Plant phenotyping is important for plants to cope with environmental changes and ensure
plant health. Imaging techniques are perceived as the most critical and reliable tools for …

A transformer is the foundational architecture behind large language models designed to
handle sequential data by using mechanisms of self‐attention to weigh the importance of …

This study focuses on the potential of multi-omics and machine learning approaches in
improving our understanding of the malting processes and cultivation systems in barley. The …

Intelligent agriculture is critical for guiding agricultural production and enhancing efficiency
through early disease diagnosis, yield estimation, automatic harvest, and postharvest …

There is increasing interest in harnessing the microbiome to improve cropping systems. With
the availability of high—throughput and low—cost sequencing technologies, gathering …

Deep learning methods have been applied when working to enhance the prediction
accuracy of traditional statistical methods in the field of plant breeding. Although deep …

Machine learning techniques for crop genomic selections, especially for single-environment
plants, are well-developed. These machine learning models, which use dense genome …

Camellia drupifera is mainly distributed in southern China. Due to geographical isolation,
the C. drupifera resources in Hainan Island are very different from those in mainland China …

Purpose More accurate prediction of phenotype traits can increase the success of genomic
selection in both plant and animal breeding studies and provide more reliable disease risk …

The expression of quantitative traits of a line of a crop depends on its genetics, the
environment where it is sown and the interaction between the genetic information and the …