AIMC Topic: Oryza
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Hybrid feature optimized CNN for rice crop disease prediction.
Scientific reports
Mar 6, 2025
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...
Endogenous storage proteins influence Rice flavor: Insights from protein-flavor correlations and predictive modeling.
Food chemistry
Mar 5, 2025
This study investigated the correlation between endogenous storage proteins and aromatic compounds in rice, and their collective influence on rice eating quality. Six rice samples, varying in four endogenous storage proteins through gene editing gene...
An exploration of RSM, ANN, and ANFIS models for methylene blue dye adsorption using Oryza sativa straw biomass: a comparative approach.
Scientific reports
Jan 23, 2025
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
Jan 17, 2025
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...
Feedback regulation of mA modification creates local auxin maxima essential for rice microsporogenesis.
Developmental cell
Jan 13, 2025
N-methyladenosine (mA) RNA modification and its effectors control various plant developmental processes, yet whether and how these effectors are transcriptionally controlled to confer functional specificity so far remain elusive. Herein, we show that...
Phenotype prediction in plants is improved by integrating large-scale transcriptomic datasets.
NAR genomics and bioinformatics
Dec 27, 2024
Research on the dynamic expression of genes in plants is important for understanding different biological processes. We used the large amounts of transcriptomic data from various plant sample sources that are publicly available to investigate whether...
Cropformer: An interpretable deep learning framework for crop genomic prediction.
Plant communications
Dec 16, 2024
Machine learning and deep learning are extensively employed in genomic selection (GS) to expedite the identification of superior genotypes and accelerate breeding cycles. However, a significant challenge with current data-driven deep learning models ...
Enhancing the estimation of cadmium content in rice leaves by integrating vegetation indices and color indices using machine learning.
Ecotoxicology and environmental safety
Dec 16, 2024
Cadmium (Cd) is a heavy metal recognized for its notable biotoxicity. Excessive Cd levels can have detrimental effects on crop growth, development, and yield. Real-time, rapid, and nondestructive monitoring of Cd content in leaves (LCd) is essential ...
Ensemble learning-assisted quantitative identifying influencing factors of cadmium and arsenic concentration in rice grain based multiplexed data.
Journal of hazardous materials
Dec 14, 2024
Rapid and accurate prediction of rice Cd (rCd) and rice As (rAs) bioaccumulation are important for assessing the safe utilization of rice. Currently, there is lack of comprehensive and systematic exploration of the factors of rCd and rAs. Herein, ens...
Increased chloroplast occupancy in bundle sheath cells of rice hap3H mutants revealed by Chloro-Count: a new deep learning-based tool.
The New phytologist
Dec 12, 2024
There is an increasing demand to boost photosynthesis in rice to increase yield potential. Chloroplasts are the site of photosynthesis, and increasing their number and size is a potential route to elevate photosynthetic activity. Notably, bundle shea...
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