AIMC Topic: Glycine max

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A comparative study of 11 non-linear regression models highlighting autoencoder, DBN, and SVR, enhanced by SHAP importance analysis in soybean branching prediction.

Scientific reports Mar 11, 2024
To explore a robust tool for advancing digital breeding practices through an artificial intelligence-driven phenotype prediction expert system, we undertook a thorough analysis of 11 non-linear regression models. Our investigation specifically emphas...
Algorithms Glycine max Plant Breeding Artificial Intelligence
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Deep Learning Model for Classifying and Evaluating Soybean Leaf Disease Damage.

International journal of molecular sciences Dec 20, 2023
Soybean ( (L.) Merr.) is a major source of oil and protein for human food and animal feed; however, soybean crops face diverse factors causing damage, including pathogen infections, environmental shifts, poor fertilization, and incorrect pesticide us...
Deep Learning Humans Plant Leaves Animal Feed Animals Pesticides Glycine max
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Geographical traceability of soybean: An electronic nose coupled with an effective deep learning method.

Food chemistry Dec 14, 2023
The quality of soybeans is correlated with their geographical origin. It is a common phenomenon to replace low-quality soybeans from substandard origins with superior ones. This paper proposes the adaptive convolutional kernel channel attention netwo...
Glycine max Geography Algorithms Deep Learning Electronic Nose
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Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning.

Sensors (Basel, Switzerland) Mar 19, 2023
Weeds can cause significant yield losses and will continue to be a problem for agricultural production due to climate change. Dicamba is widely used to control weeds in monocot crops, especially genetically engineered dicamba-tolerant (DT) dicot crop...
Deep Learning Herbicides Glycine max Plant Breeding Plant Weeds Crops, Agricultural Dicamba Unmanned Aerial Devices
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Predicting the quality of soybean seeds stored in different environments and packaging using machine learning.

Scientific reports May 25, 2022
The monitoring and evaluating the physical and physiological quality of seeds throughout storage requires technical and financial resources and is subject to sampling and laboratory errors. Therefore, machine learning (ML) techniques could help optim...
Glycine max Algorithms Seeds Humans Artificial Intelligence Machine Learning
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Machine learning models outperform deep learning models, provide interpretation and facilitate feature selection for soybean trait prediction.

BMC plant biology Apr 8, 2022
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...
Glycine max Deep Learning Genotype Machine Learning Phenotype
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Physics-informed deep learning characterizes morphodynamics of Asian soybean rust disease.

Nature communications Nov 5, 2021
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...
Gene Expression Profiling Deep Learning Glycine max Plant Diseases Humans Phakopsora pachyrhizi
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Physical and chemical properties of edamame during bean development and application of spectroscopy-based machine learning methods to predict optimal harvest time.

Food chemistry Aug 8, 2021
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...
Machine Learning Seeds Glycine max Spectrum Analysis Sugars
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Crop yield prediction integrating genotype and weather variables using deep learning.

PloS one Jun 17, 2021
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...
Crops, Agricultural Glycine max Genotype Plant Breeding Neural Networks, Computer Weather Deep Learning
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sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks.

Plant molecular biology Jan 1, 2021
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...
Glycine max RNA, Guide, CRISPR-Cas Systems Computational Biology Zea mays Sorghum Humans Internet Gene Editing Neural Networks, Computer HeLa Cells HCT116 Cells Algorithms Crops, Agricultural HEK293 Cells CRISPR-Cas Systems Triticum
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