AIMC Topic: Edible Grain

Clear Filters Showing 11 to 20 of 39 articles

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...
Spectroscopy, Near-Infrared Deep Learning Seeds Nutrients Nutritive Value High-Throughput Screening Assays Triticum Edible Grain Hyperspectral Imaging
View on PubMed DOI

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 Jul 23, 2024
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...
Models, Genetic Plant Breeding Environment Phenotype Zea mays Machine Learning Edible Grain Gene-Environment Interaction Genomics Genotype
View on PubMed DOI

Uptake of zinc from the soil to the wheat grain: Nonlinear process prediction based on artificial neural network and geochemical data.

The Science of the total environment Jul 10, 2024
Trace elements in plants primarily derive from soils, subsequently influencing human health through the food chain. Therefore, it is essential to understand the relationship of trace elements between plants and soils. Since trace elements from soils ...
Soil Trace Elements Zinc Neural Networks, Computer Triticum Environmental Monitoring Soil Pollutants Edible Grain Rhizosphere
View on PubMed DOI

The use of image analysis to study the effect of moisture content on the physical properties of grains.

Scientific reports May 22, 2024
Designing machines and equipment for post-harvest operations of agricultural products requires information about their physical properties. The aim of the work was to evaluate the possibility of introducing a new approach to predict the moisture cont...
Water Image Processing, Computer-Assisted Zea mays Neural Networks, Computer Edible Grain Seeds
View on PubMed DOI

Rapid determination of starch and alcohol contents in fermented grains by hyperspectral imaging combined with data fusion techniques.

Journal of food science May 8, 2024
Starch and alcohol serve as pivotal indicators in assessing the quality of lees fermentation. In this paper, two hyperspectral imaging (HSI) techniques (visible-near-infrared (Vis-NIR) and NIR) were utilized to acquire separate HSI data, which were t...
Hyperspectral Imaging Alcohols Least-Squares Analysis Edible Grain Fermentation Fermented Foods Starch Support Vector Machine Algorithms Principal Component Analysis Spectroscopy, Near-Infrared
View on PubMed DOI

Aphid cluster recognition and detection in the wild using deep learning models.

Scientific reports Aug 17, 2023
Aphid infestation poses a significant threat to crop production, rural communities, and global food security. While chemical pest control is crucial for maximizing yields, applying chemicals across entire fields is both environmentally unsustainable ...
Animals Recognition, Psychology Aphids Edible Grain Deep Learning Mental Recall
View on PubMed DOI

Detecting common coccinellids found in sorghum using deep learning models.

Scientific reports Jun 16, 2023
Increased global production of sorghum has the potential to meet many of the demands of a growing human population. Developing automation technologies for field scouting is crucial for long-term and low-cost production. Since 2013, sugarcane aphid (S...
Sorghum Coleoptera Aphids Saccharum Insecticides Crops, Agricultural Humans Deep Learning Animals Edible Grain
View on PubMed DOI

UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping.

Sensors (Basel, Switzerland) Feb 6, 2023
Recent advances in unmanned aerial vehicles (UAV), mini and mobile sensors, and GeoAI (a blend of geospatial and artificial intelligence (AI) research) are the main highlights among agricultural innovations to improve crop productivity and thus secur...
Unmanned Aerial Devices Artificial Intelligence Edible Grain Deep Learning Nitrogen Zea mays
View on PubMed DOI

Advances in infrared spectroscopy and hyperspectral imaging combined with artificial intelligence for the detection of cereals quality.

Critical reviews in food science and nutrition Apr 20, 2022
Cereals provide humans with essential nutrients, and its quality assessment has attracted widespread attention. Infrared (IR) spectroscopy (IRS) and hyperspectral imaging (HSI), as powerful nondestructive testing technologies, are widely used in the ...
Food Quality Humans Edible Grain Spectrophotometry, Infrared Artificial Intelligence Hyperspectral Imaging
View on PubMed DOI

Cereal grain 3D point cloud analysis method for shape extraction and filled/unfilled grain identification based on structured light imaging.

Scientific reports Feb 24, 2022
Cereals are the main food for mankind. The grain shape extraction and filled/unfilled grain recognition are meaningful for crop breeding and genetic analysis. The conventional measuring method is mainly manual, which is inefficient, labor-intensive a...
Plant Breeding Edible Grain Cloud Computing Machine Learning Image Processing, Computer-Assisted
View on PubMed DOI
Popular Topics
Recent Journals