This project involves the development of a deep learning model for classifying rice images. The following steps have been undertaken as part of the project:

• Images from the [Rice_Image_Dataset] directory were loaded and class labels were extracted.
• The images were resized to 64x64 dimensions and pixel values were normalized.

• Image and label data were split into training and test sets using the [train_test_split] function.
• Labels were encoded using [LabelEncoder] and converted to categorical format using the [to_categorical] function.

• A deep learning model was built using the [Sequential] API.
• The model included various layers such as [Conv2D], [MaxPooling2D], [Flatten], [BatchNormalization], [Dropout], and [Dense].
• The model was compiled with the [adam] optimization algorithm and [categorical_crossentropy] loss function.

• The model was trained on the training data and evaluated with validation data.
• Hyperparameter optimization was performed using [kerastuner] to select the best model.
• Early stopping ([EarlyStopping]) was used to prevent overfitting of the model.

• The best model was evaluated on the test data and the accuracy value was calculated.
• The model was saved as rice_model.h5.

• The saved model was loaded and predictions were made on a new image.
• The predicted class and the actual class were displayed on the image.

• The best model achieved an accuracy of 99.65% on the training data.
• The model's accuracy on the test data was measured at 99%.

• The model's loss value was calculated as 0.02 on the training data and 0.20 on the test data.
• The classification report and confusion matrix of the model are as follows:

Classification Report:
           precision    recall  f1-score 

  Arborio       1.00      1.00      1.00      
  Basmati       1.00      1.00      1.00      
   Ipsala       1.00      1.00      1.00      
  Jasmine       1.00      0.99      1.00      
Karacadag       1.00      1.00      1.00      

Confusion Matrix:
    [2989,    0,    0,    2,    6]
    [   0, 2990,    0,    5,    0]
    [   0,    0, 3083,    0,    0]
    [   5,   11,    0, 2979,    1]
    [   9,    0,    0,    0, 2920]

This project demonstrates the development of an effective deep learning model for classifying rice images with high accuracy and low loss values.