crop  sys.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f81d949-ee03-46c9-9f98-6193c956be55",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "print(\"1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5772aa84-9f8f-434c-820d-a8e7e2844ea7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[name: \"/device:CPU:0\"\n",
      "device_type: \"CPU\"\n",
      "memory_limit: 268435456\n",
      "locality {\n",
      "}\n",
      "incarnation: 10466624740314776472\n",
      "xla_global_id: -1\n",
      ", name: \"/device:GPU:0\"\n",
      "device_type: \"GPU\"\n",
      "memory_limit: 2232156160\n",
      "locality {\n",
      "  bus_id: 1\n",
      "  links {\n",
      "  }\n",
      "}\n",
      "incarnation: 8076824634892510355\n",
      "physical_device_desc: \"device: 0, name: NVIDIA GeForce GTX 1650 Ti, pci bus id: 0000:01:00.0, compute capability: 7.5\"\n",
      "xla_global_id: 416903419\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.python.client import device_lib\n",
    "print(device_lib.list_local_devices())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "87615de1-6848-49e6-a068-0054628ed131",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 70295 files belonging to 38 classes.\n",
      "1\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=64,\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",
    ")\n",
    "print(\"1\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0febdaef-9dbe-481b-b716-3bd9db355d7b",
   "metadata": {},
   "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=64,\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,
   "id": "39d33d30-3467-485e-a0a4-31bcb15b49b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BatchDataset element_spec=(TensorSpec(shape=(None, 128, 128, 3), dtype=tf.float32, name=None), TensorSpec(shape=(None, 38), dtype=tf.float32, name=None))>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_set\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "324d1bd2-8fe8-488b-a645-6222f8e6008f",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn = tf.keras.models.Sequential()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "90078dad-60c0-46f8-8fe5-826384f3ec97",
   "metadata": {},
   "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": 8,
   "id": "f0f95ecd-fad6-4802-9486-48c11cb35e98",
   "metadata": {},
   "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,
   "id": "8bb8fe38-d339-421b-85aa-baa4279c7289",
   "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,
   "id": "b28d916e-2818-44b3-8a55-8c83f91364ec",
   "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,
   "id": "1ed03746-9d44-4928-b88c-f7f6a31c8c0f",
   "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,
   "id": "57d2104b-2570-4d2c-9a93-75d3b9d81f27",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Dropout(0.25))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "dd220156-5e84-4784-bdfe-fbd7aa1f856b",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Flatten())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a0d7026f-1d5d-48a2-8304-47bc383e6943",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Dense(units=1500,activation='relu'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "45c0f4d6-77c0-4a12-b857-04e65e7fa3a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.add(tf.keras.layers.Dropout(0.4)) #To avoid overfitting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "9f4820af-d5d6-416b-aa28-417e44fdfe9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Output Layer\n",
    "cnn.add(tf.keras.layers.Dense(units=38,activation='softmax'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "ff0d61bb-7d98-4d53-afb7-269462fdc06f",
   "metadata": {},
   "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": 18,
   "id": "d60ea828-dadb-41e9-8869-a82e35176ff3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 128, 128, 32)      896       \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 126, 126, 32)      9248      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 63, 63, 32)       0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 63, 63, 64)        18496     \n",
      "                                                                 \n",
      " conv2d_3 (Conv2D)           (None, 61, 61, 64)        36928     \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 30, 30, 64)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_4 (Conv2D)           (None, 30, 30, 128)       73856     \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 28, 28, 128)       147584    \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 14, 14, 128)      0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_6 (Conv2D)           (None, 14, 14, 256)       295168    \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 12, 12, 256)       590080    \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 6, 6, 256)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_8 (Conv2D)           (None, 6, 6, 512)         1180160   \n",
      "                                                                 \n",
      " conv2d_9 (Conv2D)           (None, 4, 4, 512)         2359808   \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPooling  (None, 2, 2, 512)        0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " dropout (Dropout)           (None, 2, 2, 512)         0         \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 2048)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1500)              3073500   \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 1500)              0         \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 38)                57038     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 7,842,762\n",
      "Trainable params: 7,842,762\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "cnn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f6ed6788-ee14-4fc2-9f8f-e9ddfc4f8050",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "1099/1099 [==============================] - 183s 160ms/step - loss: 1.3985 - accuracy: 0.5904 - val_loss: 0.5189 - val_accuracy: 0.8363\n",
      "Epoch 2/10\n",
      "1099/1099 [==============================] - 168s 153ms/step - loss: 0.4828 - accuracy: 0.8457 - val_loss: 0.3460 - val_accuracy: 0.8890\n",
      "Epoch 3/10\n",
      "1099/1099 [==============================] - 168s 153ms/step - loss: 0.2948 - accuracy: 0.9050 - val_loss: 0.2098 - val_accuracy: 0.9299\n",
      "Epoch 4/10\n",
      "1099/1099 [==============================] - 168s 153ms/step - loss: 0.2041 - accuracy: 0.9334 - val_loss: 0.1895 - val_accuracy: 0.9356\n",
      "Epoch 5/10\n",
      "1099/1099 [==============================] - 168s 153ms/step - loss: 0.1525 - accuracy: 0.9501 - val_loss: 0.1800 - val_accuracy: 0.9423\n",
      "Epoch 6/10\n",
      "1099/1099 [==============================] - 168s 153ms/step - loss: 0.1184 - accuracy: 0.9606 - val_loss: 0.1275 - val_accuracy: 0.9608\n",
      "Epoch 7/10\n",
      "1099/1099 [==============================] - 168s 153ms/step - loss: 0.1013 - accuracy: 0.9664 - val_loss: 0.1511 - val_accuracy: 0.9511\n",
      "Epoch 8/10\n",
      "1099/1099 [==============================] - 168s 153ms/step - loss: 0.0777 - accuracy: 0.9743 - val_loss: 0.1209 - val_accuracy: 0.9626\n",
      "Epoch 9/10\n",
      "1099/1099 [==============================] - 168s 153ms/step - loss: 0.0730 - accuracy: 0.9755 - val_loss: 0.1467 - val_accuracy: 0.9533\n",
      "Epoch 10/10\n",
      "1099/1099 [==============================] - 168s 153ms/step - loss: 0.0633 - accuracy: 0.9786 - val_loss: 0.1188 - val_accuracy: 0.9641\n"
     ]
    }
   ],
   "source": [
    "training_history = cnn.fit(x=training_set,validation_data=validation_set,epochs=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7f8e365f-8f11-4396-b012-d06d33758da1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1099/1099 [==============================] - 59s 53ms/step - loss: 0.0291 - accuracy: 0.9910\n",
      "Training accuracy: 0.9909951090812683\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,
   "id": "1d48b48b-c744-43cc-814a-48f12ba83346",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "275/275 [==============================] - 15s 53ms/step - loss: 0.1188 - accuracy: 0.9641\n",
      "Validation accuracy: 0.9641475081443787\n"
     ]
    }
   ],
   "source": [
    "val_loss, val_acc = cnn.evaluate(validation_set)\n",
    "print('Validation accuracy:', val_acc)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4747b420-8ca6-42c3-be86-13157601fd2a",
   "metadata": {},
   "source": [
    "### saving model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1fb886cc-4894-43bf-9175-4932ef7af572",
   "metadata": {},
   "outputs": [],
   "source": [
    "cnn.save('trained_plant_disease_model.keras')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f2880335-1d00-458b-a172-8781cd431b69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'loss': [1.3985103368759155,\n",
       "  0.4827614426612854,\n",
       "  0.2947623133659363,\n",
       "  0.2041148990392685,\n",
       "  0.1524718999862671,\n",
       "  0.11843691021203995,\n",
       "  0.10129374265670776,\n",
       "  0.07765915244817734,\n",
       "  0.07302737981081009,\n",
       "  0.0632883831858635],\n",
       " 'accuracy': [0.5904402732849121,\n",
       "  0.8456931710243225,\n",
       "  0.9050145745277405,\n",
       "  0.9334234595298767,\n",
       "  0.9500960111618042,\n",
       "  0.9606088399887085,\n",
       "  0.9663845300674438,\n",
       "  0.974322497844696,\n",
       "  0.9754605293273926,\n",
       "  0.9785617589950562],\n",
       " 'val_loss': [0.5189390778541565,\n",
       "  0.3459879159927368,\n",
       "  0.20979002118110657,\n",
       "  0.18949605524539948,\n",
       "  0.18003733456134796,\n",
       "  0.12751859426498413,\n",
       "  0.15107066929340363,\n",
       "  0.12088192254304886,\n",
       "  0.14668872952461243,\n",
       "  0.11879461258649826],\n",
       " 'val_accuracy': [0.8362736105918884,\n",
       "  0.889028012752533,\n",
       "  0.9298884868621826,\n",
       "  0.9356362223625183,\n",
       "  0.9422945380210876,\n",
       "  0.9608467817306519,\n",
       "  0.9510585069656372,\n",
       "  0.9626109600067139,\n",
       "  0.9533348679542542,\n",
       "  0.9641475081443787]}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_history.history #Return Dictionary of history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "e992c849-ecc8-4c91-bbf8-6111faf94d54",
   "metadata": {},
   "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)\n",
    "1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "2e8b4c17-6f02-4ccc-a69c-93d216bc0ecc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    }
   ],
   "source": [
    "print(training_history.history.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "3638f0cb-dd3c-483e-9a5b-bc7b62453e86",
   "metadata": {},
   "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.ylabel('Accuracy Result')\n",
    "plt.title('Visualization of Accuracy Result')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e30e4c2c-a336-4b7f-96e6-361a621f4928",
   "metadata": {},
   "source": [
    "### evaluation metrics\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "d8a903f3-af40-4787-8707-f330ffc3155c",
   "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": 30,
   "id": "d11350c2-9cd5-47a7-a0e8-f16e135321b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "17572/17572 [==============================] - 50s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = cnn.predict(test_set)\n",
    "predicted_categories = tf.argmax(y_pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "e5089f77-086b-4afa-a965-2f78fcbe893b",
   "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": 32,
   "id": "22d8a4f7-0117-48d1-a4c0-55a7b3116a63",
   "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": [
    "Y_true\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1e84a278-a3df-4703-b7a5-2e267a149a5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(17572,), dtype=int64, numpy=array([ 0,  0,  0, ..., 37, 37, 37], dtype=int64)>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_categories\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "83757cf8-c138-4606-a111-6b138be87041",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "cm = confusion_matrix(Y_true,predicted_categories)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "43d8ee15-bcb5-4c2b-9d04-43494d05adbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "class_name = validation_set.class_names\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ec3499c3-d959-4ef6-9482-b9ca51e7c9df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                    precision    recall  f1-score   support\n",
      "\n",
      "                                Apple___Apple_scab       0.95      0.96      0.96       504\n",
      "                                 Apple___Black_rot       0.98      0.99      0.98       497\n",
      "                          Apple___Cedar_apple_rust       0.99      0.97      0.98       440\n",
      "                                   Apple___healthy       0.98      0.94      0.96       502\n",
      "                               Blueberry___healthy       0.99      0.97      0.98       454\n",
      "          Cherry_(including_sour)___Powdery_mildew       0.97      0.99      0.98       421\n",
      "                 Cherry_(including_sour)___healthy       0.96      0.99      0.98       456\n",
      "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot       0.91      0.94      0.92       410\n",
      "                       Corn_(maize)___Common_rust_       1.00      0.99      0.99       477\n",
      "               Corn_(maize)___Northern_Leaf_Blight       0.95      0.97      0.96       477\n",
      "                            Corn_(maize)___healthy       0.99      1.00      1.00       465\n",
      "                                 Grape___Black_rot       0.98      0.99      0.98       472\n",
      "                      Grape___Esca_(Black_Measles)       0.99      0.99      0.99       480\n",
      "        Grape___Leaf_blight_(Isariopsis_Leaf_Spot)       0.99      0.99      0.99       430\n",
      "                                   Grape___healthy       1.00      1.00      1.00       423\n",
      "          Orange___Haunglongbing_(Citrus_greening)       1.00      1.00      1.00       503\n",
      "                            Peach___Bacterial_spot       0.90      0.97      0.93       459\n",
      "                                   Peach___healthy       0.98      0.97      0.98       432\n",
      "                     Pepper,_bell___Bacterial_spot       0.93      0.98      0.95       478\n",
      "                            Pepper,_bell___healthy       1.00      0.89      0.94       497\n",
      "                             Potato___Early_blight       0.99      0.97      0.98       485\n",
      "                              Potato___Late_blight       0.96      0.94      0.95       485\n",
      "                                  Potato___healthy       0.95      0.96      0.96       456\n",
      "                               Raspberry___healthy       0.98      0.98      0.98       445\n",
      "                                 Soybean___healthy       0.96      0.97      0.97       505\n",
      "                           Squash___Powdery_mildew       0.98      0.99      0.98       434\n",
      "                          Strawberry___Leaf_scorch       1.00      0.97      0.98       444\n",
      "                              Strawberry___healthy       1.00      0.99      0.99       456\n",
      "                           Tomato___Bacterial_spot       0.96      0.98      0.97       425\n",
      "                             Tomato___Early_blight       0.95      0.86      0.90       480\n",
      "                              Tomato___Late_blight       0.94      0.86      0.90       463\n",
      "                                Tomato___Leaf_Mold       0.96      0.96      0.96       470\n",
      "                       Tomato___Septoria_leaf_spot       0.86      0.93      0.89       436\n",
      "     Tomato___Spider_mites Two-spotted_spider_mite       0.97      0.90      0.93       435\n",
      "                              Tomato___Target_Spot       0.93      0.92      0.92       457\n",
      "            Tomato___Tomato_Yellow_Leaf_Curl_Virus       0.99      0.99      0.99       490\n",
      "                      Tomato___Tomato_mosaic_virus       0.88      1.00      0.93       448\n",
      "                                  Tomato___healthy       0.98      1.00      0.99       481\n",
      "\n",
      "                                          accuracy                           0.96     17572\n",
      "                                         macro avg       0.96      0.96      0.96     17572\n",
      "                                      weighted avg       0.96      0.96      0.96     17572\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Precision Recall Fscore\n",
    "print(classification_report(Y_true,predicted_categories,target_names=class_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1cae7cb3-f7e4-46c8-82f6-b96ab2690515",
   "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,
   "id": "2393655f-88ad-420d-bae9-db12f25e5721",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}