AIMC Topic: Oryza

Clear Filters Showing 21 to 30 of 151 articles

DeepRice6mA: A convolutional neural network approach for 6mA site prediction in the rice Genome.

PloS one
As one of the most critical post-replication modifications, N6-methylation (6mA) at adenine residue plays an important role in a variety of biological functions. Existing computational methods for identifying 6mA sites across large genomic regions te...

Advanced data-driven interpretable analysis for predicting resistant starch content in rice using NIR spectroscopy.

Food chemistry
Resistant starch (RS) is a vital dietary component with notable health benefits, but tradition quantification methods are labor-intensive, costly, and unsuitable for large-scale applications. This study introduced an innovative data-driven framework ...

AI-imputed and crowdsourced price data show strong agreement with traditional price surveys in data-scarce environments.

PloS one
Continuous access to up-to-date food price data is crucial for monitoring food security and responding swiftly to emerging risks. However, in many food-insecure countries, price data is often delayed, lacks spatial detail, or is unavailable during cr...

A fluorescence probe-smartphone-machine learning integrated platform for the visual and intelligent detection of imidacloprid in agricultural products.

Food chemistry
Imidacloprid is a pesticide commonly used in agriculture production. Portable and accurate detection of imidacloprid residues is of great significance to food safety and human health. Herein, a red-emitting rare earth complex (Eu-IMDC) probe is prepa...

Deep Neural Network-Mining of Rice Drought-Responsive TF-TAG Modules by a Combinatorial Analysis of ATAC-Seq and RNA-Seq.

Plant, cell & environment
Drought is a critical risk factor that impacts rice growth and yields. Previous studies have focused on the regulatory roles of individual transcription factors in response to drought stress. However, there is limited understanding of multi-factor st...

Artificial intelligence for sustainable farming with dual branch convolutional graph attention networks in rice leaf disease detection.

Scientific reports
Rice is susceptible to various diseases, including brown spot, hispa, leaf smut, bacterial leaf blight, and leaf blast, all of which can negatively impact crop yields. Current disease detection methods encounter several challenges, such as reliance o...

Study on lightweight rice blast detection method based on improved YOLOv8.

Pest management science
BACKGROUND: Rice diseases that are not detected in a timely manner may trigger large-scale yield reduction and bring significant economic losses to farmers.

Leveraging ML to predict climate change impact on rice crop disease in Eastern India.

Environmental monitoring and assessment
Rice crop disease is critical in precision agriculture due to various influencing components and unstable environments. The current study uses machine learning (ML) models to predict rice crop disease in Eastern India based on biophysical factors for...

Origin traceability of agricultural products: A lightweight collaborative neural network for spectral information processing.

Food research international (Ottawa, Ont.)
The natural conditions of various regions, including climate, soil, and water quality, significantly influence the nutrient composition and quality of agricultural products. Identifying the origin of agricultural products can prevent adulteration, im...

Hybrid feature optimized CNN for rice crop disease prediction.

Scientific reports
The agricultural industry significantly relies on autonomous systems for detecting and analyzing rice diseases to minimize financial and resource losses, reduce yield reductions, improve processing efficiency, and ensure healthy crop production. Adva...