AIMC Topic: Sorghum
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Using visible and NIR hyperspectral imaging and machine learning for nondestructive detection of nutrient contents in sorghum.
Scientific reports
Feb 19, 2025
Nondestructive, rapid, and accurate detection of nutritional compositions in sorghum is crucial for agricultural and food industries. In our study, the crude protein, tannin, and crude fat contents of sorghum variety samples were taken as the researc...
Sorghum yield prediction based on remote sensing and machine learning in conflict affected South Sudan.
Scientific reports
Feb 6, 2025
Sorghum cultivation plays a pivotal role in addressing food insecurity in South Sudan, but persistent conflict continues to impose challenges in the agriculture sector therefore understanding the impact of conflict on sorghum yield prediction is impo...
Deep learning the cis-regulatory code for gene expression in selected model plants.
Nature communications
Apr 25, 2024
Elucidating the relationship between non-coding regulatory element sequences and gene expression is crucial for understanding gene regulation and genetic variation. We explored this link with the training of interpretable deep learning models predict...
Detecting common coccinellids found in sorghum using deep learning models.
Scientific reports
Jun 16, 2023
Increased global production of sorghum has the potential to meet many of the demands of a growing human population. Developing automation technologies for field scouting is crucial for long-term and low-cost production. Since 2013, sugarcane aphid (S...
Machine learning models based on remote and proximal sensing as potential methods for in-season biomass yields prediction in commercial sorghum fields.
PloS one
Mar 25, 2021
Crop yield monitoring demonstrated the potential to improve agricultural productivity through improved crop breeding, farm management and commodity planning. Remote and proximal sensing offer the possibility to cut crop monitoring costs traditionally...
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...
Sorghum Fermented by NK Enhances Inhibition of Vascular Inflammation in TNF-α-stimulated Human Aortic Smooth Muscle Cells.
International journal for vitamin and nutrition research. Internationale Zeitschrift fur Vitamin- und Ernahrungsforschung. Journal international de vitaminologie et de nutrition
Jun 25, 2019
Sorghum bicolor L. Moench is widely grown all over the world for food and feed. The effects of sorghum extracts on general inflammation have been previously studied, but its anti-vascular inflammatory effects are unknown. Therefore, this study inve...
Evaluation of a miniaturized NIR spectrometer for cultivar identification: The case of barley, chickpea and sorghum in Ethiopia.
PloS one
Mar 21, 2018
Crop cultivar identification is fundamental for agricultural research, industry and policies. This paper investigates the feasibility of using visible/near infrared hyperspectral data collected with a miniaturized NIR spectrometer to identify cultiva...
Jobelyn attenuates inflammatory responses and neurobehavioural deficits associated with complete Freund-adjuvant-induced arthritis in mice.
Biomedicine & pharmacotherapy = Biomedecine & pharmacotherapie
Dec 27, 2017
Rheumatoid arthritis (RA) is a chronic inflammatory disease that affects the physical and psychosocial wellbeing of the patients and a major cause of work disability. Current drugs for its treatment only provide palliative effect, as cure for the dis...
Combination of the Manifold Dimensionality Reduction Methods with Least Squares Support vector machines for Classifying the Species of Sorghum Seeds.
Scientific reports
Jan 28, 2016
This study was carried out for rapid and noninvasive determination of the class of sorghum species by using the manifold dimensionality reduction (MDR) method and the nonlinear regression method of least squares support vector machines (LS-SVM) combi...
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