# An improved YOLOv8n model for in-field detection of pests and diseases in pakchoi

## TL;DR

This paper introduces an improved lightweight model for detecting pests and diseases in pakchoi, achieving better accuracy and efficiency for field use.

## Contribution

The novel YOLOv8n-DBW model integrates enhanced modules and loss functions for improved pest and disease detection in pakchoi.

## Key findings

- The YOLOv8n-DBW model reduces parameters and model size by 33.3% and 31.8%, respectively.
- It improves precision and mean average precision (mAP) by 5.0% and 7.5% compared to the baseline model.
- The model outperforms mainstream object detection algorithms for pakchoi pest and disease detection.

## Abstract

As an important leafy vegetable, pakchoi (Brassica chinensis L.) frequently suffers from pests and diseases in field environments. These symptoms are often localized on specific leaf regions, resulting in substantial losses in yield and quality. To achieve efficient and accurate detection of pakchoi pests and diseases, this study proposes an improved lightweight object detection model, termed YOLOv8n-DBW, based on the YOLOv8n framework. First, the original C2f module in the backbone network is replaced with a novel C2f-PE module, which integrates Partial Convolution (PConv) and an Efficient Multi-Scale Attention (EMA) mechanism to enhance high-level semantic feature extraction and multi-scale information fusion. Second, a Weighted Bidirectional Feature Pyramid Network (BiFPN) is introduced into the neck network to strengthen multi-scale feature fusion while improving model generalization and lightweight performance. Finally, the original CIoU loss in the regression branch is replaced with the Wise-IoU (Weighted Interpolation of Sequential Evidence for Intersection over Union) bounding box loss function, which improves bounding box regression accuracy and significantly enhances the detection of small and irregular pest and disease targets. Experimental results on a field-collected pakchoi pest and disease dataset demonstrate that the proposed YOLOv8n-DBW model reduces the number of parameters and model size by 33.3% and 31.8%, respectively, while improving precision and mean average precision (mAP) by 5.0% and 7.5% compared with the baseline YOLOv8n model. Overall, the proposed method outperforms several mainstream object detection algorithms and provides an efficient and accurate solution for real-time pakchoi pest and disease detection, showing strong potential for deployment on embedded systems and mobile devices.