AIMC Topic: Triticum

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Winter wheat yield prediction using convolutional neural networks from environmental and phenological data.

Scientific reports Feb 25, 2022
Crop yield forecasting depends on many interactive factors, including crop genotype, weather, soil, and management practices. This study analyzes the performance of machine learning and deep learning methods for winter wheat yield prediction using an...
Soil Triticum Seasons Neural Networks, Computer Machine Learning
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Accurate Wheat Lodging Extraction from Multi-Channel UAV Images Using a Lightweight Network Model.

Sensors (Basel, Switzerland) Oct 14, 2021
The extraction of wheat lodging is of great significance to post-disaster agricultural production management, disaster assessment and insurance subsidies. At present, the recognition of lodging wheat in the actual complex field environment still has ...
Agriculture Triticum Neural Networks, Computer
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A Deep-Learning-Based Approach for Wheat Yellow Rust Disease Recognition from Unmanned Aerial Vehicle Images.

Sensors (Basel, Switzerland) Sep 30, 2021
Yellow rust is a disease with a wide range that causes great damage to wheat. The traditional method of manually identifying wheat yellow rust is very inefficient. To improve this situation, this study proposed a deep-learning-based method for identi...
Deep Learning Support Vector Machine Basidiomycota Triticum Plant Diseases
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Multitrait machine- and deep-learning models for genomic selection using spectral information in a wheat breeding program.

The plant genome Sep 5, 2021
Prediction of breeding values is central to plant breeding and has been revolutionized by the adoption of genomic selection (GS). Use of machine- and deep-learning algorithms applied to complex traits in plants can improve prediction accuracies. Beca...
Plant Breeding Deep Learning Artificial Intelligence Genomics Triticum Bayes Theorem Genome, Plant
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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...
Neural Networks, Computer Machine Learning Ear Learning Triticum
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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 HCT116 Cells HEK293 Cells Sorghum Gene Editing HeLa Cells Crops, Agricultural Triticum Internet Neural Networks, Computer Humans Algorithms Zea mays Computational Biology CRISPR-Cas Systems
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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...
Satellite Imagery Pakistan Agriculture Vegetables Humans Triticum Data Accuracy Nicotiana Deep Learning
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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 Transpiration Zea mays Plant Roots Triticum Solanum lycopersicum Biological Transport
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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 Remote Sensing Technology Machine Learning Triticum Algorithms 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...
Support Vector Machine Triticum Algorithms Fusarium Plant Diseases
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