AIMC Topic: Seeds

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Design and optimization of tamarind seed polysaccharide-based scaffold for tissue engineering applications using statistical modeling and machine learning, and it's in-vitro validation.

International journal of biological macromolecules Jan 30, 2025
This study explores the development and optimization of a novel biomaterial scaffold for tissue engineering, composed of Tamarind seed polysaccharide (TSP), Hydroxypropyl methylcellulose (HPMC), Chitosan (CS), and Sodium alginate (ALG). Scaffold prop...
Machine Learning Seeds Porosity Humans Neural Networks, Computer Wettability Tamarindus HeLa Cells Tissue Engineering Tissue Scaffolds Polysaccharides Biocompatible Materials
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Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models.

Sensors (Basel, Switzerland) Jan 7, 2025
Wheat is a globally cultivated cereal crop with substantial protein content present in its seeds. This research aimed to develop robust methods for predicting seed protein concentration in wheat seeds using bench-top hyperspectral imaging in the visi...
Plant Proteins Machine Learning Spectroscopy, Near-Infrared Convolutional Neural Networks Neural Networks, Computer Image Processing, Computer-Assisted Support Vector Machine Seeds Triticum Hyperspectral Imaging
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Determination and visualization of moisture content in Camellia oleifera seeds rapidly based on hyperspectral imaging combined with deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Dec 27, 2024
Moisture content (MC) is crucial for the storage, transportation, and processing of Camellia oleifera seeds. The purpose of this study was to investigate the feasibility for detecting MC in Camellia oleifera seeds using visible near-infrared hyperspe...
Water Support Vector Machine Seeds Least-Squares Analysis Hyperspectral Imaging Camellia Algorithms Spectroscopy, Near-Infrared Deep Learning Neural Networks, Computer
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Machine learning-based prediction of compost maturity and identification of key parameters during manure composting.

Bioresource technology Dec 26, 2024
Evaluating compost maturity, e.g. via manual seed germination index (GI) measurement, is both time-consuming and costly during composting. This study employed six machine learning methods, including random forest (RF), extra tree (ET), eXtreme gradie...
Nitrogen Manure Electric Conductivity Composting Soil Seeds Germination Neural Networks, Computer Machine Learning
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Non-destructive detection strategy of maize seed vigor based on seed phenotyping and the potential for accelerating breeding.

Journal of advanced research Dec 25, 2024
INTRODUCTION: Seeds are fundamental to agricultural production, and their vigor affects seedling quality, quantity, and crop yield. Accurate vigor assessment methods are crucial for agricultural productivity.
Germination Zea mays Plant Breeding Phenotype Seeds
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Deep eutectic solvent-based green extraction of Strychnos potatorum seed phenolics: Process optimization via response surface methodology and artificial neural network.

Talanta Dec 20, 2024
The current research focused on extraction optimization of bioactive compounds from Strychnos potatorum seeds (SPs) using an eco-friendly glycerol-sodium acetate based deep eutectic solvent (DES). The optimization was accomplished using response surf...
Picrates alpha-Amylases Glycerol Green Chemistry Technology Strychnos Deep Eutectic Solvents Seeds Phenols Biphenyl Compounds Plant Extracts Hypoglycemic Agents Sodium Acetate Neural Networks, Computer Antioxidants
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A rapid method for assessing seed drought resistance using integrated ID-BOA-SVM.

Analytical methods : advancing methods and applications Dec 5, 2024
This study investigates the application of near-infrared spectroscopy (NIR) for assessing drought resistance in seeds, aiming to offer a rapid and efficient method suitable for large-scale primary screening. NIR spectroscopy is utilized to analyze fo...
Bayes Theorem Zea mays Droughts Support Vector Machine Seeds Drought Resistance Spectroscopy, Near-Infrared Water
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Evaluation and prediction of the physical properties and quality of Jatobá-do-Cerrado seeds processed and stored in different conditions using machine learning models.

Scientific reports Nov 28, 2024
The conservation of seed quality throughout storage depends on established conditions, monitoring, sampling and laboratory analysis, which are subject to errors and require technical and financial resources. Thus, machine learning techniques can help...
Seeds Food Storage Neural Networks, Computer Machine Learning
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Barley Grain Proteome Assessment Using Multi-Environment Trial Data and Machine Learning.

Journal of agricultural and food chemistry Nov 13, 2024
Proteomics can be used to assess individual protein abundances, which could reflect genotypic and environmental effects and potentially predict grain/malt quality. In this study, 79 barley grain samples (genotype-location-year combinations) from Cali...
Environment Plant Proteins Hordeum Genotype Machine Learning Proteomics Mass Spectrometry Proteome Seeds Edible Grain
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Predicting physicochemical properties of papayas (Carica papaya L.) using a convolutional neural networks model approach.

Journal of food science Oct 16, 2024
The current state of quality assessment methods for agricultural produce, particularly fruits, heavily relies on manual inspection techniques, which could be subjective, time-consuming, and prone to human errors. Consequently, there have been emergin...
Neural Networks, Computer Image Processing, Computer-Assisted Carica Seeds Hydrogen-Ion Concentration Fruit
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