AIMC Topic: Zea mays

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Application of a U-Net Neural Network to the Maize Pathosystem.

Phytopathology Apr 22, 2024
Computer vision approaches to analyze plant disease data can be both faster and more reliable than traditional, manual methods. However, the requirement of manually annotating training data for the majority of machine learning applications can presen...
Neural Networks, Computer Machine Learning Plant Diseases Plant Leaves Puccinia Zea mays
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Integrating portable NIR spectrometry with deep learning for accurate Estimation of crude protein in corn feed.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Mar 28, 2024
This study investigates the challenges encountered in utilizing portable near-infrared (NIR) spectrometers in agriculture, specifically in developing predictive models with high accuracy and robust generalization abilities despite limited spectral re...
Spectroscopy, Near-Infrared Deep Learning Proteins Agriculture Zea mays
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Prediction of matrilineal specific patatin-like protein governing in-vivo maternal haploid induction in maize using support vector machine and di-peptide composition.

Amino acids Mar 9, 2024
The mutant matrilineal (mtl) gene encoding patatin-like phospholipase activity is involved in in-vivo maternal haploid induction in maize. Doubling of chromosomes in haploids by colchicine treatment leads to complete fixation of inbreds in just one g...
Phospholipases Peptides Support Vector Machine Zea mays Haploidy
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A quality detection method of corn based on spectral technology and deep learning model.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Sep 27, 2023
Corn is an important food crop in the world. With economic development and population growth, the nutritional quality of corn is of great significance to high-quality breeding, scientific cultivation and fine management. Aiming at the problems of cum...
Seeds Principal Component Analysis Bayes Theorem Zea mays Deep Learning Support Vector Machine Technology Algorithms
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Integrated transcriptomic meta-analysis and comparative artificial intelligence models in maize under biotic stress.

Scientific reports Sep 23, 2023
Biotic stress imposed by pathogens, including fungal, bacterial, and viral, can cause heavy damage leading to yield reduction in maize. Therefore, the identification of resistant genes paves the way to the development of disease-resistant cultivars a...
Algorithms Transcriptome Artificial Intelligence Bayes Theorem Zea mays
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Enhancing corn stalk-based anaerobic digestion with different types of zero-valent iron added during the acidification stage: Performance and mechanism.

Journal of environmental sciences (China) Aug 5, 2023
Anaerobic digestion has been defined as a competitive approach to facilitate the recycling of corn stalks. However, few studies have focused on the role of direct interspecies electron transfer (DIET) pathway in the acidification stage under the addi...
Methane Bioreactors Anaerobiosis Iron Fatty Acids, Volatile Zea mays
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Early Identification of Root Damages Caused by Western Corn Rootworms Using a Minimally Invasive Root Phenotyping Robot-MISIRoot.

Sensors (Basel, Switzerland) Jun 28, 2023
Western corn rootworm (WCR) is one of the most devastating corn rootworm species in North America because of its ability to cause severe production loss and grain quality damage. To control the loss, it is important to identify the infection of WCR a...
Plant Roots Larva Zea mays Animals Robotics Coleoptera
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Prediction of Soil Water-Soluble Organic Matter by Continuous Use of Corn Biochar Using Three-Dimensional Fluorescence Spectra and Deep Learning.

Computational intelligence and neuroscience Mar 6, 2023
The purpose is to study the soil's water-soluble organic matter and improve the utilization rate of the soil layer. This exploration is based on the theories of three-dimensional fluorescence spectroscopy, deep learning, and biochar. Chernozem in Har...
Deep Learning Water Nitrogen Fluorescence Soil Carbon Zea mays
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UAV Multisensory Data Fusion and Multi-Task Deep Learning for High-Throughput Maize Phenotyping.

Sensors (Basel, Switzerland) Feb 6, 2023
Recent advances in unmanned aerial vehicles (UAV), mini and mobile sensors, and GeoAI (a blend of geospatial and artificial intelligence (AI) research) are the main highlights among agricultural innovations to improve crop productivity and thus secur...
Zea mays Edible Grain Nitrogen Artificial Intelligence Unmanned Aerial Devices Deep Learning
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Y-Net: Identification of Typical Diseases of Corn Leaves Using a 3D-2D Hybrid CNN Model Combined with a Hyperspectral Image Band Selection Module.

Sensors (Basel, Switzerland) Jan 29, 2023
Corn diseases are one of the significant constraints to high-quality corn production, and accurate identification of corn diseases is of great importance for precise disease control. Corn anthracnose and brown spot are typical diseases of corn, and t...
Zea mays Neural Networks, Computer
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