# Identification of fresh leaves of Anji White Tea: S-YOLOv10-ASI algorithm fusing asymptotic feature pyra-mid network

## TL;DR

This paper introduces a new algorithm, S-YOLOv10-ASI, to improve robot identification and harvesting of Anji White Tea leaves.

## Contribution

The novel S-YOLOv10-ASI algorithm integrates a slice-assisted super-reasoning technique and a Progressive Feature Pyramid Network for improved tea leaf detection.

## Key findings

- S-YOLOv10-ASI reduces Bounding Box Regression Loss by over 30% in the training set.
- Precision, Recall, and mAP increase by 7.1%, 6.69%, and 6.78% respectively compared to YOLOv10.
- The algorithm improves AP values for specific leaf types by 6.10% to 8.28%.

## Abstract

This study proposes the S-YOLOv10-ASI algorithm to improve the accuracy of tea identification and harvesting by robots, integrating a slice-assisted super-reasoning technique. The algorithm improves the partial structure of the YOLOv10 network through space-to-depth convolution. The Progressive Feature Pyramid Network minimizes information loss during multi-stage transmission, enhances the saliency of key layers, resolves conflicts between objects, and improves the fusion of non-adjacent layers. Intersection over Union (IoU) is used to optimize the loss function calculation. The slice-assisted super-reasoning algorithm is integrated to improve the recognition ability of YOLOv10 network for long-distance and small-target tea. The experimental results demonstrate that when compared to YOLOv10, S-YOLOv10-ASI shows significant improvements across various metrics. Specifically, Bounding Box Regression Loss decreases by over 30% in the training set, while Classification Loss and Bounding Box Regression Loss drop by more than 60% in the validation set. Additionally, Distribution Focal Loss reduces by approximately 10%. Furthermore, Precision, Recall, and mAP have all increased by 7.1%, 6.69%, and 6.78% respectively. Moreover, the AP values for single bud, one bud and one leaf, and one bud and two leaves have seen improvements of 6.10%, 7.99%, and 8.28% respectively. The improved model effectively addresses challenges such as long-distance detection, small targets, and low resolution. It also offers high precision and recall, laying the foundation for the development of an Anji White Tea picking robot.