I believe that vertical farming will be able to meet the demand of 9.7 billion people by 2050 or even be able to feed eventually the entire globe or even space stations. The leading vertical farming company I like is aero farms:3.
By 2050, we’ll need to produce 70% more food to feed over 9 billion mouths. Luckily, a wide range of vertical farming companies are developing innovative solutions to redefine production, expand urban agriculture and transform consumers into green-fingered growers.
PlantRNA-FM, an AI model trained on RNA data from over 1,100 plants, decodes genetic patterns to advance plant science, improve crops, and tackle global agricultural challenges.
A groundbreaking Artificial Intelligence (AI) model designed to decode the sequences and structural patterns that form the genetic “language” of plants has been launched by a research collaboration.
Named Plant RNA-FM, this innovative model is the first of its kind and was developed by a partnership between plant researchers at the John Innes Centre and computer scientists at the University of Exeter.
Precision agriculture leverages cutting-edge machine learning algorithms to transform farming, boosting productivity and sustainability. From Random Forest for crop classification to CNNs for high-resolution imagery analysis, these tools optimize resources, detect diseases early, and improve yield prediction. Discover the top algorithms shaping modern agriculture and how they empower smarter, data-driven decisions.
A study published in the journal Optica demonstrates live plant imaging of several representative plant samples, including the biofuel crop sorghum. By employing a novel detector, researchers obtained clear images of living sorghum plants with a light far dimmer than starlight. This advance enables imaging of delicate, light-sensitive samples, such as biofuel crops, without disturbing or damaging the plants.
A method called quantum ghost imaging (QGI) allows scientists to capture images at extremely low light levels. QGI also enables the use of one low intensity color, best matched to the sample and a different color at higher intensity, sufficient to form the image of the sample. This approach improves imaging in regions of light where traditional cameras struggle.
By using label-free infrared imaging, researchers can gather critical information about important plant processes, such as water content and photosynthesis, even in low-light conditions. This is particularly beneficial for studying biofuel crops, where researchers want to optimize plant growth and health to maximize yield and sustainability.
Inside every jar of honey lies a taste of the local environment. Its sticky-sweet flavor is shaped by the flowers that nearby bees choose to sample. However, a new study from Tulane University has revealed that honey can also provide insights into local pollution.
The study, published in Environmental Pollution, analyzed 260 honey samples from 48 states for traces of six toxic metals: arsenic, lead, cadmium, nickel, chromium, and cobalt. None of the samples contained unsafe levels of these metals based on a typical serving size of one tablespoon per day, and the concentrations in the United States were generally lower than global averages. Still, researchers identified regional variations in toxic metal distribution: the highest arsenic levels were detected in honey from a cluster of Pacific Northwest states (Oregon, Idaho, Washington, and Nevada); the Southeast, including Louisiana and Mississippi, showed the highest cobalt levels; and two of the three highest lead levels were found in samples from the Carolinas.
Overall, the study highlights a potential dual role for honey as both a food source and a tool for monitoring environmental pollution.
MIT researchers have developed an environmentally friendly alternative to the harmful microbeads used in some health and beauty products.
These new polymers break down into harmless sugars and amino acids and could also encapsulate nutrients for food fortification, showing promise in both cosmetic and nutritional applications.
Biodegradable Solutions by MIT.
UNIVERSITY PARK, Pa. — A recently developed electronic tongue is capable of identifying differences in similar liquids, such as milk with varying water content; diverse products, including soda types and coffee blends; signs of spoilage in fruit juices; and instances of food safety concerns. The team, led by researchers at Penn State, also found that results were even more accurate when artificial intelligence (AI) used its own assessment parameters to interpret the data generated by the electronic tongue.
(Many people already posted this. This is the press release from Penn Sate who did the research)
The tongue comprises a graphene-based ion-sensitive field-effect transistor, or a conductive device that can detect chemical ions, linked to an artificial neural network, trained on various datasets. Critically, Das noted, the sensors are non-functionalized, meaning that one sensor can detect different types of chemicals, rather than having a specific sensor dedicated to each potential chemical. The researchers provided the neural network with 20 specific parameters to assess, all of which are related to how a sample liquid interacts with the sensor’s electrical properties. Based on these researcher-specified parameters, the AI could accurately detect samples — including watered-down milks, different types of sodas, blends of coffee and multiple fruit juices at several levels of freshness — and report on their content with greater than 80% accuracy in about a minute.
“After achieving a reasonable accuracy with human-selected parameters, we decided to let the neural network define its own figures of merit by providing it with the raw sensor data. We found that the neural network reached a near ideal inference accuracy of more than 95% when utilizing the machine-derived figures of merit rather than the ones provided by humans,” said co-author Andrew Pannone, a doctoral student in engineering science and mechanics advised by Das. “So, we used a method called Shapley additive explanations, which allows us to ask the neural network what it was thinking after it makes a decision.”
This approach uses game theory, a decision-making process that considers the choices of others to predict the outcome of a single participant, to assign values to the data under consideration. With these explanations, the researchers could reverse engineer an understanding of how the neural network weighed various components of the sample to make a final determination — giving the team a glimpse into the neural network’s decision-making process, which has remained largely opaque in the field of AI, according to the researchers. They found that, instead of simply assessing individual human-assigned parameters, the neural network considered the data it determined were most important together, with the Shapley additive explanations revealing how important the neural network considered each input data.
Higher levels of omega-6 fatty acids often found in ultraprocessed foods may interfere with the immune system’s fight against cancer cells, a new study says.
Dopamine is a powerful signal in the brain, influencing our moods, motivations, movements, and more. The neurotransmitter is crucial for reward-based learning, a function that may be disrupted in a number of psychiatric conditions, from mood disorders to addiction.
Now, researchers led by MIT Institute Professor Ann Graybiel have found surprising patterns of dopamine signaling that suggest neuroscientists may need to refine their model of how reinforcement learning occurs in the brain. The team’s findings were published recently in the journal Nature Communications.
Dopamine plays a critical role in teaching people and other animals about the cues and behaviors that portend both positive and negative outcomes; the classic example of this type of learning is the dog that Ivan Pavlov trained to anticipate food at the sound of bell.
Achieving the aggregation of different mutation types at multiple genomic loci and generating transgene-free plants in the T0 generation is an important goal in crop breeding. Although prime editing (PE), as the latest precise gene editing technology, can achieve any type of base substitution and small insertions or deletions, there are significant differences in efficiency between different editing sites, making it a major challenge to aggregate multiple mutation types within the same plant.
Recently, a collaborative research team led by Li Jiayang from the Institute of Genetics and Developmental Biology (IGDB) of the Chinese Academy of Science, developed a multiplex gene editing tool named the Cas9-PE system, capable of simultaneously achieving precise editing and site-specific random mutagenesis in rice.
By co-editing the ALSS627I gene to confer resistance to the herbicide bispyribac-sodium (BS) as a selection marker, and using Agrobacterium-mediated transient transformation, the researchers also achieved transgene-free gene editing in the T0 generation.
