Train_plant_disease.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "4JsejDm3uxTS"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vU3NlcKCwlIZ"
   },
   "source": [
    "### Training Image preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Kjoa0oUHvQAv",
    "outputId": "35053f0d-5fd0-4b98-cd8f-fa0a88a79e8f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 70295 files belonging to 38 classes.\n"
     ]
    }
   ],
   "source": [
    "training_set = tf.keras.utils.image_dataset_from_directory(\n",
    "    r'New Plant Diseases Dataset(Augmented)\\New Plant Diseases Dataset(Augmented)\\train',\n",
    "    labels=\"inferred\",\n",
    "    label_mode=\"categorical\",\n",
    "    class_names=None,\n",
    "    color_mode=\"rgb\",\n",
    "    batch_size=32,\n",
    "    image_size=(128, 128),\n",
    "    shuffle=True,\n",
    "    seed=None,\n",
    "    validation_split=None,\n",
    "    subset=None,\n",
    "    interpolation=\"bilinear\",\n",
    "    follow_links=False,\n",
    "    crop_to_aspect_ratio=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Tocls4IowkNB",
    "outputId": "3b0d8b3d-8552-4e0f-c1f2-7c9f7e28a944"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 17572 files belonging to 38 classes.\n"
     ]
    }
   ],
   "source": [
    "validation_set = tf.keras.utils.image_dataset_from_directory(\n",
    "    r'New Plant Diseases Dataset(Augmented)\\New Plant Diseases Dataset(Augmented)\\valid',\n",
    "    labels=\"inferred\",\n",
    "    label_mode=\"categorical\",\n",
    "    class_names=None,\n",
    "    color_mode=\"rgb\",\n",
    "    batch_size=32,\n",
    "    image_size=(128, 128),\n",
    "    shuffle=True,\n",
    "    seed=None,\n",
    "    validation_split=None,\n",
    "    subset=None,\n",
    "    interpolation=\"bilinear\",\n",
    "    follow_links=False,\n",
    "    crop_to_aspect_ratio=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "cUMiJrPvsBTU"
   },
   "outputs": [],
   "source": [
    "cnn = tf.keras.models.Sequential()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mgFWYq7ssLkV"
   },
   "source": [
    "### Building Convolution Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "LoaODWuZrHFg"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\jainj\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "cnn.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,padding='same',activation='relu',input_shape=[128,128,3]))\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=32,kernel_size=3,activation='relu'))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "5-potKmKsOr2"
   },
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Conv2D(filters=64,kernel_size=3,padding='same',activation='relu'))\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=64,kernel_size=3,activation='relu'))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Conv2D(filters=128,kernel_size=3,padding='same',activation='relu'))\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=128,kernel_size=3,activation='relu'))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Conv2D(filters=256,kernel_size=3,padding='same',activation='relu'))\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=256,kernel_size=3,activation='relu'))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Conv2D(filters=512,kernel_size=3,padding='same',activation='relu'))\n",
    "cnn.add(tf.keras.layers.Conv2D(filters=512,kernel_size=3,activation='relu'))\n",
    "cnn.add(tf.keras.layers.MaxPool2D(pool_size=2,strides=2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Dropout(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "id": "NPZ2NGthuTv5"
   },
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "demjWjcWuc3q"
   },
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Dense(units=1500,activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "2onuZO6YuBF3"
   },
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Dropout(0.4)) #To avoid overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "toAqflkCuwoS"
   },
   "outputs": [],
   "source": [
    "#Output Layer\n",
    "cnn.add(tf.keras.layers.Dense(units=38,activation='softmax'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sYYdAFZDvt1P"
   },
   "source": [
    "### Compiling and Training Phase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "yAcyiEfVvVzw"
   },
   "outputs": [],
   "source": [
    "cnn.compile(\n",
    "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),  # Updated optimizer\n",
    "    loss='categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X0MROJsdlC5Z",
    "outputId": "59099315-950c-4546-d62d-626f4ca3b80d"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
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     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">896</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">126</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">126</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">63</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">63</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">63</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">63</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">18,496</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">61</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">61</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">36,928</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)     │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">30</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │        <span style=\"color: #00af00; text-decoration-color: #00af00\">73,856</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">147,584</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)    │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">295,168</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)    │       <span style=\"color: #00af00; text-decoration-color: #00af00\">590,080</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">1,180,160</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │     <span style=\"color: #00af00; text-decoration-color: #00af00\">2,359,808</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling2D</span>)  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)      │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">2048</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1500</span>)           │     <span style=\"color: #00af00; text-decoration-color: #00af00\">3,073,500</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1500</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">38</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">57,038</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ conv2d (\u001b[38;5;33mConv2D\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │           \u001b[38;5;34m896\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m126\u001b[0m, \u001b[38;5;34m126\u001b[0m, \u001b[38;5;34m32\u001b[0m)   │         \u001b[38;5;34m9,248\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d (\u001b[38;5;33mMaxPooling2D\u001b[0m)    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m63\u001b[0m, \u001b[38;5;34m63\u001b[0m, \u001b[38;5;34m32\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m63\u001b[0m, \u001b[38;5;34m63\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m18,496\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_3 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m61\u001b[0m, \u001b[38;5;34m61\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │        \u001b[38;5;34m36,928\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_1 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m64\u001b[0m)     │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m30\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │        \u001b[38;5;34m73,856\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │       \u001b[38;5;34m147,584\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_2 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m128\u001b[0m)    │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_6 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m295,168\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_7 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m12\u001b[0m, \u001b[38;5;34m256\u001b[0m)    │       \u001b[38;5;34m590,080\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_3 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m256\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_8 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m6\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m1,180,160\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ conv2d_9 (\u001b[38;5;33mConv2D\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m4\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │     \u001b[38;5;34m2,359,808\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ max_pooling2d_4 (\u001b[38;5;33mMaxPooling2D\u001b[0m)  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout (\u001b[38;5;33mDropout\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m2\u001b[0m, \u001b[38;5;34m512\u001b[0m)      │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ flatten (\u001b[38;5;33mFlatten\u001b[0m)               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m2048\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1500\u001b[0m)           │     \u001b[38;5;34m3,073,500\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1500\u001b[0m)           │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m38\u001b[0m)             │        \u001b[38;5;34m57,038\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,842,762</span> (29.92 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m7,842,762\u001b[0m (29.92 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">7,842,762</span> (29.92 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m7,842,762\u001b[0m (29.92 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "K3W4dNUrvxIw",
    "outputId": "c4d220dc-40d6-438e-b940-751b3e85b037"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m397s\u001b[0m 180ms/step - accuracy: 0.1563 - loss: 4.7865 - val_accuracy: 0.6268 - val_loss: 1.3456\n",
      "Epoch 2/10\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m302s\u001b[0m 137ms/step - accuracy: 0.7291 - loss: 0.9547 - val_accuracy: 0.8334 - val_loss: 0.5684\n",
      "Epoch 3/10\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m320s\u001b[0m 146ms/step - accuracy: 0.8923 - loss: 0.3598 - val_accuracy: 0.8912 - val_loss: 0.3621\n",
      "Epoch 4/10\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m308s\u001b[0m 140ms/step - accuracy: 0.9478 - loss: 0.1733 - val_accuracy: 0.8455 - val_loss: 0.5992\n",
      "Epoch 5/10\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m315s\u001b[0m 143ms/step - accuracy: 0.9668 - loss: 0.1063 - val_accuracy: 0.9080 - val_loss: 0.3363\n",
      "Epoch 6/10\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m313s\u001b[0m 142ms/step - accuracy: 0.9793 - loss: 0.0646 - val_accuracy: 0.9181 - val_loss: 0.3217\n",
      "Epoch 7/10\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m312s\u001b[0m 142ms/step - accuracy: 0.9820 - loss: 0.0544 - val_accuracy: 0.9174 - val_loss: 0.3361\n",
      "Epoch 8/10\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m314s\u001b[0m 143ms/step - accuracy: 0.9883 - loss: 0.0389 - val_accuracy: 0.9276 - val_loss: 0.2976\n",
      "Epoch 9/10\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m316s\u001b[0m 144ms/step - accuracy: 0.9893 - loss: 0.0331 - val_accuracy: 0.9282 - val_loss: 0.2965\n",
      "Epoch 10/10\n",
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m316s\u001b[0m 144ms/step - accuracy: 0.9894 - loss: 0.0319 - val_accuracy: 0.9240 - val_loss: 0.3337\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense, Conv2D, MaxPooling2D, Flatten\n",
    "\n",
    "cnn = Sequential([\n",
    "    Conv2D(32, (3, 3), activation='relu', padding='same', input_shape=(128, 128, 3)),\n",
    "    MaxPooling2D((2, 2)),\n",
    "    Conv2D(64, (3, 3), activation='relu', padding='same'),\n",
    "    MaxPooling2D((2, 2)),\n",
    "    Conv2D(128, (3, 3), activation='relu', padding='same'),\n",
    "    MaxPooling2D((2, 2)),\n",
    "    Flatten(),\n",
    "    Dense(128, activation='relu'),\n",
    "    Dense(38, activation='softmax')  # Adjust number of classes as needed\n",
    "])\n",
    "\n",
    "cnn.compile(\n",
    "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),\n",
    "    loss='categorical_crossentropy',\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "training_history = cnn.fit(x=training_set,validation_data=validation_set,epochs=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "19E3mwj1phoW"
   },
   "source": [
    "## Evaluating Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iY6o3uxjpgl5",
    "outputId": "372a2b25-5cdd-47c0-da77-1e9a5ee84f6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2197/2197\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m91s\u001b[0m 41ms/step - accuracy: 0.9898 - loss: 0.0297\n",
      "Training accuracy: 0.9911515712738037\n"
     ]
    }
   ],
   "source": [
    "#Training set Accuracy\n",
    "train_loss, train_acc = cnn.evaluate(training_set)\n",
    "print('Training accuracy:', train_acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5SaQgd7bpgwl",
    "outputId": "0e82ce04-bdf8-4b31-bc00-cbb7198df7e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m550/550\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m23s\u001b[0m 42ms/step - accuracy: 0.9263 - loss: 0.3242\n",
      "Validation accuracy: 0.9239699244499207\n"
     ]
    }
   ],
   "source": [
    "#Validation set Accuracy\n",
    "val_loss, val_acc = cnn.evaluate(validation_set)\n",
    "print('Validation accuracy:', val_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Qxp0xsjVBGec"
   },
   "source": [
    "### Saving Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "EEUrBzCrxKt_"
   },
   "outputs": [],
   "source": [
    "cnn.save('trained_plant_disease_model.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BM-sILNMBwh1",
    "outputId": "3d1eb8a8-2742-4b1b-f8f3-10c1f0803975"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'accuracy': [0.31219857931137085,\n",
       "  0.7886051535606384,\n",
       "  0.9084429740905762,\n",
       "  0.9547478556632996,\n",
       "  0.9710505604743958,\n",
       "  0.9800981283187866,\n",
       "  0.9832136034965515,\n",
       "  0.9886763095855713,\n",
       "  0.9896009564399719,\n",
       "  0.9906821250915527],\n",
       " 'loss': [2.834536552429199,\n",
       "  0.7331081032752991,\n",
       "  0.3008267283439636,\n",
       "  0.14722761511802673,\n",
       "  0.09221765398979187,\n",
       "  0.06272539496421814,\n",
       "  0.052215561270713806,\n",
       "  0.038395050913095474,\n",
       "  0.033934421837329865,\n",
       "  0.028414180502295494],\n",
       " 'val_accuracy': [0.6267926096916199,\n",
       "  0.8334282040596008,\n",
       "  0.8912474513053894,\n",
       "  0.8455497622489929,\n",
       "  0.9079785943031311,\n",
       "  0.918108344078064,\n",
       "  0.9174254536628723,\n",
       "  0.9276121258735657,\n",
       "  0.9281811714172363,\n",
       "  0.9239699244499207],\n",
       " 'val_loss': [1.345585584640503,\n",
       "  0.5684199333190918,\n",
       "  0.36211246252059937,\n",
       "  0.5991560220718384,\n",
       "  0.3363189697265625,\n",
       "  0.3217024505138397,\n",
       "  0.3360598087310791,\n",
       "  0.2976413071155548,\n",
       "  0.29650530219078064,\n",
       "  0.33365413546562195]}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_history.history #Return Dictionary of history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "GdF3Ig4ZBU9V"
   },
   "outputs": [],
   "source": [
    "#Recording History in json\n",
    "import json\n",
    "with open('training_hist.json','w') as f:\n",
    "  json.dump(training_history.history,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w8PnAYbuCNhu",
    "outputId": "665cd52d-4777-4e7e-9c04-cc7716c5fb62"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['accuracy', 'loss', 'val_accuracy', 'val_loss'])\n"
     ]
    }
   ],
   "source": [
    "print(training_history.history.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qNIMPXKFEZ5B"
   },
   "source": [
    "## Accuracy Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "id": "gfKdieuJCt3m",
    "outputId": "00c39bea-d59d-48bd-a9bb-f819e1313252"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "epochs = [i for i in range(1,11)]\n",
    "plt.plot(epochs,training_history.history['accuracy'],color='red',label='Training Accuracy')\n",
    "plt.plot(epochs,training_history.history['val_accuracy'],color='blue',label='Validation Accuracy')\n",
    "plt.xlabel('No. of Epochs')\n",
    "plt.title('Visualization of Accuracy Result')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some other metrics for model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_name = validation_set.class_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 17572 files belonging to 38 classes.\n"
     ]
    }
   ],
   "source": [
    "test_set = tf.keras.utils.image_dataset_from_directory(\n",
    "    r'New Plant Diseases Dataset(Augmented)\\New Plant Diseases Dataset(Augmented)\\valid',\n",
    "    labels=\"inferred\",\n",
    "    label_mode=\"categorical\",\n",
    "    class_names=None,\n",
    "    color_mode=\"rgb\",\n",
    "    batch_size=1,\n",
    "    image_size=(128, 128),\n",
    "    shuffle=False,\n",
    "    seed=None,\n",
    "    validation_split=None,\n",
    "    subset=None,\n",
    "    interpolation=\"bilinear\",\n",
    "    follow_links=False,\n",
    "    crop_to_aspect_ratio=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m17572/17572\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m75s\u001b[0m 4ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = cnn.predict(test_set)\n",
    "predicted_categories = tf.argmax(y_pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "true_categories = tf.concat([y for x, y in test_set], axis=0)\n",
    "Y_true = tf.argmax(true_categories, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(17572,), dtype=int64, numpy=array([ 0,  0,  0, ..., 37, 37, 37], dtype=int64)>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(17572,), dtype=int64, numpy=array([ 0,  0,  0, ..., 37, 37, 37], dtype=int64)>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "cm = confusion_matrix(Y_true,predicted_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                    precision    recall  f1-score   support\n",
      "\n",
      "                                Apple___Apple_scab       0.87      0.95      0.91       504\n",
      "                                 Apple___Black_rot       0.87      0.99      0.93       497\n",
      "                          Apple___Cedar_apple_rust       0.96      0.97      0.96       440\n",
      "                                   Apple___healthy       0.91      0.91      0.91       502\n",
      "                               Blueberry___healthy       0.88      0.97      0.93       454\n",
      "          Cherry_(including_sour)___Powdery_mildew       0.99      0.96      0.98       421\n",
      "                 Cherry_(including_sour)___healthy       0.98      0.97      0.97       456\n",
      "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot       0.87      0.87      0.87       410\n",
      "                       Corn_(maize)___Common_rust_       0.99      0.99      0.99       477\n",
      "               Corn_(maize)___Northern_Leaf_Blight       0.89      0.93      0.91       477\n",
      "                            Corn_(maize)___healthy       0.99      1.00      0.99       465\n",
      "                                 Grape___Black_rot       0.90      0.96      0.93       472\n",
      "                      Grape___Esca_(Black_Measles)       0.96      0.94      0.95       480\n",
      "        Grape___Leaf_blight_(Isariopsis_Leaf_Spot)       1.00      0.93      0.96       430\n",
      "                                   Grape___healthy       0.94      0.99      0.96       423\n",
      "          Orange___Haunglongbing_(Citrus_greening)       0.98      0.97      0.98       503\n",
      "                            Peach___Bacterial_spot       0.86      0.95      0.90       459\n",
      "                                   Peach___healthy       0.98      0.96      0.97       432\n",
      "                     Pepper,_bell___Bacterial_spot       0.87      0.93      0.90       478\n",
      "                            Pepper,_bell___healthy       0.90      0.92      0.91       497\n",
      "                             Potato___Early_blight       0.96      0.95      0.96       485\n",
      "                              Potato___Late_blight       0.87      0.89      0.88       485\n",
      "                                  Potato___healthy       0.98      0.85      0.91       456\n",
      "                               Raspberry___healthy       1.00      0.90      0.94       445\n",
      "                                 Soybean___healthy       0.95      0.94      0.94       505\n",
      "                           Squash___Powdery_mildew       0.99      0.95      0.97       434\n",
      "                          Strawberry___Leaf_scorch       0.96      0.96      0.96       444\n",
      "                              Strawberry___healthy       0.98      0.93      0.95       456\n",
      "                           Tomato___Bacterial_spot       0.93      0.91      0.92       425\n",
      "                             Tomato___Early_blight       0.79      0.78      0.79       480\n",
      "                              Tomato___Late_blight       0.90      0.75      0.82       463\n",
      "                                Tomato___Leaf_Mold       0.97      0.80      0.88       470\n",
      "                       Tomato___Septoria_leaf_spot       0.77      0.77      0.77       436\n",
      "     Tomato___Spider_mites Two-spotted_spider_mite       0.96      0.82      0.88       435\n",
      "                              Tomato___Target_Spot       0.73      0.95      0.83       457\n",
      "            Tomato___Tomato_Yellow_Leaf_Curl_Virus       0.99      0.94      0.96       490\n",
      "                      Tomato___Tomato_mosaic_virus       0.96      0.96      0.96       448\n",
      "                                  Tomato___healthy       0.98      0.98      0.98       481\n",
      "\n",
      "                                          accuracy                           0.92     17572\n",
      "                                         macro avg       0.93      0.92      0.92     17572\n",
      "                                      weighted avg       0.93      0.92      0.92     17572\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Precision Recall Fscore\n",
    "print(classification_report(Y_true,predicted_categories,target_names=class_name))"
   ]
  }
 ],
 "metadata": {
  "colab": {
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}