Feature extraction in sensor plant disease datasets using reformed membership functions independent of class variables

Key Points

• Improved feature extraction techniques significantly enhance plant disease detection accuracy.
• A notable use of reformed membership functions allows for more effective data classification.
• Using sensor data across various conditions can create a robust dataset for machine learning applications.
• These findings support the potential for more accurate and reliable plant disease management solutions.