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disease-identification-in-rabi-crops

this repository contains the codes of the different models which we used to tackle the problem(DISEASE IDENTIFICATION IN RABI CROPS) which was given to us as a part of our internship under leading India AI and Bennett University. the models which we used are :

• CNN • Vgg 16 • ResNET • DenseNET • AlexNET • Inception

Requirements : • import numpy as np • import pickle • import cv2 • from os import listdir • from sklearn.preprocessing import LabelBinarizer • from keras.models import Sequential • from keras.layers.normalization import BatchNormalization • from keras.layers.convolutional import Conv2D • from keras.layers.convolutional import MaxPooling2D • from keras.layers.core import Activation, Flatten, Dropout, Dense • from keras import backend as K • from keras.preprocessing.image import ImageDataGenerator • from keras.optimizers import Adam • from keras.preprocessing import image • from keras.preprocessing.image import img_to_array • from sklearn.preprocessing import MultiLabelBinarizer • from sklearn.model_selection import train_test_split • import matplotlib.pyplot as plt • from keras.models import Model • from keras.optimizers import Adam • from keras.layers import GlobalAveragePooling2D • from keras.layers import Dense • from keras.applications.inception_v3 import InceptionV3 • from keras.utils.np_utils import to_categorical • from keras.applications.vgg16 import VGG16 • from keras.applications.vgg16 import preprocess_input • import seaborn as sns • from keras.applications import DenseNet121

TO RUN THE FILES • TO ACCESS CNN FILE: RUN disease identification using CNN.py • TO ACCESS VGG16 FILE: RUN disease identification using VGG16.py • TO ACCESS INCEPTION FILE: RUN disease identification using Inception.py • TO ACCESS DENSENET FILE: RUN disease identification using DenseNET.py • TO ACCESS ALEXNET FILE: RUN disease identification using alexnet.py • TO ACCESS RESNET FILE: RUN disease identification using resnet.py

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