AIMC Topic: Oryza

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Deep learning-based automatic diagnosis of rice leaf diseases using ensemble CNN models.

Scientific reports Jul 29, 2025
Rice diseases pose a critical threat to global crop yields, underscoring the need for rapid and accurate diagnostic tools to ensure effective crop management and productivity. Traditional diagnostic approaches often lack both precision and scalabilit...
Plant Leaves Oryza Plant Diseases Neural Networks, Computer Deep Learning
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An automated hybrid deep learning framework for paddy leaf disease identification and classification.

Scientific reports Jul 24, 2025
In India, agriculture remains the primary source of livelihood for many people. Pathogen attacks in crops and plants significantly diminish both the yield and quality of production, leading to financial losses. As a result, identifying diseases in cr...
Oryza Crops, Agricultural Algorithms Plant Diseases Image Processing, Computer-Assisted Plant Leaves Deep Learning
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Photosynthetic performance index (PIabs) and malondialdehyde (MDA) content determine rice biomass under combined salt stress and prohexadione-calcium treatment.

BMC plant biology Jul 2, 2025
BACKGROUND: Plant growth regulators (PGRs) have proven effective in alleviating salt stress damage in crops, yet the predictive hierarchy of physiological traits governing biomass under combined salt-PGR regimes remains unclear. In this study, we app...
Plant Growth Regulators Photosynthesis Salt Stress Oryza Hexanes Calcium Compounds Biomass Malondialdehyde
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Development and evaluation of an automated classification and counting system for rice planthoppers captured on survey boards.

Scientific reports Jul 1, 2025
Rice planthoppers are the most economically important insect pests of rice in Asia. Traditional surveys to examine their abundance and composition in paddy fields involve human visual inspection, which requires considerable time and effort by expert ...
Deep Learning Oryza Hemiptera Animals Male Female
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Comparative analysis of attentional mechanisms in rice pest identification.

Scientific reports Jul 1, 2025
Accurate detection of rice pests helps farmers take timely control measures. This study compares different attention mechanisms for rice pest detection in complex backgrounds and demonstrates that a human vision-inspired Bionic Attention (BA) mechani...
Oryza Attention Bionics Humans Neural Networks, Computer
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CATransU-Net: Cross-attention TransU-Net for field rice pest detection.

PloS one Jun 25, 2025
Accurate detection of rice pests in field is a key problem in field pest control. U-Net can effectively extract local image features, and Transformer is good at dealing with long-distance dependencies. A Cross-Attention TransU-Net (CATransU-Net) mode...
Insecta Pest Control Animals Image Processing, Computer-Assisted Oryza Neural Networks, Computer Algorithms
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DeepRice6mA: A convolutional neural network approach for 6mA site prediction in the rice Genome.

PloS one Jun 18, 2025
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...
Computational Biology DNA Methylation Adenine Genome, Plant Oryza Neural Networks, Computer Convolutional Neural Networks ROC Curve Deep Learning
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AI-imputed and crowdsourced price data show strong agreement with traditional price surveys in data-scarce environments.

PloS one Apr 8, 2025
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...
Nigeria Zea mays Food Supply Crowdsourcing COVID-19 Oryza Commerce Food Security Surveys and Questionnaires Humans Artificial Intelligence
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Deep Neural Network-Mining of Rice Drought-Responsive TF-TAG Modules by a Combinatorial Analysis of ATAC-Seq and RNA-Seq.

Plant, cell & environment Mar 31, 2025
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...
Stress, Physiological Gene Expression Regulation, Plant Chromatin Immunoprecipitation Sequencing Droughts Data Mining Plant Proteins Transcription Factors Oryza RNA-Seq Gene Regulatory Networks Neural Networks, Computer
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Artificial intelligence for sustainable farming with dual branch convolutional graph attention networks in rice leaf disease detection.

Scientific reports Mar 27, 2025
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...
Neural Networks, Computer Artificial Intelligence Deep Learning Image Processing, Computer-Assisted Oryza Plant Leaves Agriculture Plant Diseases
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