AIMC Topic: Zea mays

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Maize yield estimation in Northeast China's black soil region using a deep learning model with attention mechanism and remote sensing.

Scientific reports Apr 15, 2025
Accurate prediction of maize yields is crucial for effective crop management. In this paper, we propose a novel deep learning framework (CNNAtBiGRU) for estimating maize yield, which is applied to typical black soil areas in Northeast China. This fra...
Soil Agriculture Remote Sensing Technology Zea mays Deep Learning China
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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 Commerce Oryza COVID-19 Surveys and Questionnaires Humans Crowdsourcing Artificial Intelligence Food Security
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Grain protein function prediction based on improved FCN and bidirectional LSTM.

Food chemistry Apr 4, 2025
With the development of high-throughput sequencing technologies, predicting grain protein function from amino acid sequences based on intelligent model has become one of the significant tasks in bioinformatics. The soybean, maize, indica, and japonic...
Computational Biology Zea mays Amino Acids Plant Proteins Neural Networks, Computer Edible Grain Glycine max Amino Acid Sequence Grain Proteins
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Forewarning the seasonal dynamics of corn leafhopper and mollicutes through neural networks.

International journal of biometeorology Mar 21, 2025
The corn leafhopper (CL), Dalbulus maidis (DeLong & Wolcott) (Hemiptera: Cicadellidae), has become the most important corn pest in Brazil and other corn-producing countries. This highly efficient insect vector transmits corn stunting pathogens result...
Phytoplasma Weather Seasons Brazil Neural Networks, Computer Plant Diseases Hemiptera Zea mays Animals Spiroplasma
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Breaking the field phenotyping bottleneck in maize with autonomous robots.

Communications biology Mar 21, 2025
Understanding phenotypic plasticity in maize (Zea mays L.) is a current grand challenge for continued crop improvement. Measuring the interactive effects of genetics, environmental factors, and management practices (GxExM) on crop performance is time...
Zea mays Autonomous Robots Phenotype Robotics Machine Learning
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Leveraging Automated Machine Learning for Environmental Data-Driven Genetic Analysis and Genomic Prediction in Maize Hybrids.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Mar 6, 2025
Genotype, environment, and genotype-by-environment (G×E) interactions play a critical role in shaping crop phenotypes. Here, a large-scale, multi-environment hybrid maize dataset is used to construct and validate an automated machine learning framewo...
Genome, Plant Gene-Environment Interaction Zea mays Environment Machine Learning Phenotype Genomics Genotype Genome-Wide Association Study
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Integrating Remote Sensing and Soil Features for Enhanced Machine Learning-Based Corn Yield Prediction in the Southern US.

Sensors (Basel, Switzerland) Jan 18, 2025
Efficient and reliable corn ( L.) yield prediction is important for varietal selection by plant breeders and management decision-making by growers. Unlike prior studies that focus mainly on county-level or controlled laboratory-scale areas, this stud...
Agriculture United States Zea mays Machine Learning Remote Sensing Technology Algorithms Soil
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Optimizing Corn Tar Spot Measurement: A Deep Learning Approach Using Red-Green-Blue Imaging and the Stromata Contour Detection Algorithm for Leaf-Level Disease Severity Analysis.

Plant disease Dec 31, 2024
Visual detection of stromata (brown-black, elevated fungal fruiting bodies) is the primary method for quantifying tar spot early in the season because these structures are definitive signs of the disease and essential for effective disease monitoring...
Neural Networks, Computer Algorithms Zea mays Image Processing, Computer-Assisted Plant Diseases Deep Learning Plant Leaves Detection Algorithms
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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...
Oryza Gene Expression Regulation, Plant Phenotype Gene Expression Profiling Databases, Genetic Transcriptome Zea mays Machine Learning
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Gradient boosting for yield prediction of elite maize hybrid ZhengDan 958.

PloS one Dec 17, 2024
Understanding accurate methods for predicting yields in complex agricultural systems is critical for effective nutrient management and crop growth. Machine learning has proven to be an important tool in this context. Numerous studies have investigate...
Zea mays Machine Learning Algorithms
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