AIMC Topic: Zea mays

Clear Filters Showing 111 to 120 of 132 articles

Genome-enabled prediction using probabilistic neural network classifiers.

BMC genomics Mar 9, 2016
BACKGROUND: Multi-layer perceptron (MLP) and radial basis function neural networks (RBFNN) have been shown to be effective in genome-enabled prediction. Here, we evaluated and compared the classification performance of an MLP classifier versus that o...
Polymorphism, Single Nucleotide Models, Genetic Area Under Curve ROC Curve Genomics Gene-Environment Interaction Neural Networks, Computer Phenotype Zea mays Triticum
View on PubMed DOI

Merge Fuzzy Visual Servoing and GPS-Based Planning to Obtain a Proper Navigation Behavior for a Small Crop-Inspection Robot.

Sensors (Basel, Switzerland) Feb 24, 2016
The concept of precision agriculture, which proposes farming management adapted to crop variability, has emerged in recent years. To effectively implement precision agriculture, data must be gathered from the field in an automated manner at minimal c...
Robotics Zea mays Crops, Agricultural Humans Geographic Information Systems Image Processing, Computer-Assisted Agriculture
View on PubMed DOI

Bioactive films with essential oils and machine learning for controlling Aspergillus niger growth and fumonisin B production in vitro.

International journal of food microbiology Aug 2, 2025
Aspergillus niger is an important species in the fungal community of many foods and is one of the most significant microorganisms used in biotechnology. Some A. niger strains are capable of producing fumonisin B (FB) under certain conditions, but lit...
Aspergillus niger Polyvinyls Acrolein Zea mays Fumonisins Origanum Cinnamomum zeylanicum Food Packaging Machine Learning Food Microbiology Food Contamination Oils, Volatile Antifungal Agents
View on PubMed DOI

MS2MP: A Deep Learning Framework for Metabolic Pathway Prediction from MS/MS-Based Untargeted Metabolomics.

Analytical chemistry Jul 15, 2025
MS/MS-based untargeted metabolomics generates complex data, but pathway enrichment analysis is constrained by the low annotation rates of metabolic features. Here, we propose MS2MP, a novel deep learning-based framework for KEGG pathway prediction di...
Metabolic Networks and Pathways Zea mays Tandem Mass Spectrometry Deep Learning Metabolomics
View on PubMed DOI

Rapid detection of the viability of naturally aged maize seeds using multimodal data fusion and explainable deep learning techniques.

Food chemistry Jun 30, 2025
Seed viability, a key indicator for quality assessment, directly impacts the emergence of field seedlings. The existing nondestructive testing model for maize seed vitality based on naturally aged seeds and predominantly relying on single-modal data ...
Seeds Zea mays Deep Learning
View on PubMed DOI

Hyperspectral imaging combined with DBO-SVM for the germination prediction of thermally damaged seeds.

Analytical methods : advancing methods and applications May 29, 2025
Healthy development of the maize seed industry plays a key role in the effective supply of agricultural products and ensures national food security. Thermal damage to seeds significantly affects crop yield, seed vitality and nutritional value, making...
Hyperspectral Imaging Hot Temperature Algorithms Support Vector Machine Germination Zea mays Seeds
View on PubMed DOI

Corn Seed Freezing Damage Identification of Different Sides Based on Hyperspectral Imaging and SPA-2DCOS Fusion Algorithm.

Molecules (Basel, Switzerland) May 15, 2025
In order to improve the utilization efficiency of corn seeds and meet the demand of single-seed seeding technology in agriculture, this study was conducted to explore the effect of freezing damage detection on the endosperm and embryo sides of single...
Hyperspectral Imaging Freezing Endosperm Seeds Zea mays Image Processing, Computer-Assisted Support Vector Machine Algorithms
View on PubMed DOI

Model-to-crop conserved NUE Regulons enhance machine learning predictions of nitrogen use efficiency.

The Plant cell May 9, 2025
Systems biology aims to uncover gene regulatory networks (GRNs) for agricultural traits, but validating them in crops is challenging. We addressed this challenge by learning and validating model-to-crop transcription factor (TF) regulons governing ni...
Gene Regulatory Networks Transcription Factors Zea mays Machine Learning Arabidopsis Plant Proteins Regulon Gene Expression Regulation, Plant Nitrogen Crops, Agricultural
View on PubMed DOI

Improvement in genomic prediction of maize with prior gene ontology information depends on traits and environmental conditions.

The plant genome Mar 1, 2025
Classical genomic prediction approaches rely on statistical associations between traits and markers rather than their biological significance. Biologically informed selection of genomic regions can help prioritize polymorphisms by considering underly...
Genomics Models, Genetic Phenotype Gene Ontology Zea mays Genetic Markers Environment Genome, Plant
View on PubMed DOI

Tasselyzer, a machine learning method to quantify maize anther exertion, based on PlantCV.

The Plant journal : for cell and molecular biology Feb 1, 2025
Maize anthers emerge from male-only florets, a process that involves complex genetic programming and is affected by environmental factors. Quantifying anther exertion provides a key indicator of male fertility; however, traditional manual scoring met...
Plant Breeding Zea mays Flowers Machine Learning Genotype
View on PubMed DOI
Popular Topics
Recent Journals