Research Keyword: DenseNet121

Automatic classification of fungal-fungal interactions using deep learning models

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.

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Automatic classification of fungal-fungal interactions using deep learning models

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.

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