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Objectives to cover:
Introduction: This study explores image-based techniques for detecting and classifying diseases in Golden Nutri cereal crops.
Objectives of the Study: The aim is to develop an efficient and accurate system for early disease detection using image segmentation.
Importance of Disease Detection in Golden Nutri Cereals: Early diagnosis is crucial for minimizing yield loss and ensuring healthy crop production.
Overview of Phytopathological Imaging Techniques: Phytopathological images provide vital visual data for identifying crop disease symptoms.
Image Preprocessing and Segmentation Methods: Preprocessing enhances image quality, while segmentation isolates infected regions for analysis.
Feature Extraction Techniques: Key features such as color, texture, and shape are extracted to distinguish between healthy and diseased areas.
Classification Models and Algorithms: Machine learning algorithms classify the extracted features into specific disease categories.
Model Training and Validation: The models are trained on labeled datasets and validated to ensure accuracy and reliability.
Conclusion: The system demonstrates potential for improving crop protection through timely and automated disease diagnosis.
Reference: APA style