AIMC Topic: Zea mays

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

International journal of food microbiology
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...

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

Analytical chemistry
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...

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

Food chemistry
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 ...

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

Analytical methods : advancing methods and applications
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...

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

Molecules (Basel, Switzerland)
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...

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

The Plant cell
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...

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

The plant genome
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...

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

The Plant journal : for cell and molecular biology
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...

Segmentation-based lightweight multi-class classification model for crop disease detection, classification, and severity assessment using DCNN.

PloS one
Leaf diseases in Zea mays crops have a significant impact on both the calibre and volume of maize yield, eventually impacting the market. Prior detection of the intensity of an infection would enable the efficient allocation of treatment resources an...

Identification of biomarker genes from multiple studies for abiotic stress in maize through machine learning.

Journal of biosciences
Abiotic stresses are major limiting factors for maize growth. Therefore, exploration of the mechanisms underlying the response to abiotic stress in maize is of great interest. Toward this end, we performed integration of the feature selection method ...