AIMC Topic: Zea mays

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Hyperparameter Optimization for Transfer Learning of VGG16 for Disease Identification in Corn Leaves Using Bayesian Optimization.

Big data Dec 10, 2021
One of the world's most widely grown crops is corn. Crop loss due to diseases has a major economic effect, putting the food supply in jeopardy. In many parts of the world, lack of infrastructure still slows disease diagnosis. In this context, effecti...
Plant Leaves Bayes Theorem Zea mays Neural Networks, Computer Machine Learning
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Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.

Nature communications Sep 24, 2021
Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we add...
Zea mays Nitrogen Genetic Variation Models, Genetic Evolution, Molecular Gene Expression Regulation, Plant Arabidopsis Species Specificity Genome, Plant Machine Learning Phenotype Genomics Genotype Transcriptome
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A novel lncRNA-protein interaction prediction method based on deep forest with cascade forest structure.

Scientific reports Sep 23, 2021
Long noncoding RNAs (lncRNAs) regulate many biological processes by interacting with corresponding RNA-binding proteins. The identification of lncRNA-protein Interactions (LPIs) is significantly important to well characterize the biological functions...
Humans Datasets as Topic Arabidopsis RNA-Binding Proteins RNA, Long Noncoding Machine Learning Zea mays Computational Biology
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LightGBM: accelerated genomically designed crop breeding through ensemble learning.

Genome biology Sep 20, 2021
LightGBM is an ensemble model of decision trees for classification and regression prediction. We demonstrate its utility in genomic selection-assisted breeding with a large dataset of inbred and hybrid maize lines. LightGBM exhibits superior performa...
Decision Trees Zea mays Crops, Agricultural Genomics Polymorphism, Single Nucleotide Plant Breeding Software Genome-Wide Association Study Machine Learning
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Predicting phenotypes from genetic, environment, management, and historical data using CNNs.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik Aug 27, 2021
Convolutional Neural Networks (CNNs) can perform similarly or better than standard genomic prediction methods when sufficient genetic, environmental, and management data are provided. Predicting phenotypes from genetic (G), environmental (E), and man...
Zea mays Data Mining Neural Networks, Computer Crops, Agricultural Machine Learning Phenotype Genomics
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Robotic Assay for Drought (RoAD): an automated phenotyping system for brassinosteroid and drought responses.

The Plant journal : for cell and molecular biology Aug 10, 2021
Brassinosteroids (BRs) are a group of plant steroid hormones involved in regulating growth, development, and stress responses. Many components of the BR pathway have previously been identified and characterized. However, BR phenotyping experiments ar...
Arabidopsis Proteins Arabidopsis Robotics Brassinosteroids Triazoles Seedlings Droughts Image Processing, Computer-Assisted Equipment Design Machine Learning Protein Kinases Phenotype Zea mays Soil
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Predicting moisture content during maize nixtamalization using machine learning with NIR spectroscopy.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik Aug 3, 2021
Moisture content during nixtamalization can be accurately predicted from NIR spectroscopy when coupled with a support vector machine (SVM) model, is strongly modulated by the environment, and has a complex genetic architecture. Lack of high-throughpu...
Machine Learning Spectroscopy, Near-Infrared Genotype Genetic Association Studies Water Zea mays Cooking
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3D Reconstruction of Non-Rigid Plants and Sensor Data Fusion for Agriculture Phenotyping.

Sensors (Basel, Switzerland) Jun 15, 2021
Technology has been promoting a great transformation in farming. The introduction of robotics; the use of sensors in the field; and the advances in computer vision; allow new systems to be developed to assist processes, such as phenotyping, of crop's...
Farms Imaging, Three-Dimensional Agriculture Zea mays Robotics
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A spectral characteristic analysis method for distinguishing heavy metal pollution in crops: VMD-PCA-SVM.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Mar 9, 2021
Exploring the characteristics and types of heavy metal pollution in crops has important implications for food security and human health. In this study, a method for distinguishing heavy metal-polluted elements in corn leaves was proposed. Based on th...
Metals, Heavy Zea mays Environmental Pollution Humans Environmental Monitoring Support Vector Machine China Principal Component Analysis
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Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt.

Scientific reports Jan 15, 2021
This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions...
Machine Learning Zea mays Climate United States Crop Production
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