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Plant Breeding

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

PloS one
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...

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction.

Molecular plant
The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by inco...

The estimation and interpretation of ordered logit models for assessing the factors connected with the productivity of Holstein-Friesian dairy cows in Egypt.

Tropical animal health and production
The incorporation of novel technologies such as artificial intelligence, data mining, and advanced statistical methodologies have received wide responses from researchers. This study was designed to model the factors impacting the actual milk yield o...

PollenDetect: An Open-Source Pollen Viability Status Recognition System Based on Deep Learning Neural Networks.

International journal of molecular sciences
Pollen grains, the male gametophytes for reproduction in higher plants, are vulnerable to various stresses that lead to loss of viability and eventually crop yield. A conventional method for assessing pollen viability is manual counting after stainin...

Deep-learning-based automatic evaluation of rice seed germination rate.

Journal of the science of food and agriculture
BACKGROUND: Rice is an important food crop plant in the world and is also a model plant for genetics and breeding research. The germination rate is an important indicator that measures the performance of rice seeds. Currently, solutions involving ima...

Machine Learning-Assisted Approaches in Modernized Plant Breeding Programs.

Genes
In the face of a growing global population, plant breeding is being used as a sustainable tool for increasing food security. A wide range of high-throughput omics technologies have been developed and used in plant breeding to accelerate crop improvem...

Estimation of Off-Target Dicamba Damage on Soybean Using UAV Imagery and Deep Learning.

Sensors (Basel, Switzerland)
Weeds can cause significant yield losses and will continue to be a problem for agricultural production due to climate change. Dicamba is widely used to control weeds in monocot crops, especially genetically engineered dicamba-tolerant (DT) dicot crop...

Multimodal deep learning methods enhance genomic prediction of wheat breeding.

G3 (Bethesda, Md.)
While several statistical machine learning methods have been developed and studied for assessing the genomic prediction (GP) accuracy of unobserved phenotypes in plant breeding research, few methods have linked genomics and phenomics (imaging). Deep ...

Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics.

Bioinformatics (Oxford, England)
MOTIVATION: Developing new crop varieties with superior performance is highly important to ensure robust and sustainable global food security. The speed of variety development is limited by long field cycles and advanced generation selections in plan...

Detecting SNP markers discriminating horse breeds by deep learning.

Scientific reports
The assignment of an individual to the true population of origin using a low-panel of discriminant SNP markers is one of the most important applications of genomic data for practical use. The aim of this study was to evaluate the potential of differe...