AIMC Topic: Triticum

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Sága, a Deep Learning Spectral Analysis Tool for Fungal Detection in Grains-A Case Study to Detect Fusarium in Winter Wheat.

Toxins Aug 13, 2024
Fusarium head blight (FHB) is a plant disease caused by various species of the fungus. One of the major concerns associated with spp. is their ability to produce mycotoxins. Mycotoxin contamination in small grain cereals is a risk to human and anim...
Plant Diseases Mycotoxins Fusarium Deep Learning Food Contamination Edible Grain Fungicides, Industrial Triticum
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Contribution assessment and accumulation prediction of heavy metals in wheat grain in a smelting-affected area using machine learning methods.

The Science of the total environment Aug 11, 2024
Due to the diverse controlling factors and their uneven spatial distribution, especially atmospheric deposition from smelters, assessing and predicting the accumulation of heavy metals (HM) in crops across smelting-affected areas becomes challenging....
Machine Learning Environmental Monitoring Edible Grain Metals, Heavy Metallurgy Soil Pollutants Triticum
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Using VIS-NIR hyperspectral imaging and deep learning for non-destructive high-throughput quantification and visualization of nutrients in wheat grains.

Food chemistry Jul 29, 2024
High-throughput and low-cost quantification of the nutrient content in crop grains is crucial for food processing and nutritional research. However, traditional methods are time-consuming and destructive. A high-throughput and low-cost method of quan...
High-Throughput Screening Assays Hyperspectral Imaging Nutrients Nutritive Value Spectroscopy, Near-Infrared Deep Learning Triticum Edible Grain Seeds
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Uptake of zinc from the soil to the wheat grain: Nonlinear process prediction based on artificial neural network and geochemical data.

The Science of the total environment Jul 10, 2024
Trace elements in plants primarily derive from soils, subsequently influencing human health through the food chain. Therefore, it is essential to understand the relationship of trace elements between plants and soils. Since trace elements from soils ...
Trace Elements Zinc Environmental Monitoring Neural Networks, Computer Soil Edible Grain Soil Pollutants Rhizosphere Triticum
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Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat.

The plant genome Jun 9, 2024
Fusarium head blight (FHB) remains one of the most destructive diseases of wheat (Triticum aestivum L.), causing considerable losses in yield and end-use quality. Phenotyping of FHB resistance traits, Fusarium-damaged kernels (FDK), and deoxynivaleno...
Plant Breeding Phenomics Plant Diseases Disease Resistance Phenotype Triticum Genomics Deep Learning Fusarium
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Comparative analysis of different Karnal bunt disease prediction models developed by machine learning techniques for Punjab conditions.

International journal of biometeorology May 28, 2024
Timely prediction of pathogen is important key factor to reduce the quality and yield losses. Wheat is major crop in northern part of India. In Punjab, wheat face challenge by different diseases so the study was conducted for two locations viz. Ludhi...
Plant Diseases Triticum Machine Learning Weather India Models, Theoretical
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TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield.

Plant communications May 15, 2024
Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target...
Neural Networks, Computer Phenotype Genotype Machine Learning Oryza Crops, Agricultural Zea mays Plant Breeding Triticum
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Multimodal deep learning-based drought monitoring research for winter wheat during critical growth stages.

PloS one May 9, 2024
Wheat is a major grain crop in China, accounting for one-fifth of the national grain production. Drought stress severely affects the normal growth and development of wheat, leading to total crop failure, reduced yields, and quality. To address the la...
Stress, Physiological Triticum Deep Learning Droughts Seasons China
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Estimation of wheat protein content and wet gluten content based on fusion of hyperspectral and RGB sensors using machine learning algorithms.

Food chemistry Mar 22, 2024
The protein content (PC) and wet gluten content (WGC) are crucial indicators determining the quality of wheat, playing a pivotal role in evaluating processing and baking performance. Original reflectance (OR), wavelet feature (WF), and color index (C...
Plant Proteins Glutens Algorithms Triticum Machine Learning
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Precision in wheat flour classification: Harnessing the power of deep learning and two-dimensional correlation spectrum (2DCOS).

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy Mar 5, 2024
Wheat flour is a ubiquitous food ingredient, yet discerning its various types can prove challenging. A practical approach for identifying wheat flour types involves analyzing one-dimensional near-infrared spectroscopy (NIRS) data. This paper introduc...
Algorithms Spectroscopy, Near-Infrared Triticum Deep Learning Flour Least-Squares Analysis
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