AIMC Topic: Triticum

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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...
Flour Food Quality Algorithms Spectroscopy, Near-Infrared Deep Learning Food Analysis Triticum Quality Control Neural Networks, Computer
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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 ...
Phenotype Deep Learning Genomics Triticum Solanum lycopersicum Oryza Polymorphism, Single Nucleotide Genotype Zea mays Plant Breeding Crops, Agricultural Genome, Plant
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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...
Deep Learning India Models, Theoretical Triticum Neural Networks, Computer Weather Machine Learning Crops, Agricultural
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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 Refractometry Terahertz Spectroscopy Algorithms Support Vector Machine Triticum Neural Networks, Computer
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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...
Plant Diseases Basidiomycota Plant Leaves Triticum Deep Learning Image Processing, Computer-Assisted Agriculture
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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...
Machine Learning Triticum Algorithms Mycotoxins Support Vector Machine Fungi Biosensing Techniques Food Contamination Humans Least-Squares Analysis Neural Networks, Computer
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Ensemble and optimization algorithm in support vector machines for classification of wheat genotypes.

Scientific reports Sep 30, 2024
This study aimed to classifying wheat genotypes using support vector machines (SVMs) improved with ensemble algorithms and optimization techniques. Utilizing data from 302 wheat genotypes and 14 morphological attributes to evaluate six SVM kernels: l...
Triticum Algorithms Genotype Support Vector Machine
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Evaluation of crop water stress index of wheat by using machine learning models.

Environmental monitoring and assessment Sep 23, 2024
The Crop Water Stress Index (CWSI), a pivotal indicator derived from canopy temperature, plays a crucial role in irrigation scheduling for water conservation in agriculture. This study focuses on determining CWSI (by empirical method) for wheat crops...
Agricultural Irrigation Neural Networks, Computer Dehydration Crops, Agricultural Machine Learning Agriculture Environmental Monitoring Temperature Triticum Water India
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Unraveling the complex interactions between ozone pollution and agricultural productivity in China's main winter wheat region using an interpretable machine learning framework.

The Science of the total environment Sep 14, 2024
Surface ozone has become a significant atmospheric pollutant in China, exerting a profound impact on crop production and posing a serious threat to food security. Previous studies have extensively explored the physiological mechanisms of ozone damage...
Agriculture Environmental Monitoring Air Pollutants Air Pollution Ozone Climate Change Machine Learning Triticum China
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Predicting Cd accumulation in crops and identifying nonlinear effects of multiple environmental factors based on machine learning models.

The Science of the total environment Aug 24, 2024
The traditional prediction of the Cd content in grains (Cd) of crops primarily relies on the multiple linear regression models based on soil Cd content (Cd) and pH, neglecting inter-factorial interactions and nonlinear causal links between external e...
Triticum Cadmium China Machine Learning Crops, Agricultural Soil Oryza Environmental Monitoring Soil Pollutants
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