plant-disease-detection-model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Convolution2D, Conv2D\n",
    "from keras.layers import MaxPooling2D\n",
    "from keras.layers import Activation, Flatten\n",
    "from keras.layers import Dense, Dropout\n",
    "from keras.optimizers import SGD\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_5\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_8 (Conv2D)            (None, 256, 256, 32)      896       \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 256, 256, 32)      0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_1 (Batch (None, 256, 256, 32)      128       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_7 (MaxPooling2 (None, 85, 85, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 85, 85, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_9 (Conv2D)            (None, 85, 85, 64)        18496     \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 85, 85, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_2 (Batch (None, 85, 85, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv2d_10 (Conv2D)           (None, 85, 85, 64)        36928     \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 85, 85, 64)        0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_3 (Batch (None, 85, 85, 64)        256       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_8 (MaxPooling2 (None, 42, 42, 64)        0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 42, 42, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_11 (Conv2D)           (None, 42, 42, 128)       73856     \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 42, 42, 128)       0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_4 (Batch (None, 42, 42, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv2d_12 (Conv2D)           (None, 42, 42, 128)       147584    \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 42, 42, 128)       0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_5 (Batch (None, 42, 42, 128)       512       \n",
      "_________________________________________________________________\n",
      "max_pooling2d_9 (MaxPooling2 (None, 21, 21, 128)       0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 21, 21, 128)       0         \n",
      "_________________________________________________________________\n",
      "flatten_4 (Flatten)          (None, 56448)             0         \n",
      "_________________________________________________________________\n",
      "dense_7 (Dense)              (None, 1024)              57803776  \n",
      "_________________________________________________________________\n",
      "activation_7 (Activation)    (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "batch_normalization_6 (Batch (None, 1024)              4096      \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "dense_8 (Dense)              (None, 16)                16400     \n",
      "_________________________________________________________________\n",
      "activation_8 (Activation)    (None, 16)                0         \n",
      "=================================================================\n",
      "Total params: 58,103,696\n",
      "Trainable params: 58,100,816\n",
      "Non-trainable params: 2,880\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "\n",
    "chanDim=-1\n",
    "\n",
    "model.add(Conv2D(32, (3, 3), padding=\"same\",input_shape=(256,256,3)))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(BatchNormalization(axis=chanDim))\n",
    "model.add(MaxPooling2D(pool_size=(3, 3)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Conv2D(64, (3, 3), padding=\"same\"))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(BatchNormalization(axis=chanDim))\n",
    "model.add(Conv2D(64, (3, 3), padding=\"same\"))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(BatchNormalization(axis=chanDim))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Conv2D(128, (3, 3), padding=\"same\"))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(BatchNormalization(axis=chanDim))\n",
    "model.add(Conv2D(128, (3, 3), padding=\"same\"))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(BatchNormalization(axis=chanDim))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(1024))\n",
    "model.add(Activation(\"relu\"))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(16))\n",
    "model.add(Activation(\"softmax\"))\n",
    "\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_folders.ratio('input_folder', output=\"output\", seed=1337, ratio=(.8, .1, .1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 41276 images belonging to 16 classes.\n",
      "Found 0 images belonging to 16 classes.\n"
     ]
    }
   ],
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_datagen = ImageDataGenerator(\n",
    "        rescale=1./255,\n",
    "        shear_range=0.2,\n",
    "        zoom_range=0.2,\n",
    "        horizontal_flip=True)\n",
    "\n",
    "\n",
    "\n",
    "training_set = train_datagen.flow_from_directory('dataset/PlantVillage',\n",
    "                                                 target_size=(256, 256),\n",
    "                                                 batch_size=32,\n",
    "                                                 class_mode='categorical',\n",
    "                                                subset='training')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] Loading images ...\n",
      "[INFO] Processing 0022d6b7-d47c-4ee2-ae9a-392a53f48647___JR_B.Spot 8964.JPG ...\n",
      "Error : [WinError 267] The directory name is invalid: 'D:/plant-disease-identifier/dataset/PlantVillage/Pepper__bell___Bacterial_spot/0022d6b7-d47c-4ee2-ae9a-392a53f48647___JR_B.Spot 8964.JPG/'\n"
     ]
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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