AIMC Topic: Seeds

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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...
Spectroscopy, Near-Infrared Water Zea mays Seeds Bayes Theorem Droughts Support Vector Machine Drought Resistance
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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...
Neural Networks, Computer Machine Learning Seeds Food Storage
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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...
Machine Learning Genotype Proteomics Hordeum Seeds Environment Edible Grain Plant Proteins Proteome Mass Spectrometry
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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...
Carica Image Processing, Computer-Assisted Hydrogen-Ion Concentration Seeds Fruit Neural Networks, Computer
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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 Sep 24, 2024
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...
Phaseolus Seeds Hyperspectral Imaging Neural Networks, Computer Deep Learning Support Vector Machine Algorithms
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Effect of ethanol extract of nigella sativa L seeds and propofol on BDNF protein level as neuroplasticity and neuroprotection of traumatic brain injury in rats.

F1000Research Sep 23, 2024
BACKGROUND: Traumatic brain injury (TBI) is a change in brain function or evidence of brain pathology caused by external mechanical forces. Brain Derived Neurotrophic Factor (BDNF) is a neurotropin that functions as a neuron protective. Nigella sativ...
Neuroprotection Propofol Plant Extracts Nigella sativa Rats, Sprague-Dawley Neuronal Plasticity Neuroprotective Agents Brain Injuries, Traumatic Rats Animals Seeds Ethanol Brain-Derived Neurotrophic Factor Male
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Machine learning approach for high-throughput phenolic antioxidant screening in black Rice germplasm collection based on surface FTIR.

Food chemistry Aug 2, 2024
Pigmented rice contains beneficial phenolic antioxidants but analysing them across germplasm collections is laborious and time-consuming. Here we utilised rapid surface Fourier transform infrared (FTIR) spectroscopy and machine learning algorithms (M...
High-Throughput Screening Assays Seeds Phenols Oryza Antioxidants Spectroscopy, Fourier Transform Infrared Machine Learning
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Analyzing Medicago spp. seed morphology using GWAS and machine learning.

Scientific reports Jul 30, 2024
Alfalfa is widely recognized as an important forage crop. To understand the morphological characteristics and genetic basis of seed morphology in alfalfa, we screened 318 Medicago spp., including 244 Medicago sativa subsp. sativa (alfalfa) and 23 oth...
Machine Learning Genome, Plant Polymorphism, Single Nucleotide Medicago truncatula Seeds Quantitative Trait Loci Medicago Medicago sativa Phenotype Genome-Wide Association Study
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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 Jul 29, 2024
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...
Nutrients Nutritive Value Hyperspectral Imaging High-Throughput Screening Assays Seeds Triticum Edible Grain Spectroscopy, Near-Infrared Deep Learning
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A novel method combining deep learning with the Kennard-Stone algorithm for training dataset selection for image-based rice seed variety identification.

Journal of the science of food and agriculture Jun 21, 2024
BACKGROUND: Different varieties of rice vary in planting time, stress resistance, and other characteristics. With advances in rice-breeding technology, the number of rice varieties has increased significantly, making variety identification crucial fo...
Neural Networks, Computer Image Processing, Computer-Assisted Seeds Oryza Algorithms Deep Learning
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