Train_plant_disease.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plant Disease Prediction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BeB10ZMav-SW"
   },
   "source": [
    "## Importing Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rPw7by-7Fs5M"
   },
   "source": [
    "Dataset Link: https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5vUbBvinu8zW"
   },
   "source": [
    "## Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 390,
   "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": "i2w52yYrvTu9"
   },
   "source": [
    "## Data Preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vU3NlcKCwlIZ"
   },
   "source": [
    "### Training Image preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "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",
    "    '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": "markdown",
   "metadata": {
    "id": "nK9Ev76o0mdJ"
   },
   "source": [
    "### Validation Image Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "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",
    "    '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": "markdown",
   "metadata": {},
   "source": [
    "#### To avoid Overshooting Loss function\n",
    "1. Choose small learning rate default 0.001 here we have taken 0.0001\n",
    "2. There may be chance of underfitting so increase number of neuron\n",
    "3. Add more Convolutional Layer to extract more feature from images there may be possibilty that model unable to capture relevant feature or model is confusing due to lack of feature so feed with more feature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OQ_TcbD7r2Cw"
   },
   "source": [
    "## Building Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "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": 243,
   "metadata": {
    "id": "LoaODWuZrHFg"
   },
   "outputs": [],
   "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": 244,
   "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": 245,
   "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": 246,
   "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": 247,
   "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": 248,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Dropout(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "metadata": {
    "id": "NPZ2NGthuTv5"
   },
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "metadata": {
    "id": "demjWjcWuc3q"
   },
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Dense(units=1500,activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {
    "id": "2onuZO6YuBF3"
   },
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Dropout(0.4)) #To avoid overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "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": 253,
   "metadata": {
    "id": "yAcyiEfVvVzw"
   },
   "outputs": [],
   "source": [
    "cnn.compile(optimizer=tf.keras.optimizers.legacy.Adam(\n",
    "    learning_rate=0.0001),loss='categorical_crossentropy',metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "X0MROJsdlC5Z",
    "outputId": "59099315-950c-4546-d62d-626f4ca3b80d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_18\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_124 (Conv2D)         (None, 128, 128, 32)      896       \n",
      "                                                                 \n",
      " conv2d_125 (Conv2D)         (None, 126, 126, 32)      9248      \n",
      "                                                                 \n",
      " max_pooling2d_62 (MaxPooli  (None, 63, 63, 32)        0         \n",
      " ng2D)                                                           \n",
      "                                                                 \n",
      " conv2d_126 (Conv2D)         (None, 63, 63, 64)        18496     \n",
      "                                                                 \n",
      " conv2d_127 (Conv2D)         (None, 61, 61, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_63 (MaxPooli  (None, 30, 30, 64)        0         \n",
      " ng2D)                                                           \n",
      "                                                                 \n",
      " conv2d_128 (Conv2D)         (None, 30, 30, 128)       73856     \n",
      "                                                                 \n",
      " conv2d_129 (Conv2D)         (None, 28, 28, 128)       147584    \n",
      "                                                                 \n",
      " max_pooling2d_64 (MaxPooli  (None, 14, 14, 128)       0         \n",
      " ng2D)                                                           \n",
      "                                                                 \n",
      " conv2d_130 (Conv2D)         (None, 14, 14, 256)       295168    \n",
      "                                                                 \n",
      " conv2d_131 (Conv2D)         (None, 12, 12, 256)       590080    \n",
      "                                                                 \n",
      " max_pooling2d_65 (MaxPooli  (None, 6, 6, 256)         0         \n",
      " ng2D)                                                           \n",
      "                                                                 \n",
      " conv2d_132 (Conv2D)         (None, 6, 6, 512)         1180160   \n",
      "                                                                 \n",
      " conv2d_133 (Conv2D)         (None, 4, 4, 512)         2359808   \n",
      "                                                                 \n",
      " max_pooling2d_66 (MaxPooli  (None, 2, 2, 512)         0         \n",
      " ng2D)                                                           \n",
      "                                                                 \n",
      " dropout_45 (Dropout)        (None, 2, 2, 512)         0         \n",
      "                                                                 \n",
      " flatten_16 (Flatten)        (None, 2048)              0         \n",
      "                                                                 \n",
      " dense_34 (Dense)            (None, 1500)              3073500   \n",
      "                                                                 \n",
      " dropout_46 (Dropout)        (None, 1500)              0         \n",
      "                                                                 \n",
      " dense_35 (Dense)            (None, 38)                57038     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 7842762 (29.92 MB)\n",
      "Trainable params: 7842762 (29.92 MB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "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",
      "2197/2197 [==============================] - 274s 124ms/step - loss: 1.3926 - accuracy: 0.5918 - val_loss: 0.4671 - val_accuracy: 0.8499\n",
      "Epoch 2/10\n",
      "2197/2197 [==============================] - 268s 122ms/step - loss: 0.5093 - accuracy: 0.8419 - val_loss: 0.3337 - val_accuracy: 0.8961\n",
      "Epoch 3/10\n",
      "2197/2197 [==============================] - 269s 122ms/step - loss: 0.3325 - accuracy: 0.8951 - val_loss: 0.2296 - val_accuracy: 0.9272\n",
      "Epoch 4/10\n",
      "2197/2197 [==============================] - 278s 127ms/step - loss: 0.2506 - accuracy: 0.9226 - val_loss: 0.2608 - val_accuracy: 0.9266\n",
      "Epoch 5/10\n",
      "2197/2197 [==============================] - 274s 125ms/step - loss: 0.1962 - accuracy: 0.9405 - val_loss: 0.2714 - val_accuracy: 0.9191\n",
      "Epoch 6/10\n",
      "2197/2197 [==============================] - 268s 122ms/step - loss: 0.1619 - accuracy: 0.9506 - val_loss: 0.2136 - val_accuracy: 0.9352\n",
      "Epoch 7/10\n",
      "2197/2197 [==============================] - 272s 124ms/step - loss: 0.1459 - accuracy: 0.9560 - val_loss: 0.2002 - val_accuracy: 0.9422\n",
      "Epoch 8/10\n",
      "2197/2197 [==============================] - 279s 127ms/step - loss: 0.1262 - accuracy: 0.9633 - val_loss: 0.1890 - val_accuracy: 0.9467\n",
      "Epoch 9/10\n",
      "2197/2197 [==============================] - 273s 124ms/step - loss: 0.1100 - accuracy: 0.9679 - val_loss: 0.3728 - val_accuracy: 0.9220\n",
      "Epoch 10/10\n",
      "2197/2197 [==============================] - 260s 118ms/step - loss: 0.1059 - accuracy: 0.9702 - val_loss: 0.2495 - val_accuracy: 0.9459\n"
     ]
    }
   ],
   "source": [
    "training_history = cnn.fit(x=training_set,validation_data=validation_set,epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "19E3mwj1phoW"
   },
   "source": [
    "## Evaluating Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iY6o3uxjpgl5",
    "outputId": "372a2b25-5cdd-47c0-da77-1e9a5ee84f6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2197/2197 [==============================] - 56s 26ms/step - loss: 0.0783 - accuracy: 0.9782\n",
      "Training accuracy: 0.9781919121742249\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": 267,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "5SaQgd7bpgwl",
    "outputId": "0e82ce04-bdf8-4b31-bc00-cbb7198df7e7"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "550/550 [==============================] - 14s 25ms/step - loss: 0.2495 - accuracy: 0.9459\n",
      "Validation accuracy: 0.9458798170089722\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": 269,
   "metadata": {
    "id": "EEUrBzCrxKt_"
   },
   "outputs": [],
   "source": [
    "cnn.save('trained_plant_disease_model.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BM-sILNMBwh1",
    "outputId": "3d1eb8a8-2742-4b1b-f8f3-10c1f0803975"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': [1.3926340341567993,\n",
       "  0.5093422532081604,\n",
       "  0.332528293132782,\n",
       "  0.25064608454704285,\n",
       "  0.1961534023284912,\n",
       "  0.16193240880966187,\n",
       "  0.14591246843338013,\n",
       "  0.12622447311878204,\n",
       "  0.10997208952903748,\n",
       "  0.10591727495193481],\n",
       " 'accuracy': [0.5917917490005493,\n",
       "  0.8418664336204529,\n",
       "  0.8951276540756226,\n",
       "  0.9225834012031555,\n",
       "  0.9405078887939453,\n",
       "  0.9505512714385986,\n",
       "  0.9559854865074158,\n",
       "  0.9632833003997803,\n",
       "  0.9678924679756165,\n",
       "  0.9702112674713135],\n",
       " 'val_loss': [0.4670976996421814,\n",
       "  0.33370524644851685,\n",
       "  0.22960424423217773,\n",
       "  0.2608007490634918,\n",
       "  0.2714437246322632,\n",
       "  0.21357299387454987,\n",
       "  0.20022274553775787,\n",
       "  0.18895581364631653,\n",
       "  0.3728276193141937,\n",
       "  0.24945156276226044],\n",
       " 'val_accuracy': [0.8498747944831848,\n",
       "  0.8961415886878967,\n",
       "  0.9271568655967712,\n",
       "  0.9266446828842163,\n",
       "  0.9191327095031738,\n",
       "  0.9352378845214844,\n",
       "  0.9422376751899719,\n",
       "  0.9466765522956848,\n",
       "  0.9220350384712219,\n",
       "  0.9458798170089722]}"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_history.history #Return Dictionary of history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "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": 274,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "w8PnAYbuCNhu",
    "outputId": "665cd52d-4777-4e7e-9c04-cc7716c5fb62"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    }
   ],
   "source": [
    "print(training_history.history.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "qNIMPXKFEZ5B"
   },
   "source": [
    "## Accuracy Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "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": "code",
   "execution_count": null,
   "metadata": {
    "id": "MmHXzT0PAiOn"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some other metrics for model evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "metadata": {},
   "outputs": [],
   "source": [
    "class_name = validation_set.class_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "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",
    "    '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": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17572/17572 [==============================] - 115s 7ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = cnn.predict(test_set)\n",
    "predicted_categories = tf.argmax(y_pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "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": 406,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(17572,), dtype=int64, numpy=array([ 0,  0,  0, ..., 37, 37, 37])>"
      ]
     },
     "execution_count": 406,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(17572,), dtype=int64, numpy=array([ 0,  0,  0, ..., 37, 37, 37])>"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "cm = confusion_matrix(Y_true,predicted_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 409,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                    precision    recall  f1-score   support\n",
      "\n",
      "                                Apple___Apple_scab       1.00      0.84      0.91       504\n",
      "                                 Apple___Black_rot       0.96      0.98      0.97       497\n",
      "                          Apple___Cedar_apple_rust       0.95      0.99      0.97       440\n",
      "                                   Apple___healthy       0.85      0.93      0.89       502\n",
      "                               Blueberry___healthy       0.85      0.99      0.92       454\n",
      "          Cherry_(including_sour)___Powdery_mildew       1.00      0.89      0.94       421\n",
      "                 Cherry_(including_sour)___healthy       0.95      0.97      0.96       456\n",
      "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot       0.96      0.89      0.92       410\n",
      "                       Corn_(maize)___Common_rust_       1.00      0.98      0.99       477\n",
      "               Corn_(maize)___Northern_Leaf_Blight       0.90      0.98      0.94       477\n",
      "                            Corn_(maize)___healthy       1.00      0.99      0.99       465\n",
      "                                 Grape___Black_rot       1.00      0.94      0.97       472\n",
      "                      Grape___Esca_(Black_Measles)       0.98      0.99      0.98       480\n",
      "        Grape___Leaf_blight_(Isariopsis_Leaf_Spot)       0.96      1.00      0.98       430\n",
      "                                   Grape___healthy       0.99      0.99      0.99       423\n",
      "          Orange___Haunglongbing_(Citrus_greening)       0.93      0.99      0.96       503\n",
      "                            Peach___Bacterial_spot       0.91      0.95      0.93       459\n",
      "                                   Peach___healthy       0.94      0.99      0.96       432\n",
      "                     Pepper,_bell___Bacterial_spot       0.99      0.89      0.93       478\n",
      "                            Pepper,_bell___healthy       0.99      0.81      0.89       497\n",
      "                             Potato___Early_blight       0.99      0.94      0.96       485\n",
      "                              Potato___Late_blight       0.90      0.98      0.94       485\n",
      "                                  Potato___healthy       0.91      0.97      0.94       456\n",
      "                               Raspberry___healthy       0.89      1.00      0.94       445\n",
      "                                 Soybean___healthy       0.95      0.99      0.97       505\n",
      "                           Squash___Powdery_mildew       1.00      0.92      0.96       434\n",
      "                          Strawberry___Leaf_scorch       0.96      0.96      0.96       444\n",
      "                              Strawberry___healthy       0.98      0.98      0.98       456\n",
      "                           Tomato___Bacterial_spot       0.97      0.96      0.96       425\n",
      "                             Tomato___Early_blight       0.85      0.90      0.87       480\n",
      "                              Tomato___Late_blight       0.86      0.91      0.89       463\n",
      "                                Tomato___Leaf_Mold       0.98      0.94      0.96       470\n",
      "                       Tomato___Septoria_leaf_spot       0.95      0.82      0.88       436\n",
      "     Tomato___Spider_mites Two-spotted_spider_mite       0.89      0.97      0.93       435\n",
      "                              Tomato___Target_Spot       0.92      0.88      0.90       457\n",
      "            Tomato___Tomato_Yellow_Leaf_Curl_Virus       0.99      0.98      0.99       490\n",
      "                      Tomato___Tomato_mosaic_virus       1.00      0.91      0.95       448\n",
      "                                  Tomato___healthy       0.97      0.98      0.98       481\n",
      "\n",
      "                                          accuracy                           0.95     17572\n",
      "                                         macro avg       0.95      0.95      0.95     17572\n",
      "                                      weighted avg       0.95      0.95      0.95     17572\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Precision Recall Fscore\n",
    "print(classification_report(Y_true,predicted_categories,target_names=class_name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion Matrix Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 410,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 4000x4000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(40, 40))\n",
    "sns.heatmap(cm,annot=True,annot_kws={\"size\": 10})\n",
    "\n",
    "plt.xlabel('Predicted Class',fontsize = 20)\n",
    "plt.ylabel('Actual Class',fontsize = 20)\n",
    "plt.title('Plant Disease Prediction Confusion Matrix',fontsize = 25)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}