AIMC Topic: Triticum

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Wheat Ear Recognition Based on RetinaNet and Transfer Learning.

Sensors (Basel, Switzerland) Jul 16, 2021
The number of wheat ears is an essential indicator for wheat production and yield estimation, but accurately obtaining wheat ears requires expensive manual cost and labor time. Meanwhile, the characteristics of wheat ears provide less information, an...
Learning Triticum Ear Neural Networks, Computer Machine 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...
Sorghum CRISPR-Cas Systems Gene Editing Triticum Glycine max HCT116 Cells HeLa Cells RNA, Guide, CRISPR-Cas Systems HEK293 Cells Crops, Agricultural Algorithms Humans Internet Computational Biology Neural Networks, Computer Zea mays
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On the performance of fusion based planet-scope and Sentinel-2 data for crop classification using inception inspired deep convolutional neural network.

PloS one Sep 28, 2020
This research work aims to develop a deep learning-based crop classification framework for remotely sensed time series data. Tobacco is a major revenue generating crop of Khyber Pakhtunkhwa (KP) province of Pakistan, with over 90% of the country's To...
Humans Deep Learning Pakistan Vegetables Nicotiana Triticum Agriculture Data Accuracy Satellite Imagery
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Investigating plant uptake of organic contaminants through transpiration stream concentration factor and neural network models.

The Science of the total environment Aug 24, 2020
Uptake of seven organic contaminants including bisphenol A, estriol, 2,4-dinitrotoluene, N,N-diethyl-meta-toluamide (DEET), carbamazepine, acetaminophen, and lincomycin by tomato (Solanum lycopersicum L.), corn (Zea mays L.), and wheat (Triticum aest...
Neural Networks, Computer Plant Roots Solanum lycopersicum Biological Transport Plant Transpiration Zea mays Triticum
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Automatic wheat ear counting using machine learning based on RGB UAV imagery.

The Plant journal : for cell and molecular biology Jun 14, 2020
In wheat (Triticum aestivum L) and other cereals, the number of ears per unit area is one of the main yield-determining components. An automatic evaluation of this parameter may contribute to the advance of wheat phenotyping and monitoring. There is ...
Crop Production Aircraft Automation Machine Learning Algorithms Remote Sensing Technology Triticum Image Processing, Computer-Assisted
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Diagnosis of the Severity of Fusarium Head Blight of Wheat Ears on the Basis of Image and Spectral Feature Fusion.

Sensors (Basel, Switzerland) May 20, 2020
head blight (FHB), one of the most prevalent and damaging infection diseases of wheat, affects quality and safety of associated food. In this study, to realize the early accurate monitoring of FHB, a diagnostic model of disease severity was proposed...
Triticum Fusarium Plant Diseases Algorithms Support Vector Machine
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Detection of sunn pest-damaged wheat grains using artificial bee colony optimization-based artificial intelligence techniques.

Journal of the science of food and agriculture Nov 21, 2019
BACKGROUND: In this study, artificial intelligence models that identify sunn pest-damaged wheat grains (SDG) and healthy wheat grains (HWG) are presented. Svevo durum wheat cultivated in Konya province, Turkey is used for the process, with 150 HWG an...
Artificial Intelligence Machine Learning Image Processing, Computer-Assisted Seeds Algorithms Neural Networks, Computer Triticum Turkey
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Quantitative analysis of wheat maltose by combined terahertz spectroscopy and imaging based on Boosting ensemble learning.

Food chemistry Sep 30, 2019
To improve the prediction accuracy of existing data modeling that is based on either spectral data or image data alone, we herein propose a method for the quantitative analysis of wheat maltose contents based on the fusion of terahertz spectroscopy a...
Maltose Terahertz Spectroscopy Support Vector Machine Triticum
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Recovering Wind-Induced Plant Motion in Dense Field Environments via Deep Learning and Multiple Object Tracking.

Plant physiology Jul 22, 2019
Understanding the relationships between local environmental conditions and plant structure and function is critical for both fundamental science and for improving the performance of crops in field settings. Wind-induced plant motion is important in m...
Triticum Motion Breeding Crops, Agricultural Wind Algorithms Phenotype Environment Deep Learning Agriculture
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PolyCRACKER, a robust method for the unsupervised partitioning of polyploid subgenomes by signatures of repetitive DNA evolution.

BMC genomics Jul 12, 2019
BACKGROUND: Our understanding of polyploid genomes is limited by our inability to definitively assign sequences to a specific subgenome without extensive prior knowledge like high resolution genetic maps or genome sequences of diploid progenitors. In...
Evolution, Molecular Unsupervised Machine Learning Triticum Phylogeny DNA, Plant Nicotiana Genomics Polyploidy Genome, Plant Repetitive Sequences, Nucleic Acid
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