AIMC Topic: 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
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...

A green and efficient method for detecting nicosulfuron residues in field maize using hyperspectral imaging and deep learning.

Journal of hazardous materials
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...

Machine learning driven metal oxide-based portable sensor array for on-site detection and discrimination of mycotoxins in corn sample.

Food chemistry
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...

Applying machine learning and genetic algorithms accelerated for optimizing ethanol production.

The Science of the total environment
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...

Advancements in maize disease detection: A comprehensive review of convolutional neural networks.

Computers in biology and medicine
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...

Machine learning prediction of stalk lignin content using Fourier transform infrared spectroscopy in large scale maize germplasm.

International journal of biological macromolecules
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...

High-throughput prediction of stalk cellulose and hemicellulose content in maize using machine learning and Fourier transform infrared spectroscopy.

Bioresource technology
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...

Fusion of convolutional neural network with XGBoost feature extraction for predicting multi-constituents in corn using near infrared spectroscopy.

Food chemistry
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,...

Leaf rolling detection in maize under complex environments using an improved deep learning method.

Plant molecular biology
Leaf rolling is a common adaptive response that plants have evolved to counteract the detrimental effects of various environmental stresses. Gaining insight into the mechanisms underlying leaf rolling alterations presents researchers with a unique op...

Monitoring the Spatial Distribution of Cover Crops and Tillage Practices Using Machine Learning and Environmental Drivers across Eastern South Dakota.

Environmental management
The adoption of conservation agriculture methods, such as conservation tillage and cover cropping, is a viable alternative to conventional farming practices for improving soil health and reducing soil carbon losses. Despite their significance in miti...