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Edible Grain

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Predicting the influence of extreme temperatures on grain production in the Middle-Lower Yangtze Plains using a spatially-aware deep learning model.

PeerJ 39434796
Grain crops are vulnerable to anthropogenic climate change and extreme temperature events. Despite this, previous studies have often neglected the impact of the spatio-temporal distribution of extreme temperature events on regional grain outputs. Thi...
China Climate Change Crops, Agricultural Deep Learning Edible Grain
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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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Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik 39044035
Incorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to indirectly model genotype-by-environment interactions. Complementing phenotypic traits and molecular markers with hi...
Edible Grain Environment Gene-Environment Interaction Genomics Genotype Machine Learning Models, Genetic Phenotype Plant Breeding Zea mays
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Sága, a Deep Learning Spectral Analysis Tool for Fungal Detection in Grains-A Case Study to Detect Fusarium in Winter Wheat.

Toxins 39195764
Fusarium head blight (FHB) is a plant disease caused by various species of the fungus. One of the major concerns associated with spp. is their ability to produce mycotoxins. Mycotoxin contamination in small grain cereals is a risk to human and anim...
Deep Learning Edible Grain Food Contamination Fungicides, Industrial Fusarium Mycotoxins Plant Diseases Triticum
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Contribution assessment and accumulation prediction of heavy metals in wheat grain in a smelting-affected area using machine learning methods.

The Science of the total environment 39137845
Due to the diverse controlling factors and their uneven spatial distribution, especially atmospheric deposition from smelters, assessing and predicting the accumulation of heavy metals (HM) in crops across smelting-affected areas becomes challenging....
Edible Grain Environmental Monitoring Machine Learning Metallurgy Metals, Heavy Soil Pollutants Triticum
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Predictive modeling of rice milling degree for three typical Chinese rice varieties using interpretative machine learning methods.

Journal of food science 39218808
Brown rice over-milling causes high economic and nutrient loss. The rice degree of milling (DOM) detection and prediction remain a challenge for moderate processing. In this study, a self-established grain image acquisition platform was built. Degree...
China Color Edible Grain Food Handling Image Processing, Computer-Assisted Machine Learning Oryza
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Machine learning-based identification of critical factors for cadmium accumulation in rice grains.

Environmental geochemistry and health 39607579
The aggregation of Cadmium (Cd) in rice grains is a significant threat to human healthy. The complexity of the soil-rice system, with its numerous influencing parameters, highlights the need to identify the crucial factors responsible for Cd aggregat...
Cadmium Edible Grain Machine Learning Oryza Soil Soil Pollutants
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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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Ensemble learning-assisted quantitative identifying influencing factors of cadmium and arsenic concentration in rice grain based multiplexed data.

Journal of hazardous materials 39675080
Rapid and accurate prediction of rice Cd (rCd) and rice As (rAs) bioaccumulation are important for assessing the safe utilization of rice. Currently, there is lack of comprehensive and systematic exploration of the factors of rCd and rAs. Herein, ens...
Arsenic Cadmium China Edible Grain Ensemble Learning Environmental Monitoring Food Contamination Machine Learning Oryza Soil Soil Pollutants
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Cost-efficient training of hyperspectral deep learning models for the detection of contaminating grains in bulk oats by fluorescent tagging.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy 39923708
Computer vision based on instance segmentation deep learning models offers great potential for automating many visual inspection tasks, such as the detection of contaminating grains in bulk oats, a nutrient rich grain which is well-tolerated by peopl...
Avena Deep Learning Edible Grain Fluorescent Dyes Food Contamination Hyperspectral Imaging
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