Cotton Leaf Disease Detection Using LLM-Synthetic Data and DEMM-YOLO Model

Lijun Gao, Tiantian Ran, Hua Zou and Huanhuan Wu ()
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Lijun Gao: College of Information Engineering, Tarim University, Alar 843300, China
Tiantian Ran: College of Life Science and Technology, Tarim University, Alar 843300, China
Hua Zou: School of Computer Science, Wuhan University, Wuhan 430072, China
Huanhuan Wu: College of Information Engineering, Tarim University, Alar 843300, China

Agriculture, 2025, vol. 15, issue 15, 1-24

Abstract: Cotton leaf disease detection is essential for accurate identification and timely management of diseases. It plays a crucial role in enhancing cotton yield and quality while promoting the advancement of intelligent agriculture and efficient crop harvesting. This study proposes a novel method for detecting cotton leaf diseases based on large language model (LLM)-generated image synthesis and an improved DEMM-YOLO model, which is enhanced from the YOLOv11 model. To address the issue of insufficient sample data for certain disease categories, we utilize OpenAI’s DALL-E image generation model to synthesize images for low-frequency diseases, which effectively improves the model’s recognition ability and generalization performance for underrepresented classes. To tackle the challenges of large-scale variations and irregular lesion distribution, we design a multi-scale feature aggregation module (MFAM). This module integrates multi-scale semantic information through a lightweight, multi-branch convolutional structure, enhancing the model’s ability to detect small-scale lesions. To further overcome the receptive field limitations of traditional convolution, we propose incorporating a deformable attention transformer (DAT) into the C2PSA module. This allows the model to flexibly focus on lesion areas amidst complex backgrounds, improving feature extraction and robustness. Moreover, we introduce an enhanced efficient multi-dimensional attention mechanism (EEMA), which leverages feature grouping, multi-scale parallel learning, and cross-space interactive learning strategies to further boost the model’s feature expression capabilities. Lastly, we replace the traditional regression loss with the MPDIoU loss function, enhancing bounding box accuracy and accelerating model convergence. Experimental results demonstrate that the proposed DEMM-YOLO model achieves 94.8% precision, 93.1% recall, and 96.7% mAP@0.5 in cotton leaf disease detection, highlighting its strong performance and promising potential for real-world agricultural applications.

Keywords: cotton leaf disease detection; intelligent agriculture; crop harvesting; LLM; YOLOv11; MFAM; EEMA; MPDIoU loss function (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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