Automated Technology for Strawberry Size Measurement and Weight Prediction Using AI

Abstract

In this study, we propose an automated system for measuring the size of strawberries and predicting their weight using AI technology. The system combines computer vision techniques with LiDAR sensor data to accurately estimate the dimensions of strawberries and infer their weight. By integrating deep learning models, such as HRNet for keypoint detection, and leveraging the capabilities of LiDAR sensors, we minimize human intervention and achieve precise size measurement. The relative errors for the width and height of the strawberries are 3.71% and 5.42%, respectively, with the width exhibiting a lower error rate. The standard deviation for the width and height of the strawberries are 0.19% and 0.24%, this indicates that the individual strawberries had very low error rates in terms of their measurements for the width and height. Weight prediction was performed through regression analysis with width and height estimation. Experimental results demonstrate that our approach enables accurate weight prediction with a relative error of 10.3%. This automated technology holds great potential for strawberry harvesting and classification tasks, facilitating the automation of these processes.

Keywords

• Deep learning
• strawberry size
• LiDAR
• point cloud