A cell P system with membrane division and dissolution rules for soybean leaf disease recognition.

Area of Science:

• Agricultural Science
• Computer Science
• Biotechnology

Background:

• Accurate soybean leaf disease identification is vital for crop yield and management.
• Existing methods may lack efficiency or adaptability for real-world agricultural settings.

Purpose of the Study:

• To develop and evaluate a novel DDC-P system for rapid and accurate soybean leaf disease identification.
• To enhance disease-specific information extraction while minimizing environmental interference.

Main Methods:

• Proposed a cell P system with membrane division and dissolution rules (DDC-P system).
• Incorporated Efficient Feature Attention (EFA) and Sandglass structure with Efficient Feature Attention (SGEFA) modules.
• Utilized a fuzzy controller for adaptive membrane management within the SGEFA structure.

Main Results:

• Achieved 98.43% recognition rate and 0.9874 F1 score on a homemade dataset.
• Attained 94.40% accuracy and 0.9425 F1 score on a public dataset.
• Demonstrated a small model size (1.41 MB) and fast edge device recognition (0.042857 s, 23.3 FPS).

Conclusions:

• The DDC-P system exhibits excellent recognition accuracy and generalization capabilities.
• The system's efficiency and small footprint make it suitable for edge device deployment.
• This approach offers a revolutionary solution for soybean leaf disease management in agricultural practices.