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A Support Vector Machine-Assisted Metabolomics Approach for Non-Targeted Screening of Multi-Class Pesticides and Veterinary Drugs in Maize.

Molecules (Basel, Switzerland)
The contamination risks of plant-derived foods due to the co-existence of pesticides and veterinary drugs (P&VDs) have not been fully understood. With an increasing number of unexpected P&VDs illegally added to foods, it is essential to develop a non...

Bioplastic derived from corn stover: Life cycle assessment and artificial intelligence-based analysis of uncertainty and variability.

The Science of the total environment
Exploring feasible and renewable alternatives to reduce dependency on traditional fossil-based plastics is critical for sustainable development. These alternatives can be produced from biomass, which may have large uncertainties and variabilities in ...

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...

Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik
Incorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to indirectly model genotype-by-environment interactions. Complementing phenotypic traits and molecular markers with hi...

Novel strategy for optimizing of corn starch-based ink food 3D printing process: Printability prediction based on BP-ANN model.

International journal of biological macromolecules
Although starch has been intensively studied as a raw material for 3D printing, the relationship between several important process parameters in the preparation of starch gels and the printing results is unclear. In this study, the relationship betwe...

Dual-Mode Fluorescent/Intelligent Lateral Flow Immunoassay Based on Machine Learning Algorithm for Ultrasensitive Analysis of Chloroacetamide Herbicides.

Analytical chemistry
Given the harmful effect of pesticide residues, it is essential to develop portable and accurate biosensors for the analysis of pesticides in agricultural products. In this paper, we demonstrated a dual-mode fluorescent/intelligent (DM-f/DM-i) latera...

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...

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...

Optimizing Corn Tar Spot Measurement: A Deep Learning Approach Using Red-Green-Blue Imaging and the Stromata Contour Detection Algorithm for Leaf-Level Disease Severity Analysis.

Plant disease
Visual detection of stromata (brown-black, elevated fungal fruiting bodies) is the primary method for quantifying tar spot early in the season because these structures are definitive signs of the disease and essential for effective disease monitoring...