# A Real-Time Cotton Boll Disease Detection Model Based on Enhanced YOLOv11n
## 1 Introduction  
Cotton is a vital economic crop in China, playing a crucial role in national defense, pharmaceuticals, and industrial applications [1]. Cotton poll disease is a complex disorder caused by multiple pathogens, including cotton poll blight, red rot, black fruit disease, soft rot, and Aspergillus infection. Infected bolls exhibit poor fiber extrusion, distorted shells that fail to open properly, and frequent withering or rotting, which severely compromises cotton quality and yield [2]. Currently, disease detection relies heavily on manual inspection, a method that is time-consuming, labor-intensive, prone to subjective error, and hinders timely prevention and control. Consequently, developing automated detection technology for cotton poll diseases holds significant importance for safeguarding the cotton industry's sustainable growth and enhancing productivity. However, these diseases typically manifest during the middle to late growth stages, primarily affecting bolls on the middle and lower sections of the plants. These areas suffer from poor lighting conditions and are disturbed by withered leaves. Coupled with natural environmental factors like occlusions and shadows, the identification of diseases becomes even more challenging.  
Efficient and accurate recognition of crop disease symptoms using image recognition algorithms can provide agricultural producers with timely and accurate disease diagnosis information. [PERSON] et al. [3] employed an improved YOLOv5-CBM to detect early-stage tea leaf spots in natural environments. Compared to the original YOLOv5s model, the improved model increased the average precision of early tea powdery mildew and early tea ring spot disease detection by 1.9% and 0.9%, respectively, achieving an mAP of 97.3% for different diseases. [PERSON] et al. [4] used three different deep learning models, CNN, InceptionV3, and ResNet-52, for cotton leaf disease detection, with detection accuracies of 99.057%, 97.170%, and 98.113%, respectively. [PERSON] et al. [5] proposed the YOLOv8-FECA model to address the problem of false negatives and false positives in small target detection for wheat fusarium head blight spores. By adding a small target detection layer, constructing a focus attention mechanism (FECA), and combining Wise-IoU with DFL loss function optimization, mAP improved by 4.3% compared to the original YOLOv8. [PERSON] et al. [6] proposed a lightweight network based on YOLOv8n, achieving 98.5% mAP and 97.5% precision through StarBlock reconstruction, the introduction of a hybrid local channel attention mechanism, optimization of detection heads, and channel pruning strategies, significantly improving model efficiency and detection accuracy. [PERSON] et al. [7] proposed the multi-scale dense network MD-YOLO, which introduced DenseNet blocks and an adaptive attention module (AAM) to enhance feature extraction and small target detection capabilities, achieving a recognition accuracy of 86.2%.  
To improve the accuracy of cotton poll disease detection in complex field environments, this study proposes a YOLOv11n-ECS model suitable for mobile deployment. The proposed model demonstrates significant performance improvements over the baseline YOLOv11n. The main contributions of this study are as follows: (1) A cotton poll disease dataset was constructed, comprising images captured under varying light intensities and shooting angles to accurately reflect real-world production conditions. (2) The EMA mechanism was integrated to enhance distinctive feature representation. It adaptively weights channel-wise features, suppressing background interference while strengthening disease features under suboptimal lighting, thereby improving detection accuracy. (3) The loss function was replaced with Shape-IoU to optimize bounding box localization precision. (4) A C3k2_PConv module was designed to replace the standard C3k2 block. This module employs partial convolution operations to reduce computational redundancy while enhancing the model's capability to handle irregular regions.