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Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains.

Food chemistry 39154465
High-throughput and low-cost quantification of the nutrient content in crop grains is crucial for food processing and nutritional research. However, traditional methods are time-consuming and destructive. A high-throughput and low-cost method of quan...
Deep Learning Edible Grain High-Throughput Screening Assays Hyperspectral Imaging Nutrients Nutritive Value Seeds Spectroscopy, Near-Infrared Triticum
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Research on variety identification of common bean seeds based on hyperspectral and deep learning.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy 39348737
Accurate, fast and non-destructive identification of varieties of common bean seeds is important for the cultivation and efficient utilization of common beans. This study is based on hyperspectral and deep learning to identify the varieties of common...
Algorithms Deep Learning Hyperspectral Imaging Neural Networks, Computer Phaseolus Seeds Support Vector Machine
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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 39609502
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...
Food Storage Machine Learning Neural Networks, Computer Seeds
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Barley Grain Proteome Assessment Using Multi-Environment Trial Data and Machine Learning.

Journal of agricultural and food chemistry 39536264
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...
Edible Grain Environment Genotype Hordeum Machine Learning Mass Spectrometry Plant Proteins Proteome Proteomics Seeds
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Predicting physicochemical properties of papayas (Carica papaya L.) using a convolutional neural networks model approach.

Journal of food science 39415071
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...
Carica Fruit Hydrogen-Ion Concentration Image Processing, Computer-Assisted Neural Networks, Computer 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 39753082
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...
alpha-Amylases Antioxidants Biphenyl Compounds Deep Eutectic Solvents Glycerol Green Chemistry Technology Hypoglycemic Agents Neural Networks, Computer Phenols Picrates Plant Extracts Seeds Sodium Acetate Strychnos
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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 39742624
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...
Algorithms Camellia Deep Learning Hyperspectral Imaging Least-Squares Analysis Neural Networks, Computer Seeds Spectroscopy, Near-Infrared Support Vector Machine Water
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Machine learning-based prediction of compost maturity and identification of key parameters during manure composting.

Bioresource technology 39732375
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...
Composting Electric Conductivity Germination Machine Learning Manure Neural Networks, Computer Nitrogen Seeds Soil
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A rapid method for assessing seed drought resistance using integrated ID-BOA-SVM.

Analytical methods : advancing methods and applications 39498515
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 Drought Resistance Droughts Seeds Spectroscopy, Near-Infrared Support Vector Machine Water Zea mays
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MRI-Seed-Wizard: combining deep learning algorithms with magnetic resonance imaging enables advanced seed phenotyping.

Journal of experimental botany 39383098
Evaluation of relevant seed traits is an essential part of most plant breeding and biotechnology programmes. There is a need for non-destructive, three-dimensional assessment of the morphometry, composition, and internal features of seeds. Here, we i...
Algorithms Deep Learning Hordeum Magnetic Resonance Imaging Phenotype Seeds Triticum
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