This startup develops non-GMO plants using a platform that integrates data science, bioinformatics, and gene editing tools like CRISPR-Cas9. Their technology accelerates plant development, enabling the creation of crops with improved efficiency, sustainability, and resilience to challenging climates.
Funding
Funding not disclosed
Founders
Product
Problem
Traditional agricultural field trials are costly, time-consuming, and limited in their ability to predict crop performance across diverse environmental conditions. This makes it difficult for agri-food companies to efficiently develop new products, optimize resource allocation, and respond to changing market demands and climate conditions.
Solution
Calice provides a computational field trials platform, NODES™, that uses AI and machine learning to simulate crop performance under various environmental conditions. By integrating diverse agricultural data, including climatological, soil, genomics, and phenomics data, NODES™ enables virtual testing of different crop varieties and management practices. This allows companies to accelerate product development, optimize breeding programs, and improve product placement strategies. The platform's predictive capabilities help reduce the reliance on physical field trials, leading to cost savings, increased efficiency, and more sustainable agricultural practices.
Target Audience
Calice's primary customers include biotech and seed companies, biological input providers, food and beverage producers, energy and biofuel companies, and R&D institutions focused on agriculture.
Features
- AI-powered predictive models for simulating crop performance in virtual field trials
- Integration of diverse agricultural data types, including climatological, soil, genomics, and phenomics data
- Tools for optimizing crop selection, breeding programs, and product placement strategies
- Identification of optimal genetic-environment combinations
- Prediction of hybrid yields in environments without direct trials
- Analysis of crop life cycle to identify critical environmental variables
- Target environment prediction tool to identify varieties that meet quality, yield, and adaptability demands