User Story: Data Collection

[teman67]

As a Developer I need to collect images to train the machine-learning (ML) model so a User can use the ML model for plant disease prediction.

• Compile a diverse dataset of high-quality images depicting healthy apple leaves and leaves infected with powdery mildew or rust.
• Annotate the images with corresponding labels indicating the presence of powdery mildew, rust, or their absence (healthy).
• Preprocess the image data to standardize resolution, color balance, and orientation for consistent model training.
• Split the dataset into training, validation, and testing subsets to facilitate model evaluation and validation.
• Augment the image dataset by applying transformations such as rotation, scaling, and cropping to increase model robustness and generalization.
• Select and implement appropriate deep learning architectures or machine learning algorithms for image classification tasks.
• Train the ML model using the annotated image dataset and evaluate its performance using validation metrics such as accuracy, precision, recall, and F1 score.
• Fine-tune the model parameters and hyperparameters to optimize performance and minimize overfitting.
• Validate the trained model using the testing subset to assess its generalization capability and real-world performance.
• Document the model training process, including dataset preparation, model architecture, training parameters, and evaluation results, for reproducibility and future reference.
• Continuously update and refine the ML model based on feedback, new data, and emerging research findings to improve prediction accuracy and usability.