AIMC Topic: Glycine max

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On construction of data preprocessing for real-life SoyLeaf dataset & disease identification using Deep Learning Models.

Computational biology and chemistry Mar 8, 2025
The vast volumes of data are needed to train Deep Learning Models from scratch to identify illnesses in soybean leaves. However, there is still a lack of sufficient high-quality samples. To overcome this problem, we have developed the real-life SoyLe...
Plant Diseases Glycine max Deep Learning Plant Leaves
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Identification and taste presentation characteristics of umami peptides from soybean paste based on peptidomics and virtual screening.

Food chemistry Feb 25, 2025
This research concentrated on soybean paste fermented with Tetragenococcus halophilus, employing peptidomics and machine learning methodologies to screen for novel umami peptides. Taste characteristics of umami peptides were evaluated through sensory...
Peptides Flavoring Agents Female Molecular Docking Simulation Glycine max Amino Acid Sequence Humans Soy Foods Taste Young Adult Proteomics Adult Male
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Discrimination of unsound soybeans using hyperspectral imaging: A deep learning method based on dual-channel feature fusion strategy and attention mechanism.

Food research international (Ottawa, Ont.) Jan 22, 2025
The application of high-level data fusion in the detection of agricultural products still presents a significant challenge. In this study, dual-channel feature fusion model (DCFFM) with attention mechanism was proposed to optimize the utilization of ...
Neural Networks, Computer Spectroscopy, Near-Infrared Deep Learning Glycine max Support Vector Machine Hyperspectral Imaging
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Exploring genomic feature selection: A comparative analysis of GWAS and machine learning algorithms in a large-scale soybean dataset.

The plant genome Sep 10, 2024
The surge in high-throughput technologies has empowered the acquisition of vast genomic datasets, prompting the search for genetic markers and biomarkers relevant to complex traits. However, grappling with the inherent complexities of high dimensiona...
Genome-Wide Association Study Genomics Polymorphism, Single Nucleotide Algorithms Phenotype Glycine max Genome, Plant Machine Learning
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Integrating automated machine learning and metabolic reprogramming for the identification of microplastic in soil: A case study on soybean.

Journal of hazardous materials Aug 22, 2024
The accumulation of polyethylene microplastic (PE-MPs) in soil can significantly impact plant quality and yield, as well as affect human health and food chain cycles. Therefore, developing rapid and effective detection methods is crucial. In this stu...
Soil Pollutants Soil Microplastics Machine Learning Environmental Monitoring Herbicides Polyethylene Metabolic Reprogramming Glycine max
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Artificial neural networks as a tool for seasonal forecast of attack intensity of Spodoptera spp. in Bt soybean.

International journal of biometeorology Aug 13, 2024
Soybean (Glycine max) is the world's most cultivated legume; currently, most of its varieties are Bt. Spodoptera spp. (Lepidoptera: Noctuidae) are important pests of soybean. An artificial neural network (ANN) is an artificial intelligence tool that ...
Seasons Glycine max Spodoptera Plants, Genetically Modified Animals Larva Weather Neural Networks, Computer Forecasting
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Monitoring the Spatial Distribution of Cover Crops and Tillage Practices Using Machine Learning and Environmental Drivers across Eastern South Dakota.

Environmental management Jul 29, 2024
The adoption of conservation agriculture methods, such as conservation tillage and cover cropping, is a viable alternative to conventional farming practices for improving soil health and reducing soil carbon losses. Despite their significance in miti...
Zea mays Climate Change Conservation of Natural Resources Glycine max Crops, Agricultural Machine Learning South Dakota Agriculture Environmental Monitoring
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A deep learning-based quantitative prediction model for the processing potentials of soybeans as soymilk raw materials.

Food chemistry May 14, 2024
Current technologies as correlation analysis, regression analysis and classification model, exhibited various limitations in the evaluation of soybean possessing potentials, including single, vague evaluation and failure of quantitative prediction, a...
Soy Milk Food Handling Glycine max Humans Odorants Deep Learning Taste
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Non-invasive prediction of maca powder adulteration using a pocket-sized spectrophotometer and machine learning techniques.

Scientific reports May 7, 2024
Discriminating different cultivars of maca powder (MP) and detecting their authenticity after adulteration with potent adulterants such as maize and soy flour is a challenge that has not been studied with non-invasive techniques such as near infrared...
Flour Macau Machine Learning Spectroscopy, Near-Infrared Powders Food Contamination Glycine max Zea mays Spectrophotometry
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Optimizing soybean biofuel blends for sustainable urban medium-duty commercial vehicles in India: an AI-driven approach.

Environmental science and pollution research international Apr 23, 2024
This article presents the outcomes of a research study focused on optimizing the performance of soybean biofuel blends derived from soybean seeds specifically for urban medium-duty commercial vehicles. The study took into consideration elements such ...
Gasoline Biofuels Glycine max Artificial Intelligence Vehicle Emissions India
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