Optimizing Harvest Efficiency

Requirements and Pilot Project:

The client provided strawberry images and classification instructions to categorize berries into three groups: ripe, unripe, and partially ripe. We reviewed these requirements and suggested refinements to the classification criteria to make the annotation process as accurate and user-friendly as possible.

Data Annotation and Quality Review:

The Unidata team began annotating the images, sorting the berries into the specified ripeness categories. We considered client-provided criteria such as color, texture, and shape to enhance classification accuracy. Each annotator received additional training to standardize the approach and minimize errors.

Validation:

To ensure high accuracy, all annotated images underwent a multi-level review process. Our validation methodology involved selecting a specific data volume for re-evaluation to ensure adherence to the ripeness standard defined by the client. The validation process included several stages:

Primary Analysis:

The validation team conducted an initial review of the completed annotations to identify critical errors and detect systematic discrepancies.

Cross-Checking:

To eliminate subjective differences, several validation groups independently checked the same image sample, then compared results and refined the criteria to achieve a unified approach.

Performance Statistics and Analysis:

During validation, we closely monitored statistical data on annotator quality. This allowed us to quickly identify annotators with higher error rates and provide additional training as needed, while also recognizing top performers.

Our specialists also offered regular feedback and recommendations to annotators throughout the project. In addition to internal checks, Unidata organized clarification sessions with the client, which helped improve the evaluation criteria and achieve a higher level of consistency.