Agrobacterium tumefaciens-Mediated Transformation of Ectomycorrhizal Fungi

Kemppainen, Minna; Crespo, Maria C. Alvarez; Pardo, Alejandro Guillermo

doi:10.1007/978-3-642-15196-5_6

Cited by 1 publication

(1 citation statement)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For effective gene transfer, the Agrobacterium strain, the period of inoculation, and select antibiotic concentrations must be fine-tuned [113]. Moreover, it was determined that resistance to Agrobacterium-assisted gene transformation is easily observable using ML algorithms [114]. Polyploidy is often utilized to increase the productivity and vigor of plants [115].…”

Section: And Vegetable Biotechnologymentioning

confidence: 99%

Frontiers in the Solicitation of Machine Learning Approaches in Vegetable Science Research

2021

View full text Add to dashboard Cite

Along with essential nutrients and trace elements, vegetables provide raw materials for the food processing industry. Despite this, plant diseases and unfavorable weather patterns continue to threaten the delicate balance between vegetable production and consumption. It is critical to utilize machine learning (ML) in this setting because it provides context for decision-making related to breeding goals. Cutting-edge technologies for crop genome sequencing and phenotyping, combined with advances in computer science, are currently fueling a revolution in vegetable science and technology. Additionally, various ML techniques such as prediction, classification, and clustering are frequently used to forecast vegetable crop production in the field. In the vegetable seed industry, machine learning algorithms are used to assess seed quality before germination and have the potential to improve vegetable production with desired features significantly; whereas, in plant disease detection and management, the ML approaches can improve decision-support systems that assist in converting massive amounts of data into valuable recommendations. On similar lines, in vegetable breeding, ML approaches are helpful in predicting treatment results, such as what will happen if a gene is silenced. Furthermore, ML approaches can be a saviour to insufficient coverage and noisy data generated using various omics platforms. This article examines ML models in the field of vegetable sciences, which encompasses breeding, biotechnology, and genome sequencing.

show abstract

Section: And Vegetable Biotechnologymentioning

confidence: 99%