AIMC Topic: Nutrients

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Comprehensive nutrient analysis in agricultural organic amendments through non-destructive assays using machine learning.

PloS one
Portable X-ray fluorescence (pXRF) and Diffuse Reflectance Fourier Transformed Mid-Infrared (DRIFT-MIR) spectroscopy are rapid and cost-effective analytical tools for material characterization. Here, we provide an assessment of these methods for the ...

Deep Learning for Non-Invasive Diagnosis of Nutrient Deficiencies in Sugar Beet Using RGB Images.

Sensors (Basel, Switzerland)
In order to enable timely actions to prevent major losses of crops caused by lack of nutrients and, hence, increase the potential yield throughout the growing season while at the same time prevent excess fertilization with detrimental environmental c...

Using Deep Convolutional Neural Networks for Image-Based Diagnosis of Nutrient Deficiencies in Rice.

Computational intelligence and neuroscience
Symptoms of nutrient deficiencies in rice plants often appear on the leaves. The leaf color and shape, therefore, can be used to diagnose nutrient deficiencies in rice. Image classification is an efficient and fast approach for this diagnosis task. D...

Application of artificial neural network and multiple linear regression in modeling nutrient recovery in vermicompost under different conditions.

Bioresource technology
Vermicomposting is one of the best technologies for nutrient recovery from solid waste. This study aims to assess the efficiency of Artificial Neural Network (ANN) and Multiple Linear Regression (MLR) models in predicting nutrient recovery from solid...

Using machine learning to estimate herbage production and nutrient uptake on Irish dairy farms.

Journal of dairy science
Nutrient management on grazed grasslands is of critical importance to maintain productivity levels, as grass is the cheapest feed for ruminants and underpins these meat and milk production systems. Many attempts have been made to model the relationsh...

Phenotyping Women Based on Dietary Macronutrients, Physical Activity, and Body Weight Using Machine Learning Tools.

Nutrients
Nutritional phenotyping can help achieve personalized nutrition, and machine learning tools may offer novel means to achieve phenotyping. The primary aim of this study was to use energy balance components, namely input (dietary energy intake and macr...

Shallow convective mixing promotes massive Noctiluca scintillans bloom in the northeastern Arabian Sea.

Marine pollution bulletin
The northeastern Arabian Sea (NEAS) experiences convective mixing during winter, but this mixing does not reach up to the silicicline, resulting in the limited supply of silicate (Si) compared to nitrate (N) and phosphate (P) to the mixed layer (ML) ...

Comparison between chemometrics and machine learning for the prediction of macronutrients in cheese using Imaging spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
Traditional methods for assessing cheese's nutritional content are often labor-intensive, destructive, and environmentally taxing. This study explores the non-destructive spectral imaging technique, also called Hyperspectral Imaging (HSI) combined wi...

Morphotype-resolved characterization of microalgal communities in a nutrient recovery process with ARTiMiS flow imaging microscopy.

Water research
Microalgae-driven nutrient recovery represents a promising technology for phosphorus removal from wastewater while simultaneously generating biomass that can be valorized to offset treatment costs. As full-scale processes come online, system paramete...

Navigating nutrients: A scoping review on real-time food nutrition classification and recommendation systems.

Computers in biology and medicine
In an era where fast-paced lifestyles often conflict with the pursuit of healthy eating, the demand for innovative solutions to aid nutritional decision-making has never been more pressing. Real-time food nutrition classification and recommendation s...