AI-driven digital twin platform for forecasting peach tree behavior under future climate scenarios

Overview

This solution offers an artificial intelligence platform that models and forecasts how peach trees will perform under future climate scenarios. By combining large genomic and phenomic datasets with climate information, the platform predicts key traits such as flowering date, harvest date, chilling requirements, fruit weight, fruit shape, frost tolerance, and yield. The technology also includes a computer vision component capable of generating realistic digital images of fruit based on genetic and environmental inputs, enabling the creation of digital twins (metaindividuals) for individual peach varieties. The platform addresses a critical need for fruit growers, breeders, and agricultural planners seeking to anticipate how climate change will affect orchard productivity and fruit quality over the coming decades.

Technical specifications

Predictive modeling capabilities:

• Random forest models trained on genomic, phenomic, and climate data from over 300 peach genotypes observed across 10 growing seasons
• High accuracy (>0.85 r²) for genetically driven traits including flowering date, harvest date, chilling requirements, and fruit shape
• Moderate accuracy (0.2–0.6 r²) for environmentally influenced traits such as fruit weight, frost tolerance, soluble solids content, and cold storage performance
• Planned expansion to include Support Vector Machine, Gradient Boosting, k-Nearest Neighbors, and Artificial Neural Networks models to improve prediction accuracy across all traits

Digital fruit imaging:

• AI-based computer vision system that recognizes fruit shape patterns from images
• Generative model to create realistic fruit images based on genomic and climate data inputs

Simulation application:

• App designed to simulate peach tree behavior across different climate scenarios over a 50-year period
• Supports analysis of metaindividuals (digital twins) and individuals with artificial genomic profiles in varied climatic environments

Technology readiness level

The predictive models have been validated using historical data from a germplasm collection exceeding 300 genotypes monitored over 10 seasons, demonstrating strong accuracy for genetically determined traits. The next development phase, requiring approximately six months, focuses on benchmarking additional machine learning architectures to improve prediction accuracy. Following model optimization, the team will build the simulation application and the generative fruit imaging model. The technology is at an advanced research and validation stage, with core predictive capabilities already demonstrated and full platform deployment expected after the planned model enhancements and app development.