Applications of Generative AI in Climate - Resilient Breeding Programs

Applications of Generative AI in Climate - Resilient Breeding Programs.

[image] Person planting seedlings. Introduction. Generative AI can play a significant role in climate-resilient breeding programs by accelerating the development of crops that are more adaptable to changing environmental conditions. Here are several applications of generative AI in this context:.

Applications. 1 Crop Design Accelerated Breeding Phenotype Prediction 3 Crossbreeding Optimization 4 Genomic Prediction 7 5 6 Predicting Climate-Induced Changes 10 Optimizing Resource Allocation 8 Genetic Diversity Preservation 9 Data Integration 2 Crop Simulation and Modelling.

Crop Simulation and Modelling. Generative AI can be used to create highly detailed simulations and models of plant growth, taking into account various environmental factors such as temperature, precipitation, soil quality, and CO2 levels. These models can help breeders predict how different crop varieties will perform under specific climate scenarios..

Phenotype Prediction. Generative AI can analyse massive datasets of plant or animal phenotypes (observable traits) and genetic information to predict how specific genetic combinations might express themselves in different environmental conditions. This can help breeders select the most promising candidates for further breeding..

Genomic Prediction. Generative AI can enhance genomic prediction models, which estimate the genetic potential of an individual based on its DNA. By analysing large genomic datasets, AI can identify genetic markers associated with climate resilience traits and assist breeders in selecting parents for breeding programs..

Crossbreeding Optimization. Generative AI can optimize crossbreeding strategies by considering the genetic diversity and potential of parent organisms to produce offspring with improved climate resilience. This can help breeders design breeding programs that are more likely to succeed in producing resilient progeny..

Crop Design. Generative AI can be used to design new crop varieties with specific traits, such as drought tolerance, heat resistance, or disease resistance, by generating synthetic genetic sequences that are likely to produce the desired characteristics..

Predicting Climate-Induced Changes. AI can analyse climate data to predict how specific regions will be affected by climate change. Breeders can use this information to focus their efforts on developing crops that are particularly suited to the expected future conditions in a given area..

Accelerated Breeding. By automating the analysis of genetic data and the generation of potential breeding combinations, generative AI can significantly speed up the breeding process. This allows breeders to respond more quickly to changing climate conditions..

Genetic Diversity Preservation. AI can help breeders identify and preserve genetic diversity within breeding populations. This is essential for maintaining the adaptability of crops in the face of rapidly changing climates..

Data Integration. Generative AI can integrate data from various sources, including genomics, phenomics, environmental data, and historical breeding records, to provide a holistic view of the breeding program and guide decision-making..

Optimizing Resource Allocation. AI can help breeders allocate their resources more efficiently by identifying the most promising candidates and breeding strategies, reducing the cost and time associated with traditional trial-and-error approaches..

Summary. In summary, generative AI has the potential to revolutionize climate-resilient breeding programs by providing advanced tools for data analysis, modelling, and decision support. These applications can help breeders develop crops that are better equipped to thrive in a changing climate, ensuring food security and sustainability for the future..

National Conference on 'Generative AI in Practice for Empowering Agricultural Research Productivity'.

[image] Seedling growing in soil in the sunlight.