AIMC Topic: Zea mays

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iProm-Zea: A two-layer model to identify plant promoters and their types using convolutional neural network.

Genomics
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...

Maize leaf disease identification based on WG-MARNet.

PloS one
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...

Deep learning-based approach for identification of diseases of maize crop.

Scientific reports
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 ...

Weed Classification from Natural Corn Field-Multi-Plant Images Based on Shallow and Deep Learning.

Sensors (Basel, Switzerland)
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...

The Challenge of Data Annotation in Deep Learning-A Case Study on Whole Plant Corn Silage.

Sensors (Basel, Switzerland)
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...

Recognition of Maize Phenology in Sentinel Images with Machine Learning.

Sensors (Basel, Switzerland)
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...

Hyperparameter Optimization for Transfer Learning of VGG16 for Disease Identification in Corn Leaves Using Bayesian Optimization.

Big data
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...

Evolutionarily informed machine learning enhances the power of predictive gene-to-phenotype relationships.

Nature communications
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...

A novel lncRNA-protein interaction prediction method based on deep forest with cascade forest structure.

Scientific reports
Long noncoding RNAs (lncRNAs) regulate many biological processes by interacting with corresponding RNA-binding proteins. The identification of lncRNA-protein Interactions (LPIs) is significantly important to well characterize the biological functions...

LightGBM: accelerated genomically designed crop breeding through ensemble learning.

Genome biology
LightGBM is an ensemble model of decision trees for classification and regression prediction. We demonstrate its utility in genomic selection-assisted breeding with a large dataset of inbred and hybrid maize lines. LightGBM exhibits superior performa...