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Biomass

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Mangrove forest classification and aboveground biomass estimation using an atom search algorithm and adaptive neuro-fuzzy inference system.

PloS one
BACKGROUND: Advances in earth observation and machine learning techniques have created new options for forest monitoring, primarily because of the various possibilities that they provide for classifying forest cover and estimating aboveground biomass...

A Comparison between Several Response Surface Methodology Designs and a Neural Network Model to Optimise the Oxidation Conditions of a Lignocellulosic Blend.

Biomolecules
In this paper, response surface methodology (RSM) designs and an artificial neural network (ANN) are used to obtain the optimal conditions for the oxy-combustion of a corn-rape blend. The ignition temperature () and burnout index () were selected as ...

Deep Green Diagnostics: Urban Green Space Analysis Using Deep Learning and Drone Images.

Sensors (Basel, Switzerland)
Nowadays, more than half of the world's population lives in urban areas, and this number continues increasing. Consequently, there are more and more scientific publications that analyze health problems of people associated with living in these highly...

Effects of corn straw on dissipation of polycyclic aromatic hydrocarbons and potential application of backpropagation artificial neural network prediction model for PAHs bioremediation.

Ecotoxicology and environmental safety
In order to provide a viable option for remediation of PAHs-contaminated soils, a greenhouse experiment was conducted to assess the effect of corn straw amendment (1%, 2%, 4% or 6%, w/w) on dissipation of aged polycyclic aromatic hydrocarbons (PAHs) ...

Increasing productivity of Spirulina platensis in photobioreactors using artificial neural network modeling.

Biotechnology and bioengineering
Although production of microalgae in open ponds is conventionally practiced due to its economy, exposure of the algae to uncontrollable elements impedes achievement of quality and it is desirable to develop closed reactor cultivation methods for the ...

The application of machine learning methods for prediction of metal sorption onto biochars.

Journal of hazardous materials
The adsorption of six heavy metals (lead, cadmium, nickel, arsenic, copper, and zinc) on 44 biochars were modeled using artificial neural network (ANN) and random forest (RF) based on 353 dataset of adsorption experiments from literatures. The regres...

Non-Invasive Sensing of Nitrogen in Plant Using Digital Images and Machine Learning for ssp. L.

Sensors (Basel, Switzerland)
Monitoring plant nitrogen (N) in a timely way and accurately is critical for precision fertilization. The imaging technology based on visible light is relatively inexpensive and ubiquitous, and open-source analysis tools have proliferated. In this st...

Estimation of fungal biomass using multiphase artificial neural network based dynamic soft sensor.

Journal of microbiological methods
Interest in low cost cellulase production has become a major challenge in recent years for biorefineries. Fed-batch fermentation of Trichoderma strains for the production of low cost cellulase is carried out on complex media that has various soluble ...

Application of recurrent neural network for online prediction of cell density of recombinant Pichia pastoris producing HBsAg.

Preparative biochemistry & biotechnology
Artificial neural networking (ANN) seems to be a promising soft sensor for implementing current approaches of quality by design (QbD) and process analytical technologies (PAT) in the biopharmaceutical industry. In this study, we aimed to implement be...

Gradients of three coastal environments off the South China Sea and their impacts on the dynamics of heterotrophic microbial communities.

The Science of the total environment
Heterotrophic fungus-like marine protists are recognized to contribute significantly to the coastal carbon cycling largely due to their high biomass and ability to decompose recalcitrant organic matter. Yet, little is known about their dynamics at po...