After defying multiple mass extinctions on Earth, the hardy plant passes an intergalactic test. Breakthroughs, discoveries, and DIY tips sent every weekday. While it may appear humble, Earth's moss is built darn tough. It thrives in extreme environments -from the bitter cold, low-oxygen air of the Himalayas, down to the parched sands of Death Valley. Some species even make their home among the lava fields of active volcanoes .

Is chlorophyll actually good for you? Some benefits are rooted in science, but others are just leafy lore. Thanks to social media, chlorophyll water is now an internet sensation, promising everything from clearer skin to better breath. But does the green liquid live up to the hype? Breakthroughs, discoveries, and DIY tips sent every weekday.

Ongoing advancements in computer vision, particularly in pattern recognition and scene classification, have enabled new applications in environmental monitoring. Deep learning now offers non-contact methods for assessing water quality and detecting contamination, both critical for disaster response and public health protection. This work introduces HydroVision, a deep learning-based scene classification framework that estimates optically active water quality parameters including Chlorophyll-Alpha, Chlorophylls, Colored Dissolved Organic Matter (CDOM), Phycocyanins, Suspended Sediments, and Turbidity from standard Red-Green-Blue (RGB) images of surface water. HydroVision supports early detection of contamination trends and strengthens monitoring by regulatory agencies during external environmental stressors, industrial activities, and force majeure events. The model is trained on more than 500,000 seasonally varied images collected from the United States Geological Survey Hydrologic Imagery Visualization and Information System between 2022 and 2024. This approach leverages widely available RGB imagery as a scalable, cost-effective alternative to traditional multispectral and hyperspectral remote sensing. Four state-of-the-art convolutional neural networks (VGG-16, ResNet50, MobileNetV2, DenseNet121) and a Vision Transformer are evaluated through transfer learning to identify the best-performing architecture. DenseNet121 achieves the highest validation performance, with an R2 score of 0.89 in predicting CDOM, demonstrating the framework's promise for real-world water quality monitoring across diverse conditions. While the current model is optimized for well-lit imagery, future work will focus on improving robustness under low-light and obstructed scenarios to expand its operational utility.

Marine chlorophyll which is present within phytoplankton are the basis of photosynthesis and they have a high significance in sustaining ecological balance as they highly contribute toward global primary productivity and comes under the food chain of many marine organisms. Imbalance in the concentrations of phytoplankton can disrupt the ecological balance. The growth of phytoplankton depends upon the optimum concentrations of physiochemical constituents like iron, nitrates, phosphates, pH level, salinity, etc. and deviations from an ideal concentration can affect the growth of phytoplankton which can ultimately disrupt the ecosystem at a large scale. Thus the analysis of such constituents has high significance to estimate the probable growth of marine phytoplankton. The advancements of remote sensing technologies have improved the scope to remotely study the physiochemical constituents on a global scale. The machine learning techniques have made it possible to predict the marine chlorophyll levels based on physiochemical properties and deep learning helped to do the same but in a more advanced manner simulating the working principle of a human brain. In this study, we have used machine learning and deep learning for the Bay of Bengal to establish a regression model of chlorophyll levels based on physiochemical features and discussed its reliability and performance for different regression models. This could help to estimate the amount of chlorophyll present in water bodies based on physiochemical features so we can plan early in case there arises a possibility of disruption in the ecosystem due to imbalance in marine phytoplankton.

In this paper, we present a regression framework involving several machine learning models to estimate water parameters based on hyperspectral data. Measurements from a multi-sensor field campaign, conducted on the River Elbe, Germany, represent the benchmark dataset. It contains hyperspectral data and the five water parameters chlorophyll a, green algae, diatoms, CDOM and turbidity. We apply a PCA for the high-dimensional data as a possible preprocessing step. Then, we evaluate the performance of the regression framework with and without this preprocessing step. The regression results of the framework clearly reveal the potential of estimating water parameters based on hyperspectral data with machine learning. The proposed framework provides the basis for further investigations, such as adapting the framework to estimate water parameters of different inland waters.

Out on the rocky horizon, the robot has stopped dead in its tracks. "Uh, Dave, I got a big problem out here," a voice crackles over the radio. "OK," David Wettergreen replies carefully, peering off in the direction of the machine. "Big" turns out to be a new part for the robot that doesn't quite fit and so prevents the robot's cameras--its eyes--from turning properly. Back at the laboratory, this would be a quick fix, but the robot, Wettergreen, three geologists, two software engineers, two sociologists, an electrical engineer, a mechanical engineer, and a biologist are all out in the middle of Chile's vast Atacama Desert, [see map] many hours' drive from civilization. As he strides off to investigate, you get the sense Wettergreen's enjoying himself. For the better part of an hour, he and two colleagues will wrestle with the aberrant part [see photo, " All in a Day's Work"].