PlantDisease_Train.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dREw2-7rmscv"
      },
      "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": 33,
      "metadata": {
        "id": "4JsejDm3uxTS"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import models, utils, optimizers\n",
        "from tensorflow.keras.layers import Conv2D, MaxPool2D, Dropout, Flatten, Dense\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import os\n",
        "import json\n",
        "from zipfile import ZipFile\n",
        "from PIL import Image\n",
        "from IPython.display import HTML\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vpswsvunmscy",
        "outputId": "6c33a9db-fdee-4c04-b299-85461d25519b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Downloading new-plant-diseases-dataset.zip to /content\n",
            "100% 2.69G/2.70G [00:31<00:00, 89.2MB/s]\n",
            "100% 2.70G/2.70G [00:31<00:00, 91.9MB/s]\n"
          ]
        }
      ],
      "source": [
        "kaggle_credentails = json.load(open(\"kaggle.json\"))\n",
        "\n",
        "os.environ[\"KAGGLE_USERNAME\"] = kaggle_credentails[\"username\"]\n",
        "os.environ[\"KAGGLE_KEY\"] = kaggle_credentails[\"key\"]\n",
        "\n",
        "!kaggle datasets download -d shaharshh/plant-disease-detection-dataset\n",
        "\n",
        "with ZipFile(\"plant-disease-detection-dataset.zip\", 'r') as zip_ref:\n",
        "    zip_ref.extractall()\n",
        "\n",
        "!rm  \"plant-disease-detection-dataset.zip\"\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i2w52yYrvTu9"
      },
      "source": [
        "## Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vU3NlcKCwlIZ"
      },
      "source": [
        "### Training Image preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kjoa0oUHvQAv",
        "outputId": "89fbd45a-7693-4fdf-89cd-679d2ee14405"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 70295 files belonging to 38 classes.\n"
          ]
        }
      ],
      "source": [
        "training_set = 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",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nK9Ev76o0mdJ"
      },
      "source": [
        "### Validation Image Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tocls4IowkNB",
        "outputId": "d2b9ae47-d1f3-4652-ee77-69c7ee1fff35"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 17572 files belonging to 38 classes.\n"
          ]
        }
      ],
      "source": [
        "validation_set = 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",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xzMowxZwcLRZ",
        "outputId": "3ac5273a-e646-4bad-ef9b-244f083648ff"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Apple___Apple_scab',\n",
              " 'Apple___Black_rot',\n",
              " 'Apple___Cedar_apple_rust',\n",
              " 'Apple___healthy',\n",
              " 'Blueberry___healthy',\n",
              " 'Cherry_(including_sour)___Powdery_mildew',\n",
              " 'Cherry_(including_sour)___healthy',\n",
              " 'Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot',\n",
              " 'Corn_(maize)___Common_rust_',\n",
              " 'Corn_(maize)___Northern_Leaf_Blight',\n",
              " 'Corn_(maize)___healthy',\n",
              " 'Grape___Black_rot',\n",
              " 'Grape___Esca_(Black_Measles)',\n",
              " 'Grape___Leaf_blight_(Isariopsis_Leaf_Spot)',\n",
              " 'Grape___healthy',\n",
              " 'Orange___Haunglongbing_(Citrus_greening)',\n",
              " 'Peach___Bacterial_spot',\n",
              " 'Peach___healthy',\n",
              " 'Pepper,_bell___Bacterial_spot',\n",
              " 'Pepper,_bell___healthy',\n",
              " 'Potato___Early_blight',\n",
              " 'Potato___Late_blight',\n",
              " 'Potato___healthy',\n",
              " 'Raspberry___healthy',\n",
              " 'Soybean___healthy',\n",
              " 'Squash___Powdery_mildew',\n",
              " 'Strawberry___Leaf_scorch',\n",
              " 'Strawberry___healthy',\n",
              " 'Tomato___Bacterial_spot',\n",
              " 'Tomato___Early_blight',\n",
              " 'Tomato___Late_blight',\n",
              " 'Tomato___Leaf_Mold',\n",
              " 'Tomato___Septoria_leaf_spot',\n",
              " 'Tomato___Spider_mites Two-spotted_spider_mite',\n",
              " 'Tomato___Target_Spot',\n",
              " 'Tomato___Tomato_Yellow_Leaf_Curl_Virus',\n",
              " 'Tomato___Tomato_mosaic_virus',\n",
              " 'Tomato___healthy']"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "class_names = training_set.class_names\n",
        "class_names\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OQ_TcbD7r2Cw"
      },
      "source": [
        "## Building Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "cUMiJrPvsBTU"
      },
      "outputs": [],
      "source": [
        "cnn = models.Sequential()\n",
        "\n",
        "cnn.add(Conv2D(filters=32,kernel_size=3,padding='same',activation='relu',input_shape=[128,128,3]))\n",
        "cnn.add(MaxPool2D(pool_size=2,strides=2))\n",
        "cnn.add(Dropout(0.25))\n",
        "\n",
        "cnn.add(Conv2D(filters=64,kernel_size=3,padding='same',activation='relu'))\n",
        "cnn.add(MaxPool2D(pool_size=2,strides=2))\n",
        "\n",
        "cnn.add(Conv2D(filters=64,kernel_size=3,padding='same',activation='relu'))\n",
        "cnn.add(MaxPool2D(pool_size=2,strides=2))\n",
        "cnn.add(Dropout(0.25))\n",
        "\n",
        "cnn.add(Conv2D(filters=64,kernel_size=3,padding='same',activation='relu'))\n",
        "cnn.add(MaxPool2D(pool_size=2,strides=2))\n",
        "\n",
        "cnn.add(Conv2D(filters=64,kernel_size=3,padding='same',activation='relu'))\n",
        "cnn.add(MaxPool2D(pool_size=2,strides=2))\n",
        "cnn.add(Dropout(0.25))\n",
        "\n",
        "cnn.add(Flatten())\n",
        "cnn.add(Dense(units=1500,activation='relu'))\n",
        "cnn.add(Dropout(0.4))\n",
        "\n",
        "cnn.add(Dense(units=38,activation='softmax'))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "yAcyiEfVvVzw"
      },
      "outputs": [],
      "source": [
        "cnn.compile(optimizer=optimizers.Adam(\n",
        "    learning_rate=0.0001),loss='categorical_crossentropy',metrics=['accuracy'])\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X0MROJsdlC5Z",
        "outputId": "9391f2d8-0c39-4503-97e4-47e21e9e235e"
      },
      "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",
            " max_pooling2d (MaxPooling2  (None, 64, 64, 32)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 64, 64, 32)        0         \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 64, 64, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 32, 32, 64)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 32, 32, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPoolin  (None, 16, 16, 64)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 16, 16, 64)        0         \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 16, 16, 64)        36928     \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPoolin  (None, 8, 8, 64)          0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 8, 8, 64)          36928     \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPoolin  (None, 4, 4, 64)          0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 4, 4, 64)          0         \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 1024)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1500)              1537500   \n",
            "                                                                 \n",
            " dropout_3 (Dropout)         (None, 1500)              0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 38)                57038     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1724714 (6.58 MB)\n",
            "Trainable params: 1724714 (6.58 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "cnn.summary()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K3W4dNUrvxIw",
        "outputId": "59fafbe2-7ebc-40b5-824a-ecfda9b2be7d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "2197/2197 [==============================] - 109s 46ms/step - loss: 3.0623 - accuracy: 0.2346 - val_loss: 1.9025 - val_accuracy: 0.4936\n",
            "Epoch 2/10\n",
            "2197/2197 [==============================] - 98s 44ms/step - loss: 1.2859 - accuracy: 0.6128 - val_loss: 0.9892 - val_accuracy: 0.7251\n",
            "Epoch 3/10\n",
            "2197/2197 [==============================] - 79s 36ms/step - loss: 0.7981 - accuracy: 0.7539 - val_loss: 0.5564 - val_accuracy: 0.8449\n",
            "Epoch 4/10\n",
            "2197/2197 [==============================] - 87s 39ms/step - loss: 0.5710 - accuracy: 0.8209 - val_loss: 0.4645 - val_accuracy: 0.8573\n",
            "Epoch 5/10\n",
            "2197/2197 [==============================] - 93s 42ms/step - loss: 0.4322 - accuracy: 0.8610 - val_loss: 0.3923 - val_accuracy: 0.8786\n",
            "Epoch 6/10\n",
            "2197/2197 [==============================] - 83s 38ms/step - loss: 0.3476 - accuracy: 0.8865 - val_loss: 0.2675 - val_accuracy: 0.9187\n",
            "Epoch 7/10\n",
            "2197/2197 [==============================] - 77s 35ms/step - loss: 0.2853 - accuracy: 0.9067 - val_loss: 0.2383 - val_accuracy: 0.9264\n",
            "Epoch 8/10\n",
            "2197/2197 [==============================] - 79s 36ms/step - loss: 0.2456 - accuracy: 0.9186 - val_loss: 0.2320 - val_accuracy: 0.9267\n",
            "Epoch 9/10\n",
            "2197/2197 [==============================] - 85s 39ms/step - loss: 0.2130 - accuracy: 0.9287 - val_loss: 0.1899 - val_accuracy: 0.9391\n",
            "Epoch 10/10\n",
            "2197/2197 [==============================] - 87s 40ms/step - loss: 0.1872 - accuracy: 0.9379 - val_loss: 0.1883 - val_accuracy: 0.9389\n"
          ]
        }
      ],
      "source": [
        "training_history = cnn.fit(x=training_set,validation_data=validation_set,epochs=10)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "19E3mwj1phoW"
      },
      "source": [
        "## Evaluating Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iY6o3uxjpgl5",
        "outputId": "9d20e407-00fc-4593-95f4-3ddc90cc7d8c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2197/2197 [==============================] - 55s 25ms/step - loss: 0.1183 - accuracy: 0.9660\n",
            "Training accuracy: 0.9659719467163086\n"
          ]
        }
      ],
      "source": [
        "# Training Accuracy\n",
        "train_loss, train_acc = cnn.evaluate(training_set)\n",
        "print('Training accuracy:', train_acc)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5SaQgd7bpgwl",
        "outputId": "81441513-97a1-4b28-81e9-7822ecd26623"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "550/550 [==============================] - 12s 22ms/step - loss: 0.1883 - accuracy: 0.9389\n",
            "Validation accuracy: 0.9389369487762451\n"
          ]
        }
      ],
      "source": [
        "# Validation Accuracy\n",
        "val_loss, val_acc = cnn.evaluate(validation_set)\n",
        "print('Validation accuracy:', val_acc)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qxp0xsjVBGec"
      },
      "source": [
        "### Saving Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EEUrBzCrxKt_",
        "outputId": "30dec64f-1606-47b5-e017-636059fa798e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ],
      "source": [
        "cnn.save('trained_plant_disease_model.h5')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BM-sILNMBwh1",
        "outputId": "69c0c84d-ceb8-4c27-8ae6-75274cb210d7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'loss': [3.062305212020874,\n",
              "  1.28593111038208,\n",
              "  0.7981175780296326,\n",
              "  0.5709856152534485,\n",
              "  0.4321916103363037,\n",
              "  0.34760773181915283,\n",
              "  0.28534531593322754,\n",
              "  0.24562574923038483,\n",
              "  0.21301718056201935,\n",
              "  0.18715494871139526],\n",
              " 'accuracy': [0.2346397340297699,\n",
              "  0.6127747297286987,\n",
              "  0.7538800835609436,\n",
              "  0.8209260702133179,\n",
              "  0.8609573841094971,\n",
              "  0.8865352869033813,\n",
              "  0.906735897064209,\n",
              "  0.9186002016067505,\n",
              "  0.9287146925926208,\n",
              "  0.9379187822341919],\n",
              " 'val_loss': [1.9025198221206665,\n",
              "  0.9891754388809204,\n",
              "  0.556390106678009,\n",
              "  0.4644668698310852,\n",
              "  0.39225131273269653,\n",
              "  0.2675146162509918,\n",
              "  0.23834316432476044,\n",
              "  0.2319595366716385,\n",
              "  0.18992629647254944,\n",
              "  0.18827539682388306],\n",
              " 'val_accuracy': [0.4935693144798279,\n",
              "  0.7250739932060242,\n",
              "  0.844923734664917,\n",
              "  0.8572729229927063,\n",
              "  0.8785567879676819,\n",
              "  0.9187343716621399,\n",
              "  0.9263601303100586,\n",
              "  0.926701545715332,\n",
              "  0.9390507340431213,\n",
              "  0.9389369487762451]}"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "training_history.history\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "GdF3Ig4ZBU9V"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "with open('training_hist.json','w') as f:\n",
        "  json.dump(training_history.history,f)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w8PnAYbuCNhu",
        "outputId": "43f6b4ed-c1e8-48cf-82a8-9932dadd889a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
          ]
        }
      ],
      "source": [
        "print(training_history.history.keys())\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6JlOhlUheI_R"
      },
      "source": [
        "## Visualising results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "sR9aA3sWc7Re"
      },
      "outputs": [],
      "source": [
        "acc = training_history.history['accuracy']\n",
        "val_acc = training_history.history['val_accuracy']\n",
        "loss = training_history.history['loss']\n",
        "val_loss = training_history.history['val_loss']\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "An_GFSnqcxP3",
        "outputId": "defda640-0863-4ed2-8a2a-b6603e789c6a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1500x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(15, 8))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(range(10), acc, label='Training Accuracy')\n",
        "plt.plot(range(10), val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(range(10), loss, label='Training Loss')\n",
        "plt.plot(range(10), val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tqSTPMZXmsc5"
      },
      "source": [
        "## Model evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "TRFezv3Nmsc5"
      },
      "outputs": [],
      "source": [
        "class_name = validation_set.class_names\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPikQKKzywFx",
        "outputId": "53da806c-ee1c-4f95-f769-0b5ba536e808"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "38"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(class_name)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dfFIBpqamsc5",
        "outputId": "0f0223f0-31ce-4405-b588-8c1adea3d0db"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Found 17572 files belonging to 38 classes.\n"
          ]
        }
      ],
      "source": [
        "test_set = utils.image_dataset_from_directory(\n",
        "    'test',\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=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",
        ")\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FtI0wQfLmsc6",
        "outputId": "cc2cbef6-38b1-4f81-c96f-5b4d0ce54aa0"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(17572,), dtype=int64, numpy=array([ 0,  0,  0, ..., 37, 37, 37])>"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Predicted\n",
        "\n",
        "y_pred = cnn.predict(test_set)\n",
        "predicted_categories = tf.argmax(y_pred, axis=1)\n",
        "predicted_categories\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UdwYVLM-msc6",
        "outputId": "b74827e1-177a-4634-d29e-c2acb5aa75a5"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(17572,), dtype=int64, numpy=array([ 0,  0,  0, ..., 37, 37, 37])>"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Actual\n",
        "\n",
        "true_categories = tf.concat([y for x, y in test_set], axis=0)\n",
        "Y_true = tf.argmax(true_categories, axis=1)\n",
        "Y_true\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZkiPaEfhw6S7",
        "outputId": "4af93fec-c184-4c3a-997a-33a9ef610ef7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "33"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(Y_true)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ilg6kPHZxJXU",
        "outputId": "d8503d49-467f-4aea-c10d-a0b203ca6ba9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "33"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "len(predicted_categories)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GxsDcr_3jJh6"
      },
      "source": [
        "## Precision Recall Fscore"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "m7j5gcSGmsc6"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix,classification_report\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s0Nv0BhXmsc6",
        "outputId": "0245273d-0f84-4df7-c025-6638cee78485"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "                                                    precision    recall  f1-score   support\n",
            "\n",
            "                                Apple___Apple_scab       0.97      0.91      0.94       504\n",
            "                                 Apple___Black_rot       0.94      0.99      0.96       497\n",
            "                          Apple___Cedar_apple_rust       0.89      0.99      0.94       440\n",
            "                                   Apple___healthy       0.91      0.96      0.93       502\n",
            "                               Blueberry___healthy       0.96      0.94      0.95       454\n",
            "          Cherry_(including_sour)___Powdery_mildew       1.00      0.96      0.98       421\n",
            "                 Cherry_(including_sour)___healthy       0.95      0.89      0.92       456\n",
            "Corn_(maize)___Cercospora_leaf_spot Gray_leaf_spot       0.89      0.91      0.90       410\n",
            "                       Corn_(maize)___Common_rust_       0.99      0.99      0.99       477\n",
            "               Corn_(maize)___Northern_Leaf_Blight       0.93      0.96      0.94       477\n",
            "                            Corn_(maize)___healthy       0.97      1.00      0.99       465\n",
            "                                 Grape___Black_rot       0.93      0.94      0.94       472\n",
            "                      Grape___Esca_(Black_Measles)       0.96      0.97      0.97       480\n",
            "        Grape___Leaf_blight_(Isariopsis_Leaf_Spot)       0.97      0.97      0.97       430\n",
            "                                   Grape___healthy       0.91      0.99      0.95       423\n",
            "          Orange___Haunglongbing_(Citrus_greening)       0.93      0.98      0.95       503\n",
            "                            Peach___Bacterial_spot       0.91      0.89      0.90       459\n",
            "                                   Peach___healthy       0.95      0.99      0.97       432\n",
            "                     Pepper,_bell___Bacterial_spot       0.94      0.93      0.94       478\n",
            "                            Pepper,_bell___healthy       0.96      0.84      0.89       497\n",
            "                             Potato___Early_blight       0.93      0.98      0.96       485\n",
            "                              Potato___Late_blight       0.97      0.84      0.90       485\n",
            "                                  Potato___healthy       0.95      0.95      0.95       456\n",
            "                               Raspberry___healthy       0.94      0.97      0.95       445\n",
            "                                 Soybean___healthy       0.84      0.99      0.91       505\n",
            "                           Squash___Powdery_mildew       0.98      0.97      0.98       434\n",
            "                          Strawberry___Leaf_scorch       0.96      0.95      0.96       444\n",
            "                              Strawberry___healthy       1.00      0.98      0.99       456\n",
            "                           Tomato___Bacterial_spot       0.85      0.95      0.90       425\n",
            "                             Tomato___Early_blight       0.85      0.82      0.84       480\n",
            "                              Tomato___Late_blight       0.93      0.82      0.87       463\n",
            "                                Tomato___Leaf_Mold       0.98      0.93      0.95       470\n",
            "                       Tomato___Septoria_leaf_spot       0.93      0.76      0.84       436\n",
            "     Tomato___Spider_mites Two-spotted_spider_mite       0.93      0.95      0.94       435\n",
            "                              Tomato___Target_Spot       0.91      0.86      0.88       457\n",
            "            Tomato___Tomato_Yellow_Leaf_Curl_Virus       0.97      0.98      0.97       490\n",
            "                      Tomato___Tomato_mosaic_virus       0.99      0.96      0.97       448\n",
            "                                  Tomato___healthy       0.95      1.00      0.97       481\n",
            "\n",
            "                                          accuracy                           0.94     17572\n",
            "                                         macro avg       0.94      0.94      0.94     17572\n",
            "                                      weighted avg       0.94      0.94      0.94     17572\n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Precision Recall Fscore\n",
        "print(classification_report(Y_true, predicted_categories, target_names=class_name))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "aAAD4lXJiNds",
        "outputId": "848c89d1-4589-43e0-f5b0-dcc4f9fab223"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "<img src=\"https://www.researchgate.net/publication/336402347/figure/fig3/AS:812472659349505@1570719985505/Calculation-of-Precision-Recall-and-Accuracy-in-the-confusion-matrix.ppm\" />\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "execution_count": 34,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "HTML(\"\"\"\n",
        "<img src=\"https://www.researchgate.net/publication/336402347/figure/fig3/AS:812472659349505@1570719985505/Calculation-of-Precision-Recall-and-Accuracy-in-the-confusion-matrix.ppm\" />\n",
        "\"\"\")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aiET_fKdmsc7"
      },
      "source": [
        "### Confusion Matrix Visualization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "qAAhuKABjkgW"
      },
      "outputs": [],
      "source": [
        "cm = confusion_matrix(Y_true, predicted_categories)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "i21EGsO4msc7",
        "outputId": "dc126dd7-686b-4f80-b68b-58117d48911c"
      },
      "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\": 11})\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()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "EYeMux3dmsc7"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.11.8"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}