Case study: predicting Braeburn apple storage disorders with sensors

Apple growers often face a frustrating problem: fruit that looks perfect at harvest but develops hidden defects during storage. Issues like browning inside the apple or loss of firmness cause major waste and financial losses. These disorders are hard to predict because they depend on many factors — from orchard weather to storage conditions.

A research team tested a new way to solve this. Instead of relying on destructive tests (where fruit has to be cut open), they used non-destructive sensors combined with big data analysis. Over three years, they monitored Braeburn apples, recording weather data, orchard treatments, and sensor readings on pigments (like chlorophyll and anthocyanins), sugar levels, and dry matter.

This information was run through advanced models to see if they could predict storage outcomes. The results were promising:

-Internal browning was predicted with 90% accuracy across different years.

-Fruit firmness after storage was predicted with 80% accuracy.

The study shows that simple sensors, paired with smart data models, could help growers decide the best harvest times and storage methods. If adopted on a large scale, this technology could reduce waste, save resources, and give consumers better-quality apples.

Felix Instruments’ F-750 Produce Quality Meter and F-751 series give growers fast, non-destructive readings on firmness, sugars, pigments, and dry matter. When paired with predictive storage-disorder models, these tools turn raw data into clear decisions—helping orchards pick at the right time, store smarter, and cut waste.

Discover how the F-750 and F-751 can transform your harvest and storage decisions — contact us today to arrange a free trial.