Estimation of Fruit Chemical Properties Without Destructive Testing

Objective
To estimate internal chemical properties of fruit without destructive laboratory testing, improving efficiency in quality control processes.

How it works
The solution processes existing measurement data (such as imaging outputs or laboratory records) and applies predictive models to estimate internal parameters. Results are delivered as clear quality indicators through simple digital interfaces.

Core digital services
We provide the digital infrastructure to ingest, clean and structure agrifood datasets and to train predictive models tailored to specific crops or varieties. Our expertise lies in developing and validating machine learning models, ensuring robustness and interpretability. We deliver user-oriented dashboards or scoring systems that integrate into existing operational workflows.

How they apply to the agrifood sector
In fruit production and post-harvest handling, quality estimation is often costly and partially destructive. Our modelling approach enables producers and packers to use their existing data more effectively, accelerating grading decisions, reducing product waste and improving batch consistency without modifying their physical infrastructure.

Type of agrifood partner sought
We are seeking fruit producers, cooperatives, post-harvest operators or exporters that already collect quality-related data and wish to extract greater value from it through predictive modelling.