A quality grade classification method for fresh tea leaves based on an improved YOLOv8x-SPPCSPC-CBAM model.

In light of the prevalent issues concerning the mechanical grading of fresh tea leaves, characterized by high damage rates and poor accuracy, as well as the limited grading precision through the integration of machine vision and machine learning (ML) algorithms, this study presents an innovative approach for classifying the quality grade of fresh tea leaves. This approach leverages an integration of image recognition and deep learning (DL) algorithm to accurately classify tea leaves' grades by identifying distinct bud and leaf combinations. The method begins by acquiring separate images of orderly scattered and randomly stacked fresh tea leaves.

Point cloud registration method for maize plants based on conical surface fitting-ICP.

Reconstructing three-dimensional (3D) point cloud model of maize plants can provide reliable data for its growth observation and agricultural machinery research. The existing data collection systems and registration methods have low collection efficiency and poor registration accuracy. A point cloud registration method for maize plants based on conical surface fitting-iterative closest point (ICP) with automatic point cloud collection platform was proposed in this paper.