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README.md

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+# MobileNetV3 Model for Plant Classification
+
+## Model Description
+This model is a fine-tuned **MobileNetV3Small** trained to classify different types of plants. It was trained using transfer learning on a dataset obtained from Kaggle.
+
+- **Base Model:** MobileNetV3Small (pretrained on ImageNet)
+- **Dataset:** [Plants Classification Dataset](https://www.kaggle.com/datasets/marquis03/plants-classification)
+- **Accuracy:** 88%
+- **Fine-Tuning:** Last 20 layers of MobileNetV3Small were unfrozen for fine-tuning.
+
+## Dataset
+The dataset consists of images of various plant species, divided into training and validation sets:
+- **Training Images:** Preprocessed with data augmentation (rotation, shifting, zoom, brightness adjustment, etc.)
+- **Validation Images:** Rescaled without augmentation
+
+## Model Training
+The model was trained using **TensorFlow** and **Keras**, with categorical crossentropy loss and the Adam optimizer. The training process involved:
+
+1. **Data Augmentation** using `ImageDataGenerator`.
+2. **Transfer Learning** by leveraging MobileNetV3Small's pretrained weights.
+3. **Fine-Tuning** of the last 20 layers.
+4. **Learning Rate Scheduling** using `ReduceLROnPlateau`.
+5. **Evaluation** using classification reports and a confusion matrix.
+6. **Exporting the Model** as a `.tflite` file for mobile deployment.
+
+## Model Performance
+- **Training Accuracy:** 88%
+- **Validation Accuracy:** 88%
+- **Loss Function:** Categorical Crossentropy
+- **Optimizer:** Adam (learning rate = 0.0001)
+
+## Usage
+To use the model for inference, load it using TensorFlow:
+
+```python
+import tensorflow as tf
+from tensorflow.keras.models import load_model
+
+# Load the model
+model = load_model("mobilenetv3_tanaman.h5")
+
+# Preprocess an input image
+import numpy as np
+from tensorflow.keras.preprocessing import image
+
+img_path = "path_to_image.jpg"
+img = image.load_img(img_path, target_size=(224, 224))
+img_array = image.img_to_array(img) / 255.0
+img_array = np.expand_dims(img_array, axis=0)
+
+# Make a prediction
+predictions = model.predict(img_array)
+class_idx = np.argmax(predictions)
+print(f"Predicted class: {class_idx}")
+```
+
+## Deployment
+This model can be deployed for:
+- Mobile applications (converted to `.tflite` for TensorFlow Lite compatibility)
+- Web-based applications
+- Embedded AI systems for plant classification
+
+## License
+This model is provided for research and educational purposes. Please ensure to cite the original dataset from Kaggle if used in any publication.
+
+## Citation
+If you use this model, please cite:
+```
+@misc{PlantClassification2024,
+  title={MobileNetV3 Model for Plant Classification},
+  author={Ade Maulana},
+  year={2024},
+  url={https://huggingface.co/your-huggingface-repo}
+}
+```
+