Multifunctional Patch Offers Early Detection of Plant Diseases, Other Crop Threats

Researchers from North Carolina State University have developed an electronic patch that can be applied to the leaves of plants to monitor crops for different pathogens — such as viral and fungal infections — and stresses such as drought or salinity. In testing, the researchers found the patch was able to detect a viral infection in tomatoes more than a week before growers would be able to detect any visible symptoms of disease.

“This is important because the earlier growers can identify plant diseases or fungal infections, the better able they will be to limit the spread of the disease and preserve their crop,” says Qingshan Wei, assistant professor of chemical and biomolecular engineering at NC State.

“In addition, the more quickly growers can identify abiotic stresses, such as irrigation water contaminated by saltwater intrusion, the better able they will be to address relevant challenges and improve crop yield.”

The technology builds on a previous prototype patch, which detected plant disease by monitoring volatile organic compounds (VOCs) emitted by plants. Plants emit different combinations of VOCs under different circumstances. By targeting VOCs that are relevant to specific diseases or plant stress, the sensors can alert users to specific problems.

“The new patches incorporate additional sensors, allowing them to monitor temperature, environmental humidity, and the amount of moisture being ‘exhaled’ by the plants via their leaves,” says Yong Zhu, co-corresponding author of the paper.

The patches themselves are small — only 30 millimeters long — and consist of a flexible material containing sensors and silver nanowire-based electrodes. The patches are placed on the underside of leaves, which have a higher density of stomata — the pores that allow the plant to “breathe” by exchanging gases with the environment.

The researchers tested the new patches on tomato plants in greenhouses and experimented with patches that incorporated different combinations of sensors. The tomato plants were infected with three different pathogens: tomato spotted wilt virus (TSWV); early blight, which is a fungal infection; and late blight, which is a type of pathogen called an oomycete. The plants were also exposed to a variety of abiotic stresses, such as overwatering, drought conditions, lack of light, and high salt concentrations in the water.

The researchers took data from these experiments and plugged them into an artificial intelligence program to determine which combinations of sensors worked most effectively to identify both disease and abiotic stress.

“Our results for detecting all of these challenges were promising across the board,” Wei says. “For example, we found that using a combination of three sensors on a patch, we were able to detect TSWV four days after the plants were first infected. This is a significant advantage, since tomatoes don’t normally begin to show any physical symptoms of TSWV for 10 to 14 days.”

The researchers say they are two steps away from having a patch that growers can use. First, they need to make the patches wireless — a relatively simple challenge. Second, they need to test the patches in the field, outside of greenhouses, to ensure the patches will work under real-world conditions.

“We’re currently looking for industry and agriculture partners to help us move forward with developing and testing this technology,” Zhu says. “This could be a significant advance to help growers prevent small problems from becoming big ones, and help us address food security challenges in a meaningful way.”

The paper was published in the open-access journal Science Advances. 

AI-Powered Object Recognition May Help Prevent Wheat Disease

A new University of Illinois project is using advanced object recognition technology to keep toxin-contaminated wheat kernels out of the food supply and to help researchers make wheat more resistant to fusarium head blight, or scab disease, the crop’s top nemesis.

“Fusarium head blight causes a lot of economic losses in wheat, and the associated toxin, deoxynivalenol (DON), can cause issues for human and animal health. The disease has been a big deterrent for people growing wheat in the eastern U.S. because they could grow a perfectly nice crop, and then take it to the elevator only to have it get docked or rejected. That’s been painful for people. So it’s a big priority to try to increase resistance and reduce DON risk as much as possible,” said Jessica Rutkoski, co-author on the new paper in the Plant Phenome Journal.

Increasing resistance to any crop disease traditionally means growing a lot of genotypes of the crop, infecting them with the disease, and looking for symptoms. The process, known in plant breeding as phenotyping, is successful when it identifies resistant genotypes that don’t develop symptoms, or less severe symptoms. When that happens, researchers try to identify the genes related to disease resistance and then put those genes in high-performing hybrids of the crop.

It’s a long, repetitive process, but Rutkoski hoped one step — phenotyping for disease symptoms — could be accelerated. She looked for help from AI experts Junzhe Wu, doctoral student in the Department of Agricultural and Biological Engineering, and Girish Chowdhary, associate professor in the Department of Computer Science.

“We wanted to test whether we could quantify kernel damage using simple cell phone images of grains. Normally, we look at a petri dish of kernels and then give it a subjective rating. It’s very mind-numbing work. You have to have people specifically trained and it’s slow, difficult, and subjective. A system that could automatically score kernels for damage seemed doable because the symptoms are pretty clear,” Rutkoski says.

Wu and Chowdhary agreed it was possible. They started with algorithms similar to those used by tech giants for object detection and classification. But discerning minute differences in diseased and healthy wheat kernels from cell phone images required Wu and Chowdhary to advance the technology further.

“One of the unique things about this advance is that we trained our network to detect minutely damaged kernels with good enough accuracy using just a few images. We made this possible through meticulous pre-processing of data, transfer learning, and bootstrapping of labeling activities,” Chowdhary said. “This is another nice win for machine learning and AI for agriculture and society.”

Successfully detecting fusarium damage — small, shriveled, gray, or chalky kernels — meant the technology could also foretell the grain’s toxin load; the more external signs of damage, the greater the DON content.

When the team tested the machine learning technology alone, it was able to predict DON levels better than in-field ratings of disease symptoms, which breeders often rely on instead of kernel phenotyping to save time and resources. But when compared to humans rating disease damage on kernels in the lab, the technology was only 60 percent as accurate.

The researchers are still encouraged, though, as their initial tests didn’t use a large number of samples to train the model. They’re currently adding samples and expect to achieve greater accuracy with additional tweaking.

Rutkoski says the ultimate goal is to create an online portal where breeders like her could upload cell phone photos of wheat kernels for automatic scoring of fusarium damage.

“A tool like this could save weeks of time in a lab, and that time is critical when you’re trying to analyze the data and prepare the next trial. And ultimately, the more efficiency we can bring to the process, the faster we can improve resistance to the point where scab can be eliminated as a problem,” she said.

Discovery of Root Anatomy Gene May Lead to More Resilient Corn Crops

A new discovery, reported in a global study that encompassed more than a decade of research, could lead to the breeding of corn crops that can withstand drought and low-nitrogen soil conditions and ultimately ease global food insecurity, according to a Penn State-led team of international researchers.

In findings published in the Proceedings of the National Academy of Science, the researchers identified a gene encoding a transcription factor — a protein useful for converting DNA into RNA — that triggers a genetic sequence responsible for the development of an important trait enabling corn roots to acquire more water and nutrients.

That observable trait, or phenotype, is called root cortical aerenchyma and results in air passages forming in the roots, according to research team leader Jonathan Lynch, professor of plant science. His team at Penn State has shown that this phenotype makes roots metabolically cheaper, enabling them to explore the soil better and capture more water and nutrients from dry, infertile soil.

Now, identifying the genetic mechanism behind the trait creates a breeding target. The study used powerful genetic tools developed in previous research at Penn State to accomplish “high-throughput phenotyping” to measure characteristics of thousands of roots in a short time.

Employing technologies such as Laser Ablation Tomography and the Anatomics Pipeline, along with genome-wide association studies, the team found the gene — a “bHLH121 transcription factor” — that causes corn to express root cortical aerenchyma. But locating and then validating the genetic underpinnings of the root trait required a prolonged effort.

“We first performed the field experiments that went into this study starting in 2010, growing more than 500 lines of corn at sites in Pennsylvania, Arizona, Wisconsin and South Africa,” said Hannah Schneider, assistant professor of crop physiology at Wageningen University & Research, Netherlands. “I worked at all those locations. We saw convincing evidence that we had located a gene associated with root cortical aerenchyma.”

But proving the concept took a long time. The researchers created multiple mutant corn lines using genetic manipulation methods such as the CRISPR/Cas9 gene-editing system and gene knockouts to show the causal association between the transcription factor and formation of root cortical aerenchyma.

In the paper, the researchers reported that functional studies revealed that the mutant corn line with the bHLH121 gene knocked out and a CRISPR/Cas9 mutant line in which the gene was edited to suppress its function both showed reduced root cortical aerenchyma formation. In contrast, an overexpression line exhibited significantly greater root cortical aerenchyma formation when compared to the wildtype corn line.

Characterization of these lines under suboptimal water and nitrogen availability in multiple soil environments revealed that the bHLH121 gene is required for root cortical aerenchyma formation, according to the researchers. And the overall validation of the bHLH121 gene’s importance in root cortical aerenchyma formation, they propose, provides a new marker for plant breeders to select varieties with improved soil exploration, and thus yield, under suboptimal conditions.

The results are significant, according to Lynch, because finding a gene behind an important trait that’s going to help plants have better drought tolerance and better nitrogen and phosphorus capture looms large in the face of climate change.

Researchers find clue to help plants grow with low phosphorus levels

Phosphorus is a natural mineral that is essential for plant growth and development, and Earth’s agricultural-grade phosphorus reserves are expected to be depleted in 50 to 100 years. A new discovery by researchers at Michigan State University and the Carnegie Institution for Science is changing their understanding of iron toxicity in plants caused by low phosphorus levels.

“Once the world’s supply is used up, we can’t make more phosphorus,” said Hatem Rouached, an assistant professor in MSU’s College of Agriculture and Natural Resources. “Ideally, we would like to be able to use less phosphorus in the soil to grow plants.”

Plants absorb phosphorus from the soil. When soil doesn’t contain enough phosphorus, plants will take up more iron from the soil, which becomes toxic at increased levels. Previous research supported the idea that iron toxicity caused a plant’s roots to stop growing. Now, for the first time, researchers at MSU and the Carnegie Institution for Science have found evidence that the plant roots stop growing early, without any evidence of iron. This changes the way researchers look at this problem.

“If iron toxicity is the cause, then why does the root stop growing before iron accumulates in the roots?” said Seung Yon Rhee, incoming director of MSU’s Plant Resilience Institute. “We knew there must be something else happening.”

Using computational models to build gene regulatory networks, Rouached, Rhee and their team isolated a specific gene called an Arabidopsis root-specific kinase 1 that regulates the target of rapamycin, or TOR, complex, which is the key developmental regulator in plants, fungi and animals. When a plant is starved of phosphorus, the gene downregulates the TOR complex, which sends a signal to the root of the plant to stop growing.

“This is the first time anyone has linked a phosphorus deficiency signal to a TOR kinase in vascular plants,” said Rhee.

The researchers have filed a patent on this process and plan to explore other applications of this gene.

“We believe that this is a game changer in the field of plant mineral nutrition,” said Rouached. “We want to engineer plants whose roots will continue to grow despite phosphorus limitation.”

The research was published in the journal Current Biology.