AIMC Topic: Triticum

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Prediction of Deoxynivalenol contamination in wheat kernels and flour based on visible near-infrared spectroscopy, feature selection and machine learning modelling.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Jan 7, 2025
Contamination of wheat by the mycotoxin Deoxynivalenol (DON), produced by Fusarium fungi, poses significant challenges to the quality of crop yield and food safety. Visible and near-infrared (vis-NIR) spectroscopy has emerged as a promising, non-dest...
Food Contamination Machine Learning Triticum Algorithms Flour Trichothecenes Spectroscopy, Near-Infrared
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Seed Protein Content Estimation with Bench-Top Hyperspectral Imaging and Attentive Convolutional Neural Network Models.

Sensors (Basel, Switzerland) Jan 7, 2025
Wheat is a globally cultivated cereal crop with substantial protein content present in its seeds. This research aimed to develop robust methods for predicting seed protein concentration in wheat seeds using bench-top hyperspectral imaging in the visi...
Neural Networks, Computer Machine Learning Spectroscopy, Near-Infrared Support Vector Machine Seeds Triticum Convolutional Neural Networks Image Processing, Computer-Assisted Hyperspectral Imaging Plant Proteins
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An effective method for detecting the wheat freshness by integrating biophotonics and machine learning algorithm.

Scientific reports Dec 30, 2024
The accurate and timely assessment of wheat freshness is not only a complex scientific endeavor but also a critical aspect of grain storage safety. This study introduces an innovative approach for evaluating wheat freshness by integrating machine lea...
Support Vector Machine Triticum Machine Learning Algorithms
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Wheat disease recognition method based on the SC-ConvNeXt network model.

Scientific reports Dec 30, 2024
When utilizing convolutional neural networks for wheat disease identification, the training phase typically requires a substantial amount of labeled data. However, labeling data is both complex and costly. Additionally, the model's recognition perfor...
Plant Diseases Algorithms Triticum Neural Networks, Computer
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Lightweight deep learning algorithm for real-time wheat flour quality detection via NIR spectroscopy.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Dec 22, 2024
Wheat flour quality, determined by factors such as protein and moisture content, is crucial in food production. Traditional methods for analyzing these parameters, though precise, are time-consuming and impractical for large-scale operations. This st...
Spectroscopy, Near-Infrared Deep Learning Triticum Quality Control Food Analysis Flour Neural Networks, Computer Food Quality Algorithms
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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 ...
Genome, Plant Plant Breeding Oryza Crops, Agricultural Polymorphism, Single Nucleotide Genotype Triticum Phenotype Zea mays Deep Learning Solanum lycopersicum Genomics
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Development of multistage crop yield estimation model using machine learning and deep learning techniques.

International journal of biometeorology Dec 7, 2024
In this research paper, machine learning techniques were applied to a multivariate meteorological time series data for estimating the wheat yield of five districts of Punjab. Wheat yield data and weather parameters over 34 years were collected from t...
Weather Crops, Agricultural Machine Learning Deep Learning Models, Theoretical Triticum Neural Networks, Computer India
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Discrimination of wheat gluten quality utilizing terahertz time-domain spectroscopy (THz-TDS).

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Nov 17, 2024
Wheat is an important food crop in the world, and wheat gluten quality is one of the important standards for judging the use of wheat. In this study, a combination of chemometric and machine learning methods based on THz-TDS were used to identify thr...
Glutens Support Vector Machine Neural Networks, Computer Terahertz Spectroscopy Refractometry Algorithms Triticum
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Integrating deep learning for visual question answering in Agricultural Disease Diagnostics: Case Study of Wheat Rust.

Scientific reports Nov 15, 2024
This paper presents a novel approach to agricultural disease diagnostics through the integration of Deep Learning (DL) techniques with Visual Question Answering (VQA) systems, specifically targeting the detection of wheat rust. Wheat rust is a pervas...
Triticum Plant Diseases Basidiomycota Agriculture Plant Leaves Deep Learning Image Processing, Computer-Assisted
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Decoding wheat contamination through self-assembled whole-cell biosensor combined with linear and non-linear machine learning algorithms.

Biosensors & bioelectronics Oct 21, 2024
The contamination of mycotoxins is a serious problem around the world. It has detrimental effects on human beings and leads to tremendous economic loss. It is essential to develop a rapid and non-destructive method for contamination recognition parti...
Algorithms Humans Biosensing Techniques Triticum Food Contamination Neural Networks, Computer Mycotoxins Support Vector Machine Least-Squares Analysis Machine Learning Fungi
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