Advanced.farm Scales Apple-Picking Operations with Scale Rapid
Technology Category
- Analytics & Modeling - Machine Learning
Applicable Industries
- Agriculture
- Education
Applicable Functions
- Quality Assurance
Use Cases
- Computer Vision
- Visual Quality Detection
Services
- Testing & Certification
- Training
About The Customer
Advanced.farm is a company that is building the future of farming by using robotics to automate the toughest tasks in agriculture, making farms safer and more productive. They focus on the most resource-intensive tasks and have automated solutions for strawberry and apple picking. They are planning to scale to other challenges in the near future. Advanced.farm is building the advanced platform for agricultural equipment by combining advanced robotics, autonomous navigation, and a state-of-the-art computer vision machine learning (CVML) stack.
The Challenge
Advanced.farm, a company focused on automating agricultural tasks using robotics, was facing a challenge in refining its apple-picking capabilities. With numerous apple varieties and a short picking season, it was difficult to maintain pace. As they developed their computer vision machine learning (CVML) capabilities for apples, they needed a labeling solution that would allow them to regularly create new projects and receive a quick turnaround on labeled images. To succeed in their first apple-picking season, it was crucial for them to quickly process a large number of images through the annotation pipeline, adapt to the changing variety of apples, and ensure that their models were as accurate and efficient as possible on real data.
The Solution
To address this challenge, Advanced.farm began working with Scale Rapid to acquire large volumes of high-quality annotations. These annotations provided the data that Advanced.farm needed to improve its models. Initially, they used Scale Studio, leveraging their labeling team to take advantage of their domain expertise. However, they soon realized that the dataset sizes they hoped to train their models on required more work than their in-house staff could handle. To manage 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. With Scale Rapid, they were able to get 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.
Operational Impact
Quantitative Benefit
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