AIMC Topic: Zea mays

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High-throughput image-based plant stand count estimation using convolutional neural networks.

PloS one Jul 28, 2022
The landscape of farming and plant breeding is rapidly transforming due to the complex requirements of our world. The explosion of collectible data has started a revolution in agriculture to the point where innovation must occur. To a commercial orga...
Plant Breeding Neural Networks, Computer Algorithms Phenotype Zea mays
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Deep Learning Approach for Detection of Underground Natural Gas Micro-Leakage Using Infrared Thermal Images.

Sensors (Basel, Switzerland) Jul 16, 2022
The leakage of underground natural gas has a negative impact on the environment and safety. Trace amounts of gas leak concentration cannot reach the threshold for direct detection. The low concentration of natural gas can cause changes in surface veg...
Zea mays Natural Gas Temperature Deep Learning
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iProm-Zea: A two-layer model to identify plant promoters and their types using convolutional neural network.

Genomics May 6, 2022
A promoter is a short DNA sequence near the start codon, responsible for initiating the transcription of a specific gene in the genome. The accurate recognition of promoters is important for achieving a better understanding of transcriptional regulat...
Promoter Regions, Genetic TATA Box Base Sequence Neural Networks, Computer Algorithms Zea mays
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Maize leaf disease identification based on WG-MARNet.

PloS one Apr 28, 2022
In deep learning-based maize leaf disease detection, a maize disease identification method called Network based on wavelet threshold-guided bilateral filtering, multi-channel ResNet, and attenuation factor (WG-MARNet) is proposed. This method can sol...
Zea mays Neural Networks, Computer Plant Leaves
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Deep learning-based approach for identification of diseases of maize crop.

Scientific reports Apr 15, 2022
In recent years, deep learning techniques have shown impressive performance in the field of identification of diseases of crops using digital images. In this work, a deep learning approach for identification of in-field diseased images of maize crop ...
Crops, Agricultural India Zea mays Deep Learning
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Weed Classification from Natural Corn Field-Multi-Plant Images Based on Shallow and Deep Learning.

Sensors (Basel, Switzerland) Apr 14, 2022
Crop and weed discrimination in natural field environments is still challenging for implementing automatic agricultural practices, such as weed control. Some weed control methods have been proposed. However, these methods are still restricted as they...
Neural Networks, Computer Zea mays Weed Control Deep Learning Plant Weeds
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The Challenge of Data Annotation in Deep Learning-A Case Study on Whole Plant Corn Silage.

Sensors (Basel, Switzerland) Feb 18, 2022
Recent advances in computer vision are primarily driven by the usage of deep learning, which is known to require large amounts of data, and creating datasets for this purpose is not a trivial task. Larger benchmark datasets often have detailed proces...
Silage Data Curation Deep Learning Zea mays Supervised Machine Learning
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Recognition of Maize Phenology in Sentinel Images with Machine Learning.

Sensors (Basel, Switzerland) Dec 24, 2021
The scarcity of water for agricultural use is a serious problem that has increased due to intense droughts, poor management, and deficiencies in the distribution and application of the resource. The monitoring of crops through satellite image process...
Support Vector Machine Zea mays Algorithms Image Processing, Computer-Assisted Machine Learning
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Hyperparameter Optimization for Transfer Learning of VGG16 for Disease Identification in Corn Leaves Using Bayesian Optimization.

Big data Dec 10, 2021
One of the world's most widely grown crops is corn. Crop loss due to diseases has a major economic effect, putting the food supply in jeopardy. In many parts of the world, lack of infrastructure still slows disease diagnosis. In this context, effecti...
Neural Networks, Computer Machine Learning Bayes Theorem Zea mays Plant Leaves
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Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.

Nature communications Sep 24, 2021
Inferring phenotypic outcomes from genomic features is both a promise and challenge for systems biology. Using gene expression data to predict phenotypic outcomes, and functionally validating the genes with predictive powers are two challenges we add...
Genomics Nitrogen Phenotype Models, Genetic Genotype Genetic Variation Gene Expression Regulation, Plant Transcriptome Evolution, Molecular Zea mays Machine Learning Arabidopsis Species Specificity Genome, Plant
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