How AI impacts to Plant Genetic Resources Conservation and Plant Genebanks Management

01 January 1970 4 3K Report

Hi colleagues ,

Greeting from Vietnam National Plant Genebank, Hanoi, Vietnam

Please let us know your knowledge on how AI could contribute to 1. Plant Resources Conservation and 2. Plant Genebanks Management

For 1. Conservation: focused in developing a flexible strategy between ex-situ and in-situ conservations. But parameters/ indicators of plant diversity, their value chains, local bio knowledge, culture and economy as well as land use transformation and change of lifestyle as well as impacts of climate in in-situ must be measured, tracked and modeled through digitalization and technologies of block chains, big data, IoTs, AI to identify trends between in-situ and ex-situ for population, species and ecosystem levels;

For 2. Genebank: IT and AI to be designed and applied on specific goals oriented breeding such climate change mitigation/adaptation; yields, quality, pest/disease resistance as well as nutrition, health and environment, etc

Thank you and hearing from you soon!!!

Zimam Ahamed

AI is transforming Plant Genetic Resources (PGR) Conservation and Genebank Management by using data-driven technologies for better preservation and utilization of plant diversity. In conservation, AI integrates big data, IoT, blockchain, and remote sensing to monitor biodiversity, predict genetic erosion, and assess land-use changes due to climate and socio-economic factors. This helps balance ex-situ and in-situ conservation strategies effectively.

For genebank management, AI enhances genomic selection, trait prediction, and automated phenotyping to improve crop resilience, yield, and disease resistance. Machine learning accelerates marker-assisted selection (MAS) and genome-wide studies (GWAS) for precision breeding. AI-powered decision-support systems optimize genetic resource utilization for food security and sustainability. Blockchain ensures transparent tracking of germplasm exchange. Overall, AI enables smarter conservation, efficient breeding, and better management of plant genetic resources.

Nguyen Van Kien

Thank my friend,

This is a piece of a landscape in Plant conservation and Plant genebank in line with their cost- benefit

it is more useful and better to much pieces given, linked, measured and tracked, compared.

We are looking your suggestion and practices soon.

Have a fun weekend.

Abdul Razzak Khan Qureshi

The preservation of plant genetic resources is critical for ensuring future food security, biodiversity, and ecosystem stability. In this context, artificial intelligence (AI) emerges as a powerful tool to enhance and revolutionize conservation efforts. This source delves into the specific applications of AI within plant gene banks and broader conservation strategies, emphasizing its impact on plant biodiversity.

Key Areas of Focus:

AI in Plant Gene Banks:Keywords: gene bank management, germplasm preservation, data analysis, automation, optimization, cryogenic storage, viability assessment. Discussion: Plant gene banks serve as vital repositories of plant genetic material. AI can significantly improve their efficiency by:Automating data management: AI algorithms can analyze vast datasets related to germplasm accessions, including phenotypic and genotypic data, streamlining cataloging and retrieval processes. Optimizing storage conditions: Machine learning can analyze environmental data to predict optimal storage conditions (temperature, humidity) for different plant species, maximizing seed viability and longevity. Improving viability assessment: AI-powered image analysis can automate the assessment of seed germination and seedling vigor, providing rapid and accurate data on germplasm viability. Enhancing cryogenic preservation: AI can analyze data related to freezing and thawing processes to optimize cryogenic protocols, ensuring the long-term preservation of valuable germplasm.

AI for Plant Genetic Resource Conservation:Keywords: biodiversity monitoring, species identification, habitat mapping, disease detection, predictive modeling, conservation planning, remote sensing. Discussion: Beyond gene banks, AI can contribute to broader conservation efforts by:Monitoring biodiversity: AI-powered image recognition and remote sensing can track plant populations, identify invasive species, and assess ecosystem health. Improving species identification: Machine learning algorithms can analyze leaf morphology, floral characteristics, and other features to accurately identify plant species, aiding in biodiversity surveys. Predicting disease outbreaks: AI can analyze environmental data and plant health indicators to predict and prevent disease outbreaks that threaten plant populations. Optimizing conservation planning: AI can integrate data from various sources to develop predictive models that inform conservation strategies, such as habitat restoration and species reintroduction. Processing remote sensing data: AI can extract meaningfull data from satelite and drone imagery, to monitor plant populations in remote locations.

Impact on Plant Biodiversity:Keywords: genetic diversity, conservation, sustainability, food security, climate change adaptation, resilience. Discussion: By enhancing the efficiency and effectiveness of conservation efforts, AI plays a crucial role in safeguarding plant biodiversity. This has significant implications for:Maintaining genetic diversity: Preserving diverse plant genetic resources is essential for adapting to climate change, developing new crop varieties, and ensuring food security. Promoting sustainable agriculture: AI-driven conservation strategies can support the development of sustainable agricultural practices that minimize environmental impact. Building resilience: By protecting plant biodiversity, AI contributes to the resilience of ecosystems and agricultural systems to environmental stressors.

Nguyen Van Kien

Thank my friend,

This is useful but before, we must have big data already to could analyze and compare which model is better one , etc and IoTs is also already

AI also has to learn and be interpreted/translated, grew and developed and self- evolved.

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