{
"cells": [
{
"cell_type": "markdown",
"id": "6747e964-0a54-4d94-ad2a-17b1fd94fd31",
"metadata": {},
"source": [
"# Plant Leave Dataset"
]
},
{
"cell_type": "markdown",
"id": "9126454a-e001-40e6-a361-a1785bc237b6",
"metadata": {},
"source": [
"**First, Restructure the Dataset & Split Into Folders**\n",
"* Training (70%) → Used to train the CNN model.\n",
"* Validation (15%) → Used to tune hyperparameters & avoid overfitting.\n",
"* Testing (15%) → Used for final performance evaluation."
]
},
{
"cell_type": "markdown",
"id": "0315ba31-6518-48e9-823d-52617f8b6bd5",
"metadata": {},
"source": [
"The dataset from **https://data.mendeley.com/datasets/hb74ynkjcn/1** are pictures in jpg format and are in the following structure. After downloading the dataset, you will unzip it and reorganise it, automatically. Then, you may split it into Training, Validation, and Testing. \n",
"\n",
"**Original Dataset Structure**\n",
"\n",
"Plant_Leaves_Dataset/\n",
"
│── Lemon(P10)/\n",
"
│ │── diseased/\n",
"
│ │ ├── img1.jpg\n",
"
│ │ ├── img2.jpg\n",
"
│ │── healthy/\n",
"
│ ├── img1.jpg\n",
"
│ ├── img2.jpg\n",
"
│\n",
"
│── Mango(P0)/\n",
"
│ │── diseased/\n",
"
│ │ ├── img1.jpg\n",
"
│ │ ├── img2.jpg\n",
"
│ │── healthy/\n",
"
│ ├── img1.jpg\n",
"
│ ├── img2.jpg\n",
"
│\n",
"
...\n",
"\n",
"**Target Structure**\n",
"\n",
"Plant_Leaves_Dataset/processed_plant_dataset/\n",
"
│── Test/\n",
"
│ ├── lemon_1_Diseased.jpg\n",
"
│ ├── mango_2_Healthy.jpg\n",
"
│ ├── ...\n",
"
│── Train/\n",
"
│ ├── lemon_3_Healthy.jpg\n",
"
│ ├── mango_4_Healthy.jpg\n",
"
│ ├── ...\n",
"
│── Val/\n",
"
│ ├── lemon_3_Diseased.jpg\n",
"
│ ├── mango_4_Diseased.jpg\n",
"
│ ├── ..."
]
},
{
"cell_type": "markdown",
"id": "5e2328ba-3065-45b0-b73e-8e0a300cde4b",
"metadata": {},
"source": [
"- ### The following script will reorganise the downloaded dataset automatically.\n",
"\n",
"- ### Change the path to reflect the location of your files \n",
"\n",
"- ### Note you only need to run this once after downloading,"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "40d75351-094c-4ded-a0e1-26ff3ae8f5dc",
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import shutil\n",
"import random\n",
"\n",
"# Original dataset directory\n",
"original_dir = \"C:\\\\Users\\\\aliyu\\\\Downloads\\\\Plant_Leaves_Dataset3\" # <<< --- note the double \\\\ in the path. This can be replaced by a / (forward slash)\n",
"structured_dir = \"C:\\\\Users\\\\aliyu\\\\Downloads\\\\Plant_Leaves_Dataset3\\\\processed_plant_dataset3\"\n",
"\n",
"# Directories for training, validation, and testing\n",
"train_dir = os.path.join(structured_dir, \"train\")\n",
"val_dir = os.path.join(structured_dir, \"val\")\n",
"test_dir = os.path.join(structured_dir, \"test\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "b894aeee-fb2a-40d1-a90e-11fa3c7aac37",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"✅ Dataset restructuring and splitting complete!\n"
]
}
],
"source": [
"# Ensure directories exist\n",
"for directory in [train_dir, val_dir, test_dir]:\n",
" os.makedirs(directory, exist_ok=True)\n",
"\n",
"# Splitting ratios\n",
"train_ratio = 0.7\n",
"val_ratio = 0.15\n",
"test_ratio = 0.15\n",
"\n",
"# Process each plant folder\n",
"for plant in os.listdir(original_dir):\n",
" plant_path = os.path.join(original_dir, plant)\n",
"\n",
" if os.path.isdir(plant_path):\n",
" for condition in [\"healthy\", \"diseased\"]:\n",
" condition_path = os.path.join(plant_path, condition)\n",
"\n",
" if os.path.isdir(condition_path):\n",
" # Define new class name\n",
" class_name = f\"{plant}_{condition.capitalize()}\"\n",
"\n",
" # Create class directories in train, val, and test\n",
" for subset in [train_dir, val_dir, test_dir]:\n",
" os.makedirs(os.path.join(subset, class_name), exist_ok=True)\n",
"\n",
" # Get all image files\n",
" all_images = os.listdir(condition_path)\n",
" random.shuffle(all_images) # Shuffle to ensure randomness\n",
"\n",
" # Split images\n",
" total_images = len(all_images)\n",
" train_split = int(train_ratio * total_images)\n",
" val_split = int(val_ratio * total_images)\n",
"\n",
" train_images = all_images[:train_split]\n",
" val_images = all_images[train_split:train_split + val_split]\n",
" test_images = all_images[train_split + val_split:]\n",
"\n",
" # Function to move images\n",
" def move_images(image_list, dest_folder):\n",
" for img_file in image_list:\n",
" src_path = os.path.join(condition_path, img_file)\n",
" dst_path = os.path.join(dest_folder, class_name, img_file)\n",
" shutil.copy2(src_path, dst_path)\n",
"\n",
" # Move images to respective folders\n",
" move_images(train_images, train_dir)\n",
" move_images(val_images, val_dir)\n",
" move_images(test_images, test_dir)\n",
"\n",
"print(\"✅ Dataset restructuring and splitting complete!\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "5aa88862-8f2e-4360-99bb-d94f9013761d",
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"from collections import Counter\n",
"\n",
"\n",
"split_dirs = {\n",
" \"Train\": f\"{structured_dir}\\\\train\",\n",
" \"Validation\": f\"{structured_dir}\\\\val\",\n",
" \"Test\": f\"{structured_dir}\\\\test\"\n",
"}\n",
"\n",
"# Count images in each category\n",
"class_counts = {split: Counter() for split in split_dirs}\n",
"\n",
"for split, path in split_dirs.items():\n",
" for class_name in os.listdir(path):\n",
" class_dir = os.path.join(path, class_name)\n",
" if os.path.isdir(class_dir):\n",
" class_counts[split][class_name] = len(os.listdir(class_dir))\n",
"\n",
"# Convert to lists for plotting\n",
"fig, axes = plt.subplots(1, 3, figsize=(18, 5))\n",
"for i, (split, counts) in enumerate(class_counts.items()):\n",
" sns.barplot(x=list(counts.keys()), y=list(counts.values()), ax=axes[i])\n",
" axes[i].set_title(f\"{split} Set Class Distribution\")\n",
" axes[i].set_xlabel(\"Class\")\n",
" axes[i].set_ylabel(\"Image Count\")\n",
" axes[i].tick_params(axis='x', rotation=90)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"id": "fe51c96c-a40b-4546-83ff-a1885f158048",
"metadata": {},
"source": [
"## Load Training, Validation & Testing Data"
]
},
{
"cell_type": "markdown",
"id": "27fb92d4-5c72-4ded-95b2-9547b979c8a7",
"metadata": {},
"source": [
"We now load the dataset with ImageDataGenerator, applying:\n",
"\n",
"- Augmentation for training\n",
"- Rescaling for all sets\n",
"- Batching"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "e1de566c-958e-4024-9cbc-fa5a220764cd",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found 4086 images belonging to 22 classes.\n",
"Found 1222 images belonging to 22 classes.\n",
"Found 1279 images belonging to 22 classes.\n",
"Class indices: {'Alstonia Scholaris (P2)_Diseased': 0, 'Alstonia Scholaris (P2)_Healthy': 1, 'Arjun (P1)_Diseased': 2, 'Arjun (P1)_Healthy': 3, 'Bael (P4)_Diseased': 4, 'Basil (P8)_Healthy': 5, 'Chinar (P11)_Diseased': 6, 'Chinar (P11)_Healthy': 7, 'Gauva (P3)_Diseased': 8, 'Gauva (P3)_Healthy': 9, 'Jamun (P5)_Diseased': 10, 'Jamun (P5)_Healthy': 11, 'Jatropha (P6)_Diseased': 12, 'Jatropha (P6)_Healthy': 13, 'Lemon (P10)_Diseased': 14, 'Lemon (P10)_Healthy': 15, 'Mango (P0)_Diseased': 16, 'Mango (P0)_Healthy': 17, 'Pomegranate (P9)_Diseased': 18, 'Pomegranate (P9)_Healthy': 19, 'Pongamia Pinnata (P7)_Diseased': 20, 'Pongamia Pinnata (P7)_Healthy': 21}\n"
]
}
],
"source": [
"import tensorflow as tf\n",
"from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
"\n",
"# Image parameters\n",
"IMG_SIZE = (128, 128)\n",
"BATCH_SIZE = 32\n",
"\n",
"# Data augmentation & preprocessing\n",
"train_datagen = ImageDataGenerator(\n",
" rescale=1./255,\n",
" rotation_range=30,\n",
" width_shift_range=0.2,\n",
" height_shift_range=0.2,\n",
" shear_range=0.2,\n",
" zoom_range=0.2,\n",
" horizontal_flip=True\n",
")\n",
"\n",
"# Only rescaling for validation & test sets\n",
"val_test_datagen = ImageDataGenerator(rescale=1./255)\n",
"\n",
"# Load datasets\n",
"train_generator = train_datagen.flow_from_directory(\n",
" train_dir, target_size=IMG_SIZE, batch_size=BATCH_SIZE, class_mode=\"categorical\"\n",
")\n",
"\n",
"val_generator = val_test_datagen.flow_from_directory(\n",
" val_dir, target_size=IMG_SIZE, batch_size=BATCH_SIZE, class_mode=\"categorical\"\n",
")\n",
"\n",
"test_generator = val_test_datagen.flow_from_directory(\n",
" test_dir, target_size=IMG_SIZE, batch_size=BATCH_SIZE, class_mode=\"categorical\", shuffle=False\n",
")\n",
"\n",
"# Print class labels\n",
"print(\"Class indices:\", train_generator.class_indices)\n"
]
},
{
"cell_type": "markdown",
"id": "d080f5c1-da4a-467c-98fc-97fa60a3093c",
"metadata": {},
"source": [
"Step 3: Build & Train CNN Model\n",
"This updated model includes:\n",
"\n",
"Dropout for regularization\n",
"Callbacks for early stopping & checkpoints\n",
"Training on the training set\n",
"Validating on the validation set"
]
},
{
"cell_type": "markdown",
"id": "3f4b1651-682c-4dac-a440-bbb4844b2c32",
"metadata": {},
"source": [
"\n",
"## Use the data generators when training\n",
"When training the model use the ImageDataGenerator you defined
\n",
"(**train_generator, val_generator, test_generator** for Training, Validation, and Testing, respectively)\n",
"\n",
"**For example:**\n",
"\n",
" model.fit(train_generator, validation_data=val_generator, ...\n",
" \n",
" model.evaluate(test_generator)\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "046d0779-c102-4363-93ef-d1ff822ba774",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "bc939190-75b8-468e-aa53-3a99e8dff967",
"metadata": {},
"outputs": [],
"source": []
}
],
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"display_name": "Python 3 (ipykernel)",
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