C2PSA-Enhanced YOLOv11 Architecture: A Novel Approach for Small Target Detection in Cotton Disease Diagnosis

TL;DR

This paper enhances YOLOv11 with C2PSA and other techniques to improve small cotton disease target detection, achieving higher accuracy and real-time performance for agricultural monitoring.

Contribution

The study introduces C2PSA module, dynamic category weighting, and improved data augmentation to significantly boost small target detection in cotton disease diagnosis.

Findings

mAP50 improved by 8.0% to 0.820

mAP50-95 improved by 10.5% to 0.705

real-time inference at 158 FPS

Abstract

This study presents a deep learning-based optimization of YOLOv11 for cotton disease detection, developing an intelligent monitoring system. Three key challenges are addressed: (1) low precision in early spot detection (35% leakage rate for sub-5mm2 spots), (2) performance degradation in field conditions (25% accuracy drop), and (3) high error rates (34.7%) in multi-disease scenarios. The proposed solutions include: C2PSA module for enhanced small-target feature extraction; Dynamic category weighting to handle sample imbalance; Improved data augmentation via Mosaic-MixUp scaling. Experimental results on a 4,078-image dataset show: mAP50: 0.820 (+8.0% improvement); mAP50-95: 0.705 (+10.5% improvement); Inference speed: 158 FPS. The mobile-deployed system enables real-time disease monitoring and precision treatment in agricultural applications.