Classification of leaves based on healthiness & nutrition efficiency using Machine Learning

Step 1: Collected the leaf images in real-time dynamicaaly using the mobile camera for training and testing
Step 2: Train-Test split ratio of 8:2 (80% for training & 20% for testing)
Step 3: Define the CNN model architecture
Step 4: Fit the training datasets in the model and run it for 10 epochs
Step 5: Evaluate the model with the testing dataset
Step 6: Finetune the model for achieving higher accuracy
Step 7: Deploy the model