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Oryza

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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...

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...

An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach.

Scientific reports
This study focused on simulating the adsorption-based separation of Methylene Blue (MB) dye utilising Oryza sativa straw biomass (OSSB). Three distinct modelling approaches were employed: artificial neural networks (ANN), adaptive neuro-fuzzy inferen...

Field-scale detection of Bacterial Leaf Blight in rice based on UAV multispectral imaging and deep learning frameworks.

PloS one
Bacterial Leaf Blight (BLB) usually attacks rice in the flowering stage and can cause yield losses of up to 50% in severely infected fields. The resulting yield losses severely impact farmers, necessitating compensation from the regulatory authoritie...

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...

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...

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...

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...

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...

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...