Strawberry Weight Estimation Based on Plane-Constrained Binary Division Point Cloud Completion

Labor shortages and the development of digital technology both impose requirements on the fruit industry. Modern agricultural competition has shifted from competition between products to competition between supply chains. Enhancing the digitization of production lines is crucial for gaining a competitive advantage. Strawberries, as fruits with a short shelf life, require sorting and packaging of fruits of different weights after being harvested. Estimating strawberry weight through visual technology can save time and labor costs. Common methods include methods based on feature size and learning-based methods, with the former having larger errors and the latter requiring a large amount of data. To address these issues, we propose a dataset for estimating strawberry weight, which includes strawberries with different heights and angles. Additionally, we propose a strawberry weight estimation method based on plane-constrained binary division point cloud completion. This method separates the plane point cloud and strawberry point cloud, constructs a coordinate system on the strawberry point cloud, generates an axis-aligned bounding box (AABB), and estimates the strawberry weight based on the bounding box and placement plane as constraints. Through comparison with different methods, we achieved a maximum improvement of 20.95% in prediction accuracy, demonstrating that our method provides the best estimation accuracy.