We can't find the internet
Attempting to reconnect
Something went wrong!
Hang in there while we get back on track
Detection and cross-organ characterization of physiological response to microplastic stress in Panax ginseng based on hyperspectral imaging assisted with machine learning
AI summary Read the abstract
Scientists found that microplastic pollution stresses out ginseng plants, causing measurable changes in their chlorophyll, sugars, and antioxidant levels—key markers of the plant's health and medicinal quality. Using special camera imaging and AI, they could detect this stress just by scanning the leaves, without digging up the roots, showing a fast way to check if popular herbal supplements are being grown in contaminated environments. This matters because it suggests microplastics in soil could be quietly affecting the quality of medicinal plants many people rely on for health remedies.
Microplastic pollution can affect growth and quality of medicinal plants, yet rapid detection of microplastic stress responses remains underexplored. We treated ginseng with polyethylene microplastics, acquired leaf hyperspectral images (HSI) on day 23, and constructed machine learning models for identifying stress levels and predicting physiological indicators. Furthermore, the applicability of successive projections algorithm (SPA) and competitive adaptive reweighted sampling (CARS) for characteristic wavelength selection was compared. Results showed that polyethylene stress significantly affected the physiological state. The classification models effectively identified microplastic stresses of different concentrations, with the support vector machine (SVM) model performing the best (accuracy of 85.2%). For quantitative prediction, the partial least squares regression (PLSR) model exhibited optimal performance for indicators including chlorophyll (Chl) (RPD = 3.98), soluble sugar (RPD = 2.56) and peroxidase (POD) (RPD = 2.89), and the convolutional neural network performed better in superoxide dismutase (SOD) prediction (aerial RPD = 3.27, underground RPD = 2.65). Leaf spectral data enabled prediction of aerial and underground physiological indicators (RPD = 2.10 to 2.73), indicating that aerial spectral information reflected underground physiological state. Characteristic wavelength selection results showed that SPA had advantages for SOD prediction, while CARS performed better for the remaining seven indicators (RPD >2.0). In conclusion, HSI combined with machine learning models enabled rapid nondestructive identification of microplastic stress responses and prediction of key physiological indicators in ginseng, suggesting quantifiable relationships between aerial spectral data and underground physiological states. This study provides a technical prototype for the growth detection of medicinal plants.
More Papers Like This
Detection of microplastics stress on rice seedling by visible/near-infrared hyperspectral imaging and synchrotron radiation Fourier transform infrared microspectroscopy
AI summary Read the abstract
Researchers used visible/near-infrared hyperspectral imaging combined with synchrotron radiation analysis and deep learning to detect physiological and biochemical stress responses in rice seedlings exposed to microplastics, developing a rapid and interpretable early-detection method for microplastic stress in crops.
From metabolic reprogramming to diagnostic tool: A machine learning framework identifies biomarkers for soil microplastic stress in rice
AI summary Read the abstract
Microplastics in soil don't just pollute farmland—they actually change the internal chemistry of rice plants, disrupting how they process energy and nutrients. Scientists used AI to spot just seven chemical "warning signs" in rice that can reveal how contaminated the soil is with 90% accuracy, potentially offering a fast way to screen crops for microplastic pollution before they reach your plate. This matters because it could help catch contaminated food supplies early, though more research is needed to know exactly how these changes affect the safety or nutrition of the rice we eat.
Integrating automated machine learning and metabolic reprogramming for the identification of microplastic in soil: A case study on soybean
AI summary Read the abstract
Scientists used automated machine learning to detect microplastic contamination in soybean plants by analyzing changes in the plants' metabolism and antioxidant systems. The technology could identify microplastic-contaminated crops with high accuracy, even when pesticides were also present. This rapid detection method could help monitor food crop safety and identify fields where microplastic pollution threatens the food supply.
Study on detection method of microplastics in farmland soil based on hyperspectral imaging technology
AI summary Read the abstract
Researchers developed a method using hyperspectral imaging and machine learning to rapidly detect and classify different types of microplastics in farmland soil. The technology achieved high accuracy in identifying common plastic types like polyethylene and polypropylene in soil samples. Better detection tools like this are essential for monitoring microplastic contamination in agricultural land and understanding its potential impact on food safety.
2T2D spectroscopy and deep learning for soil MPs quantification
AI summary Read the abstract
Scientists have developed an AI-powered tool that can accurately measure how much microplastic (tiny plastic particles) is hiding in soil, using light-based scanning combined with deep learning. This matters because microplastics in soil can end up in the food we grow and eventually in our bodies, so having a fast, reliable way to detect and measure them is a key step toward understanding—and eventually reducing—our exposure to this pollution.
Research digests by email
When a large batch of papers lands in the Atlas, we read through it and send a short write-up of what stood out.