The increasing frequency of extreme weather events and global population growth present challenges to food security, emphasizing the need for resilient and high-yielding crops. Central to this challenge is understanding Root ...
Fusarium head blight (FHB) is a widespread floral disease in wheat that causes significant yield losses and produces harmful mycotoxins, posing serious health risks. Recent research has focused on improving disease detection, ...
Image-based plant phenotyping has become crucial in understanding plant biology and agriculture. However, distinguishing relevant biological structures from the background remains a challenge, particularly in low-contrast ...
Cereals, crucial for food and biofuel, are increasingly using genetically modified (GM) technology in production to enhance resistance and nutrition. Despite biosafety concerns, precise detection methods like PCR are complex ...
Scientists from the University of Rochester say deep learning can supercharge a technique that is already the gold standard for characterizing new materials. In an npj Computational Materials paper, the interdisciplinary ...
Crowdsourcing has become pivotal in scientific research, particularly in data-intensive fields like plant phenotyping, leveraging platforms like Kaggle for data analysis and machine learning challenges.
The accurate measurement of the green fraction (GF), a critical photosynthetic trait in crops, typically relies on RGB image analysis employing segmentation algorithms to identify green pixels within the crop. Traditional ...
Swift plant disease diagnosis is vital to prevent extensive production losses and uphold food security. Recently, object detection-based methods using deep learning have shown promise in accurately identifying and locating ...
A team of computational scientists at the Department of Energy's Oak Ridge National Laboratory has generated and released datasets of unprecedented scale that provide the ultraviolet visible spectral properties of over 10 ...
Strong precipitation may cause natural disasters, such as floodings or landslides. Global climate models are required to forecast the frequency of these extreme events, which is expected to change as a result of climate change. ...
