ShikhaMaurya212402/Plant-Disease-Classification-Model

AI Summary: This issue proposes enhancing the Streamlit UI for a plant disease detection application. The main goal is to enable users to upload custom images for classification using `st.file_uploader()`, thereby improving interactivity and real-world usability, and displaying real-time prediction results.

AI Summary: Improve the accuracy of a plant disease classification model by implementing and fine-tuning pretrained models like EfficientNetV2 and ResNet50/101. The goal is to achieve at least 95% accuracy, while maintaining or improving inference speed. This involves developing a robust training pipeline, evaluating performance using various metrics (accuracy, precision, recall, F1-score, confusion matrix), and integrating the improved model into an existing web application.

AI Summary: The task involves improving the accuracy of a pre-existing plant disease classification model built using TensorFlow and trained on the PlantVillage dataset. This requires understanding the existing model architecture, experimenting with different hyperparameters, potentially exploring different model architectures or data augmentation techniques, and evaluating the results. The goal is to increase the model's accuracy beyond the current 92%.