AIMC Topic: Zea mays

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Weed and Corn Seedling Detection in Field Based on Multi Feature Fusion and Support Vector Machine.

Sensors (Basel, Switzerland) Dec 31, 2020
Detection of weeds and crops is the key step for precision spraying using the spraying herbicide robot and precise fertilization for the agriculture machine in the field. On the basis of k-mean clustering image segmentation using color information an...
Zea mays Crops, Agricultural Support Vector Machine Seedlings Plant Weeds
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Predicting functions of maize proteins using graph convolutional network.

BMC bioinformatics Dec 16, 2020
BACKGROUND: Maize (Zea mays ssp. mays L.) is the most widely grown and yield crop in the world, as well as an important model organism for fundamental research of the function of genes. The functions of Maize proteins are annotated using the Gene Ont...
Humans Neural Networks, Computer Plant Proteins Molecular Sequence Annotation Gene Ontology Area Under Curve Zea mays
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Whole-Field Reinforcement Learning: A Fully Autonomous Aerial Scouting Method for Precision Agriculture.

Sensors (Basel, Switzerland) Nov 18, 2020
Unmanned aerial systems (UAS) are increasingly used in precision agriculture to collect crop health related data. UAS can capture data more often and more cost-effectively than sending human scouts into the field. However, in large crop fields, fligh...
Aircraft Agriculture Zea mays Neural Networks, Computer Machine Learning
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An embedded gene selection method using knockoffs optimizing neural network.

BMC bioinformatics Sep 22, 2020
BACKGROUND: Gene selection refers to find a small subset of discriminant genes from the gene expression profiles. How to select genes that affect specific phenotypic traits effectively is an important research work in the field of biology. The neural...
Data Mining Gene Expression Regulation Breast Neoplasms Transcriptome Computational Biology Zea mays Neural Networks, Computer Female Humans
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Scaling Effects on Chlorophyll Content Estimations with RGB Camera Mounted on a UAV Platform Using Machine-Learning Methods.

Sensors (Basel, Switzerland) Sep 9, 2020
Timely monitoring and precise estimation of the leaf chlorophyll contents of maize are crucial for agricultural practices. The scale effects are very important as the calculated vegetation index (VI) were crucial for the quantitative remote sensing. ...
Machine Learning Agriculture Zea mays Chlorophyll Plant Leaves Remote Sensing Technology
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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...
Plant Transpiration Plant Roots Zea mays Solanum lycopersicum Biological Transport Neural Networks, Computer Triticum
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Precise Estimation of NDVI with a Simple NIR Sensitive RGB Camera and Machine Learning Methods for Corn Plants.

Sensors (Basel, Switzerland) Jun 5, 2020
The normalized difference vegetation index (NDVI) is widely used in remote sensing to monitor plant growth and chlorophyll levels. Usually, a multispectral camera (MSC) or hyperspectral camera (HSC) is required to obtain the near-infrared (NIR) and r...
Machine Learning Algorithms Chlorophyll Plant Leaves Zea mays
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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 ...
Oxygen Zea mays Biomass Oxidation-Reduction Carbon Dioxide Lignin Neural Networks, Computer Brassica napus Temperature
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Multi-feature fusion for deep learning to predict plant lncRNA-protein interaction.

Genomics May 11, 2020
Long non-coding RNAs (lncRNAs) play key roles in regulating cellular biological processes through diverse molecular mechanisms including binding to RNA binding proteins. The majority of plant lncRNAs are functionally uncharacterized, thus, accurate p...
Arabidopsis RNA, Plant Zea mays Plant Proteins RNA-Binding Proteins Deep Learning RNA, Long Noncoding
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Machine Learning Enables High-Throughput Phenotyping for Analyses of the Genetic Architecture of Bulliform Cell Patterning in Maize.

G3 (Bethesda, Md.) Dec 3, 2019
Bulliform cells comprise specialized cell types that develop on the adaxial (upper) surface of grass leaves, and are patterned to form linear rows along the proximodistal axis of the adult leaf blade. Bulliform cell patterning affects leaf angle and ...
Gene Expression Regulation, Plant Plant Leaves Quantitative Trait, Heritable Machine Learning Zea mays Genome-Wide Association Study
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