AIMC Topic: Biomass
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Spatiotemporal variation in biomass abundance of different algal species in Lake Hulun using machine learning and Sentinel-3 images.
Scientific reports
Jan 21, 2025
Climate change and human activities affect the biomass of different algal and the succession of dominant species. In the past, phytoplankton phyla inversion has been focused on oceanic and continental shelf waters, while phytoplankton phyla inversion...
Machine Learning-Assisted Biomass-Derived Carbon Dots as Fluorescent Sensor Array for Discrimination of Warfarin and Its Metabolites.
Langmuir : the ACS journal of surfaces and colloids
Jan 11, 2025
Warfarin (WAR), an effective oral anticoagulant, is of utmost importance in treating many diseases. Despite its significance, rapid and precise discrimination of WAR remains a formidable challenge, especially facing its structural analogs of metaboli...
Aboveground biomass estimation in a grassland ecosystem using Sentinel-2 satellite imagery and machine learning algorithms.
Environmental monitoring and assessment
Jan 6, 2025
The grassland ecosystem forms a critical part of the natural ecosystem, covering up to 15-26% of the Earth's land surface. Grassland significantly impacts the carbon cycle and climate regulation by storing carbon dioxide. The organic matter found in ...
Nutrient based classification of Phyllospora comosa biomasses using machine learning algorithms: Towards sustainable valorisation.
Food research international (Ottawa, Ont.)
Dec 31, 2024
Sustainable seaweed value chains necessitate accurate biomass biochemical characterisation that leads to product development, geographical authentications and quality and sustainability assurances. Underutilised yet abundantly available seaweed speci...
Recent advances in dark fermentative hydrogen production from vegetable waste: role of inoculum, consolidated bioprocessing, and machine learning.
Environmental science and pollution research international
Dec 6, 2024
Waste-centred-bioenergy generation have been garnering interest over the years due to environmental impact presented by fossil fuels. Waste generation is an unavoidable consequence of urbanization and population growth. Sustainable waste management t...
Comparing statistical and deep learning approaches for simultaneous prediction of stand-level above- and belowground biomass in tropical forests.
The Science of the total environment
Dec 5, 2024
Accurate and cost-effective prediction of aboveground biomass (AGB), belowground biomass (BGB), and the total (ABGB) at stand-level within tropical forests is crucial for effective forest ecological management and the provision of forest ecosystem se...
Harnessing Artificial Intelligence for Sustainable Bioenergy: Revolutionizing Optimization, Waste Reduction, and Environmental Sustainability.
Bioresource technology
Nov 27, 2024
Assessing the mutual benefits of artificial intelligence (AI) and bioenergy systems, to promote efficient and sustainable energy production. By addressing issues with conventional bioenergy techniques, it highlights how AI is revolutionising optimisa...
A review on the role of various machine learning algorithms in microwave-assisted pyrolysis of lignocellulosic biomass waste.
Journal of environmental management
Nov 11, 2024
The fourth industrial revolution will heavily rely on machine learning (ML). The rationale is that these strategies make various business operations in many sectors easier. ML modeling is the discovery of hidden patterns between multiple process para...
Graph-learning-based machine learning improves prediction and cultivation of commercial-grade marine microalgae Porphyridium.
Bioresource technology
Nov 7, 2024
A graph learning [Binarized Attributed Network Embedding (BANE)] model enhances the single-target and multi-target prediction performances of random forest and eXtreme Gradient Boosting (XGBoost) by learning complex interrelationships between cultiva...
Forest Aboveground Biomass Estimation Based on Unmanned Aerial Vehicle-Light Detection and Ranging and Machine Learning.
Sensors (Basel, Switzerland)
Nov 2, 2024
is a widely planted species in plantation forests because of its outstanding characteristics, such as fast growth rate and high adaptability. Accurate and rapid prediction of biomass is important for plantation forest management and the prediction ...
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