wheatDisDet.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fa5147b9",
   "metadata": {},
   "source": [
    "# CNN model to detect wheat diseases"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "831cee03",
   "metadata": {},
   "source": [
    "## Pre-requisites"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f3c5ba1",
   "metadata": {},
   "source": [
    "### Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "393f01f7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\abdal\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import random\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n",
    "from keras.preprocessing.image import ImageDataGenerator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86efbaa2",
   "metadata": {},
   "source": [
    "### Setting the paths of the dataset and images to be classified"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9859ff6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_path = 'cropDiseaseDataset'\n",
    "klass_dir = 'testCDD'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c890a84",
   "metadata": {},
   "source": [
    "### Setting the parameters "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "99f63a16",
   "metadata": {},
   "outputs": [],
   "source": [
    "img_size = 64\n",
    "num_classes = 4\n",
    "batch_size = 32"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c39fdada",
   "metadata": {},
   "source": [
    "### Creating the image and the labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "708e4bf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "def load_dataset():\n",
    "    images = []\n",
    "    labels = []\n",
    "    classes = os.listdir(dataset_path)\n",
    "    label_binarizer = LabelBinarizer()\n",
    "    for class_name in classes:\n",
    "        class_path = os.path.join(dataset_path, class_name)\n",
    "        if os.path.isdir(class_path):  # Check if the current item is a directory\n",
    "            for image_name in os.listdir(class_path):\n",
    "                image_path = os.path.join(class_path, image_name)\n",
    "                try:\n",
    "                    image = cv2.imread(image_path)\n",
    "                    if image is not None:  # Check if the image was loaded successfully\n",
    "                        image = cv2.resize(image, (img_size, img_size))\n",
    "                        images.append(image)\n",
    "                        labels.append(class_name)\n",
    "                    else:\n",
    "                        print(f\"Skipping invalid image: {image_path}\")\n",
    "                except Exception as e:\n",
    "                    print(f\"Error loading image: {image_path}\")\n",
    "                    print(str(e))\n",
    "    # Convert labels to unique binary arrays\n",
    "    labels = label_binarizer.fit_transform(labels)\n",
    "    return images, labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55f49164",
   "metadata": {},
   "source": [
    "Images that are not in the correct format are skipped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "92fc6bc5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Skipping invalid image: cropDiseaseDataset\\Wheat_crown_root_rot\\00421.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_crown_root_rot\\00431.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_crown_root_rot\\00511.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_crown_root_rot\\00531.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_crown_root_rot\\00541.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_crown_root_rot\\00571.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_crown_root_rot\\03681.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_crown_root_rot\\07071.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_Leaf_Rust\\07361.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_Loose_Smut\\0121.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_Loose_Smut\\0131.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_Loose_Smut\\0191.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_Loose_Smut\\3181.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_Loose_Smut\\3191.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_Loose_Smut\\3271.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_Loose_Smut\\6261.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_Loose_Smut\\6271.gif\n",
      "Skipping invalid image: cropDiseaseDataset\\Wheat_Loose_Smut\\6351.gif\n"
     ]
    }
   ],
   "source": [
    "images, labels = load_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b448c689",
   "metadata": {},
   "source": [
    "The arrays assigned for the 4 labels are as follows:\n",
    "Crown root rot - 0010, Healthy - 0001, Leaf rust - 1000, Loose smut - 0100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a9e0062a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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     ]
    }
   ],
   "source": [
    "#Crown root rot - 0010, Healthy - 0001, Leaf rust - 1000, Loose smut - 0100\n",
    "for i in labels:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55ddd5e8",
   "metadata": {},
   "source": [
    "### Preprocessing the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "d383e037",
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess_dataset(images, labels):\n",
    "    images = np.array(images)\n",
    "    images = images.astype('float32') / 255.0\n",
    "    labels = np.array(labels)\n",
    "    return images, labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "15963a0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "images, labels = preprocess_dataset(images, labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cb909d4",
   "metadata": {},
   "source": [
    "The labels after preprocessing are stored as a NumPy array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a984c6e0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 1 0]\n",
      " [0 0 1 0]\n",
      " [0 0 1 0]\n",
      " ...\n",
      " [0 1 0 0]\n",
      " [0 1 0 0]\n",
      " [0 1 0 0]]\n"
     ]
    }
   ],
   "source": [
    "print(labels)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0abedca",
   "metadata": {},
   "source": [
    "## Creating the Convolutional Neural Network (CNN)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f8dfca5",
   "metadata": {},
   "source": [
    "### Splitting the data into a training and testing set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8064ad94",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_images, test_images, train_labels, test_labels = train_test_split(images, labels, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "750969e1",
   "metadata": {},
   "source": [
    "### Creating the CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e8cb2ae4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\abdal\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\backend.py:873: The name tf.get_default_graph is deprecated. Please use tf.compat.v1.get_default_graph instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\abdal\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\layers\\pooling\\max_pooling2d.py:161: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(img_size, img_size, 3)))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(Conv2D(128, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D((2, 2)))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dense(num_classes, activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d04ffd5",
   "metadata": {},
   "source": [
    "### Compiling the CNN model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "755fd2a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\abdal\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\optimizers\\__init__.py:309: The name tf.train.Optimizer is deprecated. Please use tf.compat.v1.train.Optimizer instead.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f61ab104",
   "metadata": {},
   "source": [
    "### Augmenting the training data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "96b56853",
   "metadata": {},
   "outputs": [],
   "source": [
    "data_generator = ImageDataGenerator(rotation_range=20, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=True)\n",
    "data_generator.fit(train_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "de918449",
   "metadata": {},
   "source": [
    "### Training the model with the training data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "65857df1",
   "metadata": {},
   "source": [
    "The history variable stores the values of accuracy and loss after each epoch, which are then used to plot the graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "689c4ebd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "WARNING:tensorflow:From C:\\Users\\abdal\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\utils\\tf_utils.py:492: The name tf.ragged.RaggedTensorValue is deprecated. Please use tf.compat.v1.ragged.RaggedTensorValue instead.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\abdal\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\engine\\base_layer_utils.py:384: The name tf.executing_eagerly_outside_functions is deprecated. Please use tf.compat.v1.executing_eagerly_outside_functions instead.\n",
      "\n",
      "84/84 [==============================] - 4s 41ms/step - loss: 1.0815 - accuracy: 0.5120\n",
      "Epoch 2/30\n",
      "84/84 [==============================] - 3s 41ms/step - loss: 0.9975 - accuracy: 0.5876\n",
      "Epoch 3/30\n",
      "84/84 [==============================] - 4s 43ms/step - loss: 0.9330 - accuracy: 0.6109\n",
      "Epoch 4/30\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 0.8941 - accuracy: 0.6316\n",
      "Epoch 5/30\n",
      "84/84 [==============================] - 4s 43ms/step - loss: 0.8668 - accuracy: 0.6523\n",
      "Epoch 6/30\n",
      "84/84 [==============================] - 3s 41ms/step - loss: 0.8480 - accuracy: 0.6632\n",
      "Epoch 7/30\n",
      "84/84 [==============================] - 4s 42ms/step - loss: 0.8699 - accuracy: 0.6447\n",
      "Epoch 8/30\n",
      "84/84 [==============================] - 4s 41ms/step - loss: 0.8542 - accuracy: 0.6474\n",
      "Epoch 9/30\n",
      "84/84 [==============================] - 3s 41ms/step - loss: 0.8186 - accuracy: 0.6602\n",
      "Epoch 10/30\n",
      "84/84 [==============================] - 4s 42ms/step - loss: 0.7747 - accuracy: 0.6902\n",
      "Epoch 11/30\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 0.7495 - accuracy: 0.7083\n",
      "Epoch 12/30\n",
      "84/84 [==============================] - 4s 42ms/step - loss: 0.7381 - accuracy: 0.7165\n",
      "Epoch 13/30\n",
      "84/84 [==============================] - 4s 44ms/step - loss: 0.6930 - accuracy: 0.7192\n",
      "Epoch 14/30\n",
      "84/84 [==============================] - 4s 42ms/step - loss: 0.6777 - accuracy: 0.7312\n",
      "Epoch 15/30\n",
      "84/84 [==============================] - 4s 43ms/step - loss: 0.6562 - accuracy: 0.7436\n",
      "Epoch 16/30\n",
      "84/84 [==============================] - 4s 42ms/step - loss: 0.6199 - accuracy: 0.7549\n",
      "Epoch 17/30\n",
      "84/84 [==============================] - 3s 41ms/step - loss: 0.6070 - accuracy: 0.7609\n",
      "Epoch 18/30\n",
      "84/84 [==============================] - 4s 42ms/step - loss: 0.5870 - accuracy: 0.7711\n",
      "Epoch 19/30\n",
      "84/84 [==============================] - 4s 42ms/step - loss: 0.5386 - accuracy: 0.7842\n",
      "Epoch 20/30\n",
      "84/84 [==============================] - 4s 41ms/step - loss: 0.5406 - accuracy: 0.7808\n",
      "Epoch 21/30\n",
      "84/84 [==============================] - 3s 41ms/step - loss: 0.5349 - accuracy: 0.7891\n",
      "Epoch 22/30\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 0.5492 - accuracy: 0.7895\n",
      "Epoch 23/30\n",
      "84/84 [==============================] - 5s 54ms/step - loss: 0.4807 - accuracy: 0.8113\n",
      "Epoch 24/30\n",
      "84/84 [==============================] - 4s 43ms/step - loss: 0.4632 - accuracy: 0.8293\n",
      "Epoch 25/30\n",
      "84/84 [==============================] - 4s 45ms/step - loss: 0.4327 - accuracy: 0.8361\n",
      "Epoch 26/30\n",
      "84/84 [==============================] - 4s 42ms/step - loss: 0.4049 - accuracy: 0.8444\n",
      "Epoch 27/30\n",
      "84/84 [==============================] - 4s 42ms/step - loss: 0.3848 - accuracy: 0.8541\n",
      "Epoch 28/30\n",
      "84/84 [==============================] - 4s 42ms/step - loss: 0.4066 - accuracy: 0.8417\n",
      "Epoch 29/30\n",
      "84/84 [==============================] - 4s 42ms/step - loss: 0.3761 - accuracy: 0.8564\n",
      "Epoch 30/30\n",
      "84/84 [==============================] - 4s 42ms/step - loss: 0.3447 - accuracy: 0.8658\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(data_generator.flow(train_images, train_labels, batch_size=batch_size), steps_per_epoch=len(train_images) // batch_size, epochs=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3ca90f0",
   "metadata": {},
   "source": [
    "### The graphs of accuracy and loss after each epoch are plotted"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "11d11a31",
   "metadata": {},
   "source": [
    "These graphs show a logarithmic trend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "55e28196",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot accuracy\n",
    "plt.plot(history.history['accuracy'])\n",
    "plt.title('Model Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend(['train', 'validation'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plot loss\n",
    "plt.plot(history.history['loss'])\n",
    "plt.title('Model Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend(['train', 'validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2fbe3ed0",
   "metadata": {},
   "source": [
    "### Evaluating the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0797aa7",
   "metadata": {},
   "source": [
    "The model's loss and accuracy is evaluted against the test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f8b61da0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 0s 11ms/step - loss: 0.6169 - accuracy: 0.8205\n",
      "Test Loss: 0.6169487237930298\n",
      "Test Accuracy: 0.8204748034477234\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model\n",
    "loss, accuracy = model.evaluate(test_images, test_labels)\n",
    "print(\"Test Loss:\", loss)\n",
    "print(\"Test Accuracy:\", accuracy)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35a721e3",
   "metadata": {},
   "source": [
    "A testing accuracy of 83% indicates that the model is accurate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b7e5bd8",
   "metadata": {},
   "source": [
    "## Classifying images "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "197e3b57",
   "metadata": {},
   "source": [
    "### Initializing the arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "934bde4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "images_to_be_classified = os.listdir(klass_dir)\n",
    "resized_images = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0e5ec25",
   "metadata": {},
   "source": [
    "### Preprocessing the images"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4295b3ad",
   "metadata": {},
   "source": [
    "This is done to make the images compatible with the model, as it can function accurately only if a particular format of images is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f07685c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "for image_name in images_to_be_classified:\n",
    "    # Construct the full path to the image\n",
    "    image_path = os.path.join(klass_dir, image_name)\n",
    "    # Load the image using OpenCV\n",
    "    image = cv2.imread(image_path)\n",
    "    # Resize the image\n",
    "    resized_image = cv2.resize(image, (img_size, img_size))\n",
    "    # Append the resized image to the list\n",
    "    resized_images.append(resized_image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f1e0626f",
   "metadata": {},
   "outputs": [],
   "source": [
    "resized_images = np.array(resized_images)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "74e6fbcc",
   "metadata": {},
   "outputs": [],
   "source": [
    "resized_images = resized_images / 255.0"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3dcac5fa",
   "metadata": {},
   "source": [
    "### Predicting image labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e4bc1801",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 86ms/step\n"
     ]
    }
   ],
   "source": [
    "predictions = model.predict(resized_images)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94ccde01",
   "metadata": {},
   "source": [
    "### Printing image names along with their corresponding labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7d5d34c",
   "metadata": {},
   "source": [
    "The labels with their corresponding arrays are as follows:\n",
    "Crown root rot - 0010, Healthy - 0001, Leaf rust - 1000, Loose smut - 0100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "cca8a7e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image: crr_1.jpg Predicted Label: [0. 0. 0. 1.]\n",
      "Image: crr_2.jpg Predicted Label: [0. 0. 0. 0.]\n",
      "Image: h_1.jpg Predicted Label: [0. 0. 0. 1.]\n",
      "Image: h_2.jpg Predicted Label: [0. 0. 0. 1.]\n",
      "Image: lr_1.jpg Predicted Label: [0. 0. 1. 0.]\n",
      "Image: lr_2.jpg Predicted Label: [0. 0. 1. 0.]\n",
      "Image: ls_1.jpg Predicted Label: [0. 1. 0. 0.]\n",
      "Image: ls_2.jpg Predicted Label: [0. 1. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(images_to_be_classified)):\n",
    "    image_name = images_to_be_classified[i]\n",
    "    prediction = predictions[i]\n",
    "    rounded_prediction = (np.round(prediction))\n",
    "    print(\"Image:\", image_name, \"Predicted Label:\", rounded_prediction)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6101dd8",
   "metadata": {},
   "source": [
    "It is observed that out of the 8 images, 6 images are correctly identified."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b3eaa91",
   "metadata": {},
   "source": [
    "### Saving the model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9547af2",
   "metadata": {},
   "source": [
    "The model is saved as an H5 file. This format is used as both weights and model configuration can be stored in the same file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5e2a72d3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\abdal\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\keras\\src\\engine\\training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "model.save('wheatDiseaseModel.h5')"
   ]
  }
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