AIMC Topic: Edible Grain

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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 Edible Grain Soil Pollutants Rhizosphere
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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...
Neural Networks, Computer Water Zea mays Edible Grain Image Processing, Computer-Assisted Seeds
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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...
Algorithms Edible Grain Principal Component Analysis Starch Alcohols Least-Squares Analysis Fermented Foods Spectroscopy, Near-Infrared Hyperspectral Imaging Fermentation Support Vector Machine
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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 ...
Recognition, Psychology Aphids Animals Edible Grain Mental Recall Deep Learning
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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...
Aphids Sorghum Coleoptera Insecticides Crops, Agricultural Humans Animals Edible Grain Saccharum Deep Learning
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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...
Artificial Intelligence Nitrogen Deep Learning Unmanned Aerial Devices Zea mays Edible Grain
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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 ...
Hyperspectral Imaging Food Quality Spectrophotometry, Infrared Artificial Intelligence Edible Grain Humans
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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 Image Processing, Computer-Assisted Machine Learning Edible Grain Cloud Computing
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Towards Automated Analysis of Grain Spikes in Greenhouse Images Using Neural Network Approaches: A Comparative Investigation of Six Methods.

Sensors (Basel, Switzerland) Nov 9, 2021
Automated analysis of small and optically variable plant organs, such as grain spikes, is highly demanded in quantitative plant science and breeding. Previous works primarily focused on the detection of prominently visible spikes emerging on the top ...
Plant Leaves Edible Grain Neural Networks, Computer Image Processing, Computer-Assisted Plant Breeding
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Reproductive characteristics of the giant gurami sago strain ( Lacepède, 1801): basic knowledge for a future hatchery development strategy.

F1000Research Sep 15, 2021
The giant gourami sago strain ( Lacepède) has been approved in 2018 as a candidate for freshwater aquaculture in Indonesia. However, information on the species' reproduction is minimal. This study analyzed the reproductive characteristics of the go...
Spermatozoa Male Fishes Sperm Motility Edible Grain Female Reproduction Aquaculture Animals
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