in their paper, which was recently published in Plant Phenomics, this pipeline covers most of the quirks and challenges that one could find when using these models. “We took various real-world variables into consideration, including data preparation, model validation, inference, and how to derive yield-specific metrics,” explains Prof. Chapman, “We aimed to outline a practical and end-to-end pipeline for head detection in sorghum.”

There are two variants to the proposed pipeline, which are demonstrated by way of two independent illustrative experiments. In the first one, the researchers show how one should proceed if one needed to prepare training, testing, and validation datasets for a given machine learning model from scratch. This is usually the case when publicly available datasets are not suitable for the target field, which can happen, for example, when one is dealing with a different stage in plant development than the available datasets.

In the second experiment, the team showcases the steps required to use various pre-trained deep-learning models for sorghum head detection and/or counting. They demonstrate how the detection results (that is, the output of models that only outline sorghum heads on a set of given images) can be ‘stitched together’ into larger mosaic images. This enables one to observe and analyze large areas more easily and calculate important metrics, such as head density per tilling row or per square meter. “Our pipeline produces a high-resolution head density map that can be used for the diagnosis of agronomic variability within a field without relying on commercial software,” highlights Prof. Chapman.

Overall, this study will be useful to researchers and people involved in the agricultural industry alike. Not only it explains how deep learning models can be leveraged to assess grain crops more efficiently, but it also helps unlock new functionalities for camera-equipped drones in agriculture. Worth noting, the proposed pipeline could be adapted to other plants besides sorghum, as Prof. Chapman remarks: “Although we demonstrated our pipeline in a sorghum field, it can be generalized to other grain species. In future works, we intend to test our pipeline on tasks involving other grain types, such as wheat and maize yield estimation.”

Let us hope this work help us bridge the field of agriculture with machine learning to improve crop breeding and, thus, secure better food supplies.

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Reference

Authors

Chrisbin James1, Yanyang Gu2, Andries Potgieter3, Etienne David4, Simon Madec4, Wei Guo5, Frédéric Baret6, Anders Eriksson2, and Scott Chapman1

Affiliations

1School of Agriculture and Food Sciences, The University of Queensland

2School of Information Technology and Electrical Engineering, The University of Queensland

3Queensland Alliance for Agriculture and Food Innovation, The University of Queensland

4Arvalis, Institut du Végétal

5Graduate School of Agricultural and Life Sciences, The University of Tokyo

6Institut National de la Recherche Agronomique

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JOURNAL

Plant Phenomics

DOI

10.34133/plantphenomics.0017 

ARTICLE TITLE

From Prototype to Inference: A Pipeline to Apply Deep Learning in Sorghum Panicle Detection