# Research on grape leaf disease recognition method based on improved YOLOv8n model

## TL;DR

This paper introduces an improved YOLOv8n model for accurately and quickly identifying grape leaf diseases, which can help in precision agriculture.

## Contribution

The study proposes an enhanced YOLOv8n model with architectural modifications and a new loss function for grape leaf disease recognition.

## Key findings

- The improved YOLOv8n model achieved 97.3% accuracy with a model size of 3.53MB and 228.55 FPS processing speed.
- When deployed on a spraying device, the model maintained 89.3% accuracy and 5.18 seconds average processing time.
- The model outperformed YOLOv3-tiny, YOLOv5n, and YOLOv6n in average accuracy.

## Abstract

Grape leaf disease recognition models face challenges such as large model sizes and a lack of classification for various disease types. This study proposes an enhanced grape leaf disease recognition model using an improved version of YOLOv8n, addressing these limitations. To improve performance, several modifications were made to the original YOLOv8n architecture. First, the G-bneck module was introduced into the backbone network to replace the ConvModule, enhancing feature extraction. Simultaneously, the simSPPF module was adopted to replace the SPPF, improving computation speed while preserving feature extraction capabilities. Next, the UIB module was incorporated into both the backbone and neck networks, replacing the Bottleneck module in C2f. This modification resulted in the C2F-UIB module, which reduced parameter size and computational load by eliminating the skip connection. Additionally, the LInner-CIoU loss function was introduced to replace the traditional LCIoU loss in the head network. To accelerate inference and handle irregular, missing, or occluded images, partial convolution and convolution parameter sharing were integrated into the detection head.Experimental results demonstrated that the proposed model outperforms other models, including YOLOv3-tiny, YOLOv5n, and YOLOv6n, in terms of average accuracy. The improved YOLOv8n model achieved an accuracy of 97.3%, with a model size of 3.53MB and a processing speed of 228.55 frames per second (FPS). When deployed to a spraying device, the model maintained an average accuracy of 89.3% and an average processing time of 5.18 seconds. This study successfully addresses the challenges of grape leaf disease recognition by improving model accuracy, size, and inference speed. The proposed model enables accurate and rapid identification of grape leaf diseases in natural environments, offering significant potential for precision agriculture, particularly in the development of effective grape disease management and control technologies.