AIMC Topic: Glycine max

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Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction.

BMC plant biology
Recent growth in crop genomic and trait data have opened opportunities for the application of novel approaches to accelerate crop improvement. Machine learning and deep learning are at the forefront of prediction-based data analysis. However, few app...

Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease.

Nature communications
Medicines and agricultural biocides are often discovered using large phenotypic screens across hundreds of compounds, where visible effects of whole organisms are compared to gauge efficacy and possible modes of action. However, such analysis is ofte...

Physical and chemical properties of edamame during bean development and application of spectroscopy-based machine learning methods to predict optimal harvest time.

Food chemistry
This study aims to investigate the changes in physical and chemical properties of edamame during bean development and apply a spectroscopy-based machine learning (ML) technique to determine optimal harvest time. The edamame harvested at R5 (beginning...

Crop yield prediction integrating genotype and weather variables using deep learning.

PloS one
Accurate prediction of crop yield supported by scientific and domain-relevant insights, is useful to improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop producti...

sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks.

Plant molecular biology
We proposed an ensemble convolutional neural network model to identify sgRNA high on-target activity in four crops and we used one-hot encoding and k-mers for sequence encoding. As an important component of the CRISPR/Cas9 system, single-guide RNA (s...

Nondestructive Classification of Soybean Seed Varieties by Hyperspectral Imaging and Ensemble Machine Learning Algorithms.

Sensors (Basel, Switzerland)
During the processing and planting of soybeans, it is greatly significant that a reliable, rapid, and accurate technique is used to detect soybean varieties. Traditional chemical analysis methods of soybean variety sampling (e.g., mass spectrometry a...

Interactive machine learning for soybean seed and seedling quality classification.

Scientific reports
New computer vision solutions combined with artificial intelligence algorithms can help recognize patterns in biological images, reducing subjectivity and optimizing the analysis process. The aim of this study was to propose an approach based on inte...

Assessing crop damage from dicamba on non-dicamba-tolerant soybean by hyperspectral imaging through machine learning.

Pest management science
BACKGROUND: Dicamba effectively controls several broadleaf weeds. The off-target drift of dicamba spray or vapor drift can cause severe injury to susceptible crops, including non-dicamba-tolerant crops. In a field experiment, advanced hyperspectral i...

Investigation of a rapid infrared heating assisted mineralization of soybean matrices for trace element analysis.

Food chemistry
A fast sample preparation procedure based on use of infrared (IR) assisted heating for mineralization of soybean derived samples has been developed for their subsequent multielement analysis by inductively coupled plasma optical emission spectrometry...

Soybean inoculants in Brazil: an overview of quality control.

Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology]
The bacterial strains SEMIA 587 and 5019 (Bradyrhizobium elkanii), 5079 (Bradyrhizobium japonicum), and 5080 (Bradyrhizobium diazoefficiens) are recommended for soybean inoculants in Brazil. In several countries, the current regulations are insuffici...