With Scale Rapid, advanced.farm Scales its Apple-Picking Operations

Advanced.farm began working with Scale Rapid to acquire large volumes of high-quality annotations to address this challenge. These annotations provide the data that advanced.farm needed to improve its models. 

Advanced.farm came to Scale for training data to enable their robots to pick apples more reliably—with precision and speed. At first, they turned to Scale Studio, leveraging their labeling team to take advantage of their domain expertise. However, they quickly found that the dataset sizes they hoped to train their models on required more work than their in-house staff could accommodate. 

To tackle the transition from strawberries to apples and scale to new orchards, advanced.farm turned to Scale to reliably and robustly produce training data with accurately labeled apples.

The biggest bottleneck in training or retraining models is getting quality annotations. Previously this took advanced.farm up to a week, but with Rapid, they got quality data within 24 hours, which helped them iterate throughout the Apple season as their needs changed.

Advanced.farm required 50,000 annotations to be returned within 24 hours, and Scale completed these annotations within that time frame at high quality. Other providers took a full week to return the same volume of annotations.

With other solutions, advanced.farm found it challenging to incorporate several different annotation attributes into a single project, but with Rapid, they could confidently create projects with multiple complex attributes and receive quality turnaround quickly. In turn, this improved the efficiency of its data pipelines so it could get the performance it needed more quickly.