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Related Concept Videos

Plant Breeding and Biotechnology01:59

Plant Breeding and Biotechnology

Crop cultivation has a long history in human civilization, with records showing the cultivation of cereal plants beginning at around 8000 BC. This early plant breeding was developed primarily to provide a steady supply of food.
Synthetic Biology02:55

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Transgenic Plants

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Impact of Intron and Retransformation on Transgene Expression in Leaf and Fruit Tissues of Field-Grown Pear Trees.

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Related Experiment Video

Updated: May 28, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Artificial Intelligence Methods in Forest Biotechnology: Current Status and Future Prospects.

Vadim Lebedev1, Andrey Lebedev2

  • 1Branch of Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry, Russian Academy of Sciences, Pushchino 142290, Russia.

International Journal of Molecular Sciences
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

Artificial intelligence (AI) enhances forest biotechnology by improving tree breeding for high yields and stress tolerance. This review explores AI applications in tree improvement, addressing challenges and future directions for sustainable forestry.

Keywords:
artificial neural networksforest breedinggenetic engineeringin vitro culturemachine learningomics technologies

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Last Updated: May 28, 2026

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring
08:16

Collecting and Processing Drone-based Remotely Sensed Data for Use in Forest Recovery Monitoring

Published on: October 24, 2025

Area of Science:

  • Forestry Science
  • Computer Science
  • Biotechnology

Background:

  • Deforestation due to climate change and human activity poses ecological and economic risks.
  • Biotechnological breeding methods are crucial for developing resilient, high-yielding tree varieties.
  • Artificial intelligence (AI) offers advanced tools for accelerating forest biotechnology research.

Purpose of the Study:

  • To review the current applications and achievements of AI in forest biotechnology.
  • To examine machine learning and neural network use in areas like in vitro culture, transgenic plants, and genomic selection.
  • To identify challenges and ethical considerations of AI in woody plant improvement.

Main Methods:

  • Review of existing research on AI applications in forest biotechnology.
  • Analysis of machine learning algorithms and neural network architectures.
  • Discussion of AI's role in in vitro culture, transgenic plants, genome editing, omics, and genomic selection.

Main Results:

  • AI is increasingly applied across various forest biotechnology domains.
  • Machine learning and neural networks show potential in improving tree breeding and genetic analysis.
  • Challenges include data scarcity for woody plants and ethical concerns like bias and accountability.

Conclusions:

  • AI holds significant promise for advancing forest tree improvement and conservation.
  • Addressing data limitations and ethical issues is vital for responsible AI implementation in forestry.
  • Future research should focus on developing robust AI models and exploring new applications for sustainable forest management.