A study of volatile organic compounds (VOCs) emitted by new cars on hot summer days finds concerning levels of formaldehyde and other aldehydes.
A research team has investigated the efficacy of AlexNet, an advanced Convolutional Neural Network (CNN) variant, for automatic crop classification using high-resolution aerial imagery from UAVs. Their findings demonstrated ...
Skoltech researchers and their colleagues from Sber have proposed deep learning models that predict droughts using climate data. Long-term forecasts of this kind are sought by agricultural producers planning their operations, ...
Amidst the challenges posed by climate change in predicting typhoons, a team of researchers has developed a technology that leverages real-time satellite data and deep learning capabilities to predict typhoons with greater ...
Imagine you are a law enforcement official at a wildlife market and suspect some of the birds on sale are from endangered or illegally traded populations. This is a situation that demands decisive identification and action, ...
A research team has developed a novel method combining computer vision and deep learning to phenotype drought-stressed poplar saplings, achieving 99% accuracy in variety identification and 76% accuracy in stress-level classification.
A research team has developed LGNet, a dual-branch network that combines convolutional neural networks (CNNs) and visual transformers (VTs) for plant disease identification. LGNet effectively fuses local and global features, ...
Soybean is an agriculturally important legume rich in protein and oil, with breeders aiming to enhance yields through traits like seed weight, shape, and pod count. Current research leverages deep learning (DL) for high-throughput ...
A research team has developed the Point-Line Net, a deep learning method based on the Mask R-CNN framework, to automatically recognize maize field images and determine the number and growth trajectory of leaves and stalks. ...
A collaborative research team has recently developed a new method for measuring glucose using deep learning technology. Their research was published in Laser & Photonics Reviews.
