machine learning – Page 2

Artificial Neural Network Prediction of Mechanical Properties in Mycelium-Based Biocomposites

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Researchers used artificial intelligence to predict how strong mushroom-based materials would be. These eco-friendly composites are made from wood particles held together by fungal networks instead of synthetic glue. The AI model successfully learned to predict the strength of these materials based on which type of fungus was used and what wood particles they were grown on, potentially reducing the need for expensive testing.

Polysaccharide prediction in Ganoderma lucidum fruiting body by hyperspectral imaging

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Researchers developed a quick, damage-free method to measure the health-promoting polysaccharide content in Ganoderma lucidum mushrooms using special imaging technology that analyzes light reflection. This technology combines visible and near-infrared light imaging with computer learning to predict polysaccharide levels across the entire mushroom cap. The method achieved 92.4% accuracy and could help mushroom farmers determine the best time to harvest for maximum nutritional value.

Differential responses of Cacao pathogens Colletotrichum gloeosporioides and Pestalotiopsis sp. to UVB 305 nm and UVC 275 nm

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Scientists studied how UV light can be used to fight fungal diseases that harm cacao plants. They found that UVC light (a type of ultraviolet radiation) is much more effective at killing these fungi than UVB light. Some fungi were very resistant to UV treatment, but the researchers discovered that combining UV light with sound waves (sonication) could overcome this resistance, offering a chemical-free way to protect crops.

Classification of Mycena and Marasmius Species Using Deep Learning Models: An Ecological and Taxonomic Approach

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Researchers developed an artificial intelligence system to automatically identify mushroom species from the Mycena and Marasmius groups by analyzing photographs. Using advanced computer vision and machine learning techniques, they achieved 98.9% accuracy in classification. This technology could help scientists, conservationists, and nature enthusiasts quickly identify mushroom species in the field, supporting biodiversity research and conservation efforts.

Automatic classification of fungal-fungal interactions using deep learning models

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Researchers developed a computer artificial intelligence system that can automatically analyze images of fungal interactions to identify strains that could help control harmful crop diseases. Instead of having humans manually examine thousands of fungal culture plates—a slow and subjective process—the AI system can now classify the interactions between beneficial fungi and plant pathogens with 95% accuracy. This breakthrough significantly speeds up the search for natural alternatives to synthetic pesticides, supporting sustainable agriculture and food security.

The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products

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Food companies need to know how long products stay fresh and safe to eat. This review explains different scientific methods for predicting when food will spoil based on microbial growth. It compares traditional mathematical models with newer computer-based machine learning approaches, showing that newer methods can be more accurate and efficient for determining how long foods can be stored safely.

Deep Ensemble Learning and Explainable AI for Multi-Class Classification of Earthstar Fungal Species

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Scientists created an artificial intelligence system that can identify eight types of earthstar mushrooms from photographs with over 96% accuracy. These mushrooms look very similar to each other, making them difficult to tell apart by eye alone. The AI system not only identifies the mushrooms correctly but also shows which parts of the image it looked at to make its decision, making it transparent and trustworthy. This technology could help scientists monitor wild mushroom populations and improve sustainable farming practices.

Automatic classification of fungal-fungal interactions using deep learning models

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Researchers developed an artificial intelligence system that automatically analyzes images of fungi growing together to identify which ones can fight off disease-causing fungi. Instead of having humans manually look at thousands of plate images, which is time-consuming and subjective, their computer vision system can classify the outcomes with 95% accuracy. This automation tool could help scientists quickly find beneficial fungi that could replace chemical pesticides in agriculture, supporting the goal of sustainable and more environmentally friendly farming.

Integrating Machine Learning and Molecular Methods for Trichophyton indotineae Identification and Resistance Profiling Using MALDI-TOF Spectra

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A new type of fungus called Trichophyton indotineae is causing stubborn skin infections that don’t respond well to standard antifungal treatments. Researchers used advanced laboratory techniques combined with computer analysis to better identify this fungus from MALDI-TOF spectra, which is a quick fingerprinting method for microorganisms. The study showed that machine learning could accurately distinguish this problematic fungus from similar species and found specific markers that could help clinics detect it faster, potentially improving patient treatment outcomes.

Application of ATR-FTIR and FT-NIR spectroscopy coupled with chemometrics for species identification and quality prediction of boletes

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Researchers developed a fast and non-destructive method to identify different types of edible boletes and assess their nutritional quality by analyzing their amino acid content. Using special spectroscopy techniques combined with computer analysis, they achieved perfect accuracy in identifying five bolete species and could predict the amino acid content that contributes to flavor and nutrition. This breakthrough provides consumers with better protection against accidentally purchasing toxic mushroom species that look similar to edible ones, while helping food producers quickly assess quality without lengthy lab testing.

Research Topic: machine learning