# Enhanced multiscale plant disease detection with the PYOLO model innovations

## TL;DR

This paper introduces PYOLO, a new model for detecting plant diseases that improves accuracy by enhancing feature fusion and attention mechanisms.

## Contribution

The novel PYOLO model introduces a weighted BiFPN, dynamic convolutional kernel resizing, and an MHC2f mechanism for better multiscale disease detection.

## Key findings

- PYOLO achieves a 4.1% increase in mAP compared to YOLOv8n.
- The model improves feature fusion and attention for complex plant disease scenarios.
- Experiments confirm PYOLO's superiority in multiscale plant disease detection.

## Abstract

Timely detection of plant diseases is crucial for agricultural safety, product quality, and environmental protection. However, plant disease detection faces several challenges, including the diversity of plant disease scenarios and complex backgrounds. To address these issues, we propose a plant disease detection model named PYOLO. Firstly, the model enhances feature fusion capabilities by optimizing the PAN structure, introducing a weighted bidirectional feature pyramid network (BiFPN), and repeatedly fusing top and bottom scale features. Additionally, the model’s ability to focus on different parts of the image is improved by redesigning the EC2f structure and dynamically adjusting the convolutional kernel size to better capture features at various scales. Finally, the MHC2f mechanism is designed to enhance the model’s ability to perceive complex backgrounds and targets at different scales by utilizing its self-attention mechanism for parallel processing. Experiments demonstrate that the model’s mAP value increases by 4.1% compared to YOLOv8n, confirming its superiority in plant disease detection.