AIMC Topic: Oryza

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Insights into the comparison of machine learning models on rice grain arsenic prediction: Interplay of rice cultivation systems and soil environmental factors.

Environmental pollution (Barking, Essex : 1987)
Arsenic (As) exposure to rice threatens food safety while transferring As to rice from paddy soils significantly impacts increasing As levels in rice. This study explores establishing an efficient model for predicting As accumulation in rice grain us...

Diffusive gradient in thin films combined with machine learning to discern the accumulation characteristics and driving factors of Cd and Cu in soil-rice systems.

Journal of hazardous materials
The dietary exposure risk of cadmium (Cd) in rice is significantly higher than that of copper (Cu), while the co-migration of Cd and Cu in the soil-crop system may enhance the bioavailability of pollution, thus making rapid and accurate prediction of...

Mapping QTLs for PHS resistance and development of a deep learning model to measure PHS rate in japonica rice.

The plant genome
Rice (Oryza sativa L.) is a staple food for more than half of the global population. Preharvest sprouting (PHS), which reduces yield and grain quality, presents a major challenge for rice production. The development of PHS-resistant varieties is a ma...

Optimizing genomic prediction with transfer learning under a ridge regression framework.

The plant genome
Genomic selection (GS) is a predictive plant and animal methodology that allows the selection of plants and animals based on predictions without the need to measure the phenotype. However, its practical application requires challenging prediction acc...

Hidden threats beneath: uncovering the bio-accessible hazards of chromite-asbestos mine waste and their impacts on rice components via multi-machine learning algorithm.

Environmental geochemistry and health
The chromite-asbestos mining leaves behind tonnes of toxic waste, contaminating nearby agricultural fields with potentially toxic elements (PTEs). Over time, wind and water erosion spread these pollutants, severely impacting the ecosystem, food chain...

Prediction of total phosphorus removal in hybrid constructed wetlands: a machine learning approach for rice mill wastewater treatment.

Water environment research : a research publication of the Water Environment Federation
Efficient prediction of pollutant concentrations in constructed wetlands is critical for optimizing treatment performance, yet existing methodologies often fail to account for the influence of meteorological conditions and flow rate variations in rea...

Harnessing artificial intelligence for advancements in Rice / wheat functional food Research and Development.

Food research international (Ottawa, Ont.)
In recent years, the research and development (R&D) of rice and wheat functional foods has attracted a widespread attention from food researchers, driven by the increasing global food consumption and growing consumer demand for healthier and safer fo...

HD-6mAPred: a hybrid deep learning approach for accurate prediction of N6-methyladenine sites in plant species.

PeerJ
BACKGROUND: N6-methyladenine (6mA) is an important DNA methylation modification that serves a crucial function in various biological activities. Accurate prediction of 6mA sites is essential for elucidating its biological function and underlying mech...

Multi-class rice seed recognition based on deep space and channel residual network combined with double attention mechanism.

PloS one
Accurately recognizing rice seed varieties poses significant challenges due to their diverse morphological characteristics and complex classification requirements. Traditional image recognition methods often struggle with both accuracy and efficiency...

RiceSNP-ABST: a deep learning approach to identify abiotic stress-associated single nucleotide polymorphisms in rice.

Briefings in bioinformatics
Given the adverse effects faced by rice due to abiotic stresses, the precise and rapid identification of single nucleotide polymorphisms (SNPs) associated with abiotic stress traits (ABST-SNPs) in rice is crucial for developing resistant rice varieti...