AIMC Topic: Glycine max

Clear Filters Showing 1 to 10 of 43 articles

An interpretable machine learning approach based on SHAP, Sobol and LIME values for precise estimation of daily soybean crop coefficients.

Scientific reports
Increasing water scarcity and climate variability have intensified the need for precise agricultural irrigation management. Accurate estimation of crop coefficients (Kc) is critical for determining crop water requirements, especially in arid and semi...

Deciphering the sequence basis and application of transcriptional initiation regulation in plant genomes through deep learning.

Genome biology
BACKGROUND: Transcription initiation is a key checkpoint in plant gene regulation, yet the DNA features that determine where and the frequency of the genes start transcription remain unclear.

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

Scientific reports
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...

Synergistic multi-level fusion framework of VNIR and SWIR hyperspectral data for soybean fungal contamination detection.

Food chemistry
Current methods for detecting soybean fungal contamination are often destructive, time-consuming, and labor-intensive. This study proposed an efficient approach by fusing visible and near-infrared (VNIR) and short-wave infrared (SWIR) hyperspectral i...

Predicting the composition of multiple soybean varieties from whole and ground seeds using Fourier transform near-infrared spectroscopy (FT-NIRS) and machine learning.

Food chemistry
Soybean is being increasingly included in human diets, highlighting the importance of determining its composition. Although Fourier-Transform Near-Infrared Spectroscopy (FT-NIRS) has become a promising technology, currently used models remain limited...

Grain protein function prediction based on improved FCN and bidirectional LSTM.

Food chemistry
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...

Identification and taste presentation characteristics of umami peptides from soybean paste based on peptidomics and virtual screening.

Food chemistry
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...

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.)
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 ...

Exploring genomic feature selection: A comparative analysis of GWAS and machine learning algorithms in a large-scale soybean dataset.

The plant genome
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...

Integrating automated machine learning and metabolic reprogramming for the identification of microplastic in soil: A case study on soybean.

Journal of hazardous materials
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...