AIMC Topic: Triticum

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Machine Learning-Assisted Ratiometric Fluorescence Electrospun Nanofiber Films for Portable and Intelligent Monitoring of Multiple Alkylresorcinol Homologues in Whole Wheat Foods.

ACS applied materials & interfaces
The intelligent authentication of whole wheat products remains a significant challenge due to the difficulty in simultaneously monitoring multiple alkylresorcinol (AR) homologues within complex food matrices. To address this, we have developed a nove...

A novel agricultural commodity price prediction model integrating deep learning and enhanced swarm intelligence algorithm.

PloS one
The volatility of agricultural commodity prices significantly affects market stability and financial market dynamics, especially during periods of economic uncertainty and global shocks. Accurate price prediction, however, remains challenging due to ...

Novel dual-input stream-based hybrid approach for wheat leaf disease classification using edge-aware features.

Scientific reports
The prevalence of diseases in wheat crops poses a significant threat to global food security, as it reduces yield and quality. Addressing these challenges is critical for sustainable agriculture. This study proposes and evaluates a hybrid deep learni...

Enhanced YOLO-based framework for accurate detection and identification of common wheat impurities with distinct objects.

Scientific reports
Real-time detecting and identifying impurities in wheat grain mass is crucial for wheat storage silos, flour mills and modern combines. Depending on the detection objectives, accuracy is typically prioritized in laboratory-based applications, whereas...

Enhancing image based classification for crop disease detection using a multiclass SVM approach with kernel comparison.

Scientific reports
Agricultural production is still quite susceptible to plant diseases, despite the fact that it is essential to both economic growth and food security. Yellow rust can lower wheat yields by 20-30%, red rust by 5-10%, and anthracnose by up to 60% in cr...

Enhanced wheat crop leaf disease classification using multi-level contrast enhancement and modified vision transformers.

Scientific reports
The integration of advanced tools and techniques has significantly boosted agricultural productivity. Wheat crops, which are vital for global food security, are often susceptible to various bacterial and viral diseases, considerably impacting both yi...

Lightweight dual-stage feature refinement for black gram leaf disease classification using ConViTSE.

Scientific reports
Black gram, also known as urad bean, is an economically crucial crop widely cultivated in India, particularly in the central and southern regions. However, black gram is highly prone to multiple leaf diseases, resulting in considerable crop losses an...

Food defect detection technologies based on deep learning and prospects in detection of unsound wheat kernels.

Food chemistry
With rising concerns over global food security and quality pressures and the rapid advancement of agricultural intelligence, wheat quality detection demands higher efficiency, accuracy, and automation. Unsound wheat kernels, which adversely affect fl...

T10SLRE: A novel ensemble learning approach for rapid and non-destructive prediction of bread loaf volume in wheat using NIR spectroscopy.

Food chemistry
Bread loaf volume is a critical indicator of wheat processing quality, but conventional bread-making tests are laborious and time-consuming. This study evaluated near-infrared spectroscopy combined with machine learning for rapid prediction of loaf v...

Evaluation of ion mobility, uni- and multidimensional liquid chromatography for non-target screening of phenolic compounds in wheat flag leaves.

Journal of chromatography. A
Non-target screening (NTS) of plant secondary metabolites is analytically challenging due to the complexity of mixtures with structurally similar compounds and isomers. This study evaluates the added value of ion mobility spectrometry (IMS) and compr...