AIMC Topic: Zea mays

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Cropformer: An interpretable deep learning framework for crop genomic prediction.

Plant communications Dec 16, 2024
Machine learning and deep learning are extensively employed in genomic selection (GS) to expedite the identification of superior genotypes and accelerate breeding cycles. However, a significant challenge with current data-driven deep learning models ...
Plant Breeding Triticum Solanum lycopersicum Crops, Agricultural Genome, Plant Oryza Genotype Zea mays Genomics Polymorphism, Single Nucleotide Phenotype Deep Learning
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Predicting the seasonal dynamics of Dalbulus maidis (Hemiptera: Cicadellidae) in corn using artificial neural networks.

Neotropical entomology Dec 5, 2024
This study addresses the challenge of predicting Dalbulus maidis (DeLong & Wolcott) (Hemiptera: Cicadellidae) density in cornfields by developing an artificial neural network (ANN). Over two years, we collected data on meteorological variables (atmos...
Animals Brazil Neural Networks, Computer Seasons Hemiptera Population Density Population Dynamics Zea mays
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A rapid method for assessing seed drought resistance using integrated ID-BOA-SVM.

Analytical methods : advancing methods and applications Dec 5, 2024
This study investigates the application of near-infrared spectroscopy (NIR) for assessing drought resistance in seeds, aiming to offer a rapid and efficient method suitable for large-scale primary screening. NIR spectroscopy is utilized to analyze fo...
Bayes Theorem Zea mays Droughts Support Vector Machine Drought Resistance Spectroscopy, Near-Infrared Seeds Water
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A green and efficient method for detecting nicosulfuron residues in field maize using hyperspectral imaging and deep learning.

Journal of hazardous materials Nov 30, 2024
Accurate and rapid detection of nicosulfuron herbicide residues in field-grown maize is essential for implementing chemical remediation and optimizing spraying strategies. However, current detection methods are costly and time-consuming. This study a...
Zea mays Herbicides Pesticide Residues Sulfonylurea Compounds Hyperspectral Imaging Plant Leaves Pyridines Neural Networks, Computer Deep Learning
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Machine learning driven metal oxide-based portable sensor array for on-site detection and discrimination of mycotoxins in corn sample.

Food chemistry Nov 1, 2024
Cereals, grains, and feedstuffs are prone to contamination by fungi during various stages from growth to storage. These fungi may produce harmful mycotoxins impacting food quality and safety. Thus, the development of quick and reliable methods for on...
Discriminant Analysis Food Contamination Machine Learning Limit of Detection Mycotoxins Metals Oxides Zea mays
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Applying machine learning and genetic algorithms accelerated for optimizing ethanol production.

The Science of the total environment Oct 20, 2024
Corn straws can produce bioethanol via simultaneous saccharification and co-fermentation (SSCF). However, identifying optimal combinations of operating parameters from numerous possibilities through a cost-effective strategy to improve SSCF efficienc...
Ethanol Zea mays Fermentation Machine Learning Algorithms Biofuels Neural Networks, Computer
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Advancements in maize disease detection: A comprehensive review of convolutional neural networks.

Computers in biology and medicine Oct 9, 2024
This review article provides a comprehensive examination of the state-of-the-art in maize disease detection leveraging Convolutional Neural Networks (CNNs). Beginning with the intrinsic significance of plants and the pivotal role of maize in global a...
Algorithms Zea mays Plant Diseases Neural Networks, Computer
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Machine learning prediction of stalk lignin content using Fourier transform infrared spectroscopy in large scale maize germplasm.

International journal of biological macromolecules Sep 28, 2024
Lignin has been recognized as a major factor contributing to lignocellulosic recalcitrance in biofuel production and attracted attentions as a high-value product in the biorefinery field. As the traditional wet chemical methods for detecting lignin c...
Zea mays Machine Learning Lignin Spectroscopy, Fourier Transform Infrared Algorithms Principal Component Analysis
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High-throughput prediction of stalk cellulose and hemicellulose content in maize using machine learning and Fourier transform infrared spectroscopy.

Bioresource technology Sep 23, 2024
Cellulose and hemicellulose are key cross-linked carbohydrates affecting bioethanol production in maize stalks. Traditional wet chemical methods for their detection are labor-intensive, highlighting the need for high-throughput techniques. This study...
Machine Learning Algorithms Zea mays Polysaccharides High-Throughput Screening Assays Spectroscopy, Fourier Transform Infrared Cellulose
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Fusion of convolutional neural network with XGBoost feature extraction for predicting multi-constituents in corn using near infrared spectroscopy.

Food chemistry Aug 31, 2024
Near-infrared (NIR) spectroscopy has been widely utilized to predict multi-constituents of corn in agriculture. However, directly extracting constituent information from the NIR spectra is challenging due to many issues such as broad absorption band,...
Plant Proteins Zea mays Neural Networks, Computer Starch Spectroscopy, Near-Infrared
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