AIMC Topic: Brassica napus

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Nondestructive detection of cadmium content in oilseed rape leaves under different silicon environments using deep transfer learning and Vis-NIR hyperspectral imaging.

Food chemistry Mar 8, 2025
In this paper, a transfer stack denoising autoencoder (T-SDAE) algorithm is proposed to implement the migration of cadmium (Cd) prediction depth characteristic model of oilseed rape leaves in different silicon environments. Stacked denoising autoenco...
Spectroscopy, Near-Infrared Deep Learning Algorithms Hyperspectral Imaging Silicon Brassica napus Plant Leaves Cadmium
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Integrated Assays of Genome-Wide Association Study, Multi-Omics Co-Localization, and Machine Learning Associated Calcium Signaling Genes with Oilseed Rape Resistance to .

International journal of molecular sciences Jun 25, 2024
(Ss) is one of the most devastating fungal pathogens, causing huge yield loss in multiple economically important crops including oilseed rape. Plant resistance to Ss pertains to quantitative disease resistance (QDR) controlled by multiple minor gene...
Machine Learning Genome-Wide Association Study Multiomics Plant Diseases Ascomycota Genomics Polymorphism, Single Nucleotide Brassica napus Calcium Signaling Disease Resistance Quantitative Trait Loci
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A deep learning method for predicting lead content in oilseed rape leaves using fluorescence hyperspectral imaging.

Food chemistry Dec 20, 2022
The purpose of this study was to develop a deep learning method involving wavelet transform (WT) and stacked denoising autoencoder (SDAE) for extracting deep features of heavy metal lead (Pb) detection of oilseed rape leaves. Firstly, the standard no...
Hyperspectral Imaging Brassica napus Spectroscopy, Near-Infrared Deep Learning Plant Leaves Least-Squares Analysis Algorithms
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Modeling risk of Sclerotinia sclerotiorum-induced disease development on canola and dry bean using machine learning algorithms.

Scientific reports Jan 17, 2022
Diseases caused by the fungus Sclerotinia sclerotiorum are managed mainly through fungicide applications in canola and dry bean. Accurate estimation of the risk of disease development on these crops could help farmers make spraying decisions. Five ma...
Agriculture Brassica napus Plant Diseases Logistic Models Temperature Ascomycota Fabaceae Neural Networks, Computer Fungicides, Industrial Risk Assessment Crops, Agricultural Machine Learning
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A Comparison between Several Response Surface Methodology Designs and a Neural Network Model to Optimise the Oxidation Conditions of a Lignocellulosic Blend.

Biomolecules May 19, 2020
In this paper, response surface methodology (RSM) designs and an artificial neural network (ANN) are used to obtain the optimal conditions for the oxy-combustion of a corn-rape blend. The ignition temperature () and burnout index () were selected as ...
Zea mays Neural Networks, Computer Carbon Dioxide Lignin Brassica napus Temperature Oxidation-Reduction Biomass Oxygen
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Using artificial neural network to investigate physiological changes and cerium oxide nanoparticles and cadmium uptake by Brassica napus plants.

Environmental pollution (Barking, Essex : 1987) Dec 12, 2018
Heavy metals and emerging engineered nanoparticles (ENPs) are two current environmental concerns that have attracted considerable attention. Cerium oxide nanoparticles (CeONPs) are now used in a plethora of industrial products, while cadmium (Cd) is ...
Plant Roots Soil Pollutants Soil Brassica napus Humans Nanoparticles Photosynthesis Neural Networks, Computer Cadmium Cerium
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Characterizing root response phenotypes by neural network analysis.

Journal of experimental botany May 26, 2015
Roots play an immediate role as the interface for water acquisition. To improve sustainability in low-water environments, breeders of major crops must therefore pay closer attention to advantageous root phenotypes; however, the complexity of root arc...
Plant Roots Genotyping Techniques Stress, Physiological Neural Networks, Computer Brassica napus Phenotype
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Lightweight deep learning model for embedded systems efficiently predicts oil and protein content in rapeseed.

Food chemistry Jun 15, 2025
Conventional methods for determining protein and oil content in rapeseed are often time-consuming, labor-intensive, and costly. In this study, a mobile application was developed using an optimized deep learning method for low-cost, non-destructive an...
Brassica rapa Deep Learning Plant Oils Brassica napus Plant Proteins Rapeseed Oil
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