AIMC Topic: Glycine max

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Evaluating crop yield prediction models in illinois using aquacrop, semi-physical model and artificial neural networks.

Scientific reports Jul 28, 2025
Crop yield is important for agricultural productivity and the country's economy. While crop yield estimation is an essential aspect of modern agriculture, it continues to be one of the most challenging tasks to manage effectively. Corn and soybean ar...
Glycine max Illinois Neural Networks, Computer Agriculture Models, Theoretical Zea mays Crops, Agricultural
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Grain protein function prediction based on improved FCN and bidirectional LSTM.

Food chemistry Apr 4, 2025
With the development of high-throughput sequencing technologies, predicting grain protein function from amino acid sequences based on intelligent model has become one of the significant tasks in bioinformatics. The soybean, maize, indica, and japonic...
Neural Networks, Computer Computational Biology Zea mays Plant Proteins Glycine max Grain Proteins Amino Acids Edible Grain Amino Acid Sequence
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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...
Humans Male Peptides Molecular Docking Simulation Female Young Adult Adult Soy Foods Taste Proteomics Flavoring Agents Amino Acid Sequence Glycine max
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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 ...
Hyperspectral Imaging Glycine max Deep Learning Support Vector Machine Neural Networks, Computer Spectroscopy, Near-Infrared
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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...
Polymorphism, Single Nucleotide Glycine max Genome, Plant Phenotype Genome-Wide Association Study Genomics Machine Learning Algorithms
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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 Environmental Monitoring Metabolic Reprogramming Soil Pollutants Microplastics Herbicides Machine Learning Polyethylene 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 ...
Larva Weather Seasons Glycine max Plants, Genetically Modified Neural Networks, Computer Spodoptera Forecasting Animals
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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...
Environmental Monitoring Glycine max Zea mays South Dakota Climate Change Crops, Agricultural Machine Learning Conservation of Natural Resources Agriculture
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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...
Glycine max Odorants Humans Soy Milk Taste Deep Learning Food Handling
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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...
Glycine max Spectrophotometry Flour Machine Learning Zea mays Macau Spectroscopy, Near-Infrared Powders Food Contamination
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