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FCAQI HOME PAGE   This is a starting page of our application it have some functionality like Services, Portfolio, Team and About Us. It has a slider which show the few activities. When user visits our site this home page will be viewed and for classification free trial and full packaged system is purely for authenticated user have an access of this system. When user click “Services”, page will be scrolled down at Services section and where use can select one from Free Trial or Full Packaged System. About Us Section Contains our System Description and Objective also some guided material about our system. FCAQI SERVICES This is Important and Core Section of Our System which provides firstly our System Model Phases from Training to Testing. In this section Research Work Button Contains links that redirect user to our research work page. Lastly two button for Free Trial and Full Packaged where user choice select one of them, if he/she wants to make a free...

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#Including Libraries import numpy as np from PIL import Image from sklearn.preprocessing import LabelEncoder import os #Model Trained on These Types of Fruits cause of Limited GPU classes = {"acerolas":1,"apples ":2,"apricots":3,"avocados":4,"bananas":5} #Reading Image from Dataset Path def read_image(path,image): img= Image.open(os.path.join(path,image)) img=img.resize((224,224),Image.ANTIALIAS) img=img.convert("RGB") img=np.array(img) img=img.astype('float32') return img #One Hot Encoding eg: [1 0 0 0 0] def one_hot_encode(labels): n_labels=len(labels) n_unique_labels=len(np.unique(labels)) one_hot_encode=np.zeros((n_labels,n_unique_labels)) one_hot_encode[np.arange(n_labels),labels]=1 return one_hot_encode #Reading Directory by Directory DataSet Images and Preprocess them and Return In Memory Numpy Array def read_dataset(cw_dir): ...

### Deep Learning Libraries from PIL import Image import io import os import tensorflow as tf from sklearn import cross_validation, metrics from train_dataset import read_dataset, classes, read_prediction_images, test_demo import numpy as np import sys import base64, datetime import xml #Setting Batch Size for Model Training batch_size=25 x_train=[] #Experiment Analysis #----------------------------------------------------------- **************************------------------------------------- def plot_images(expected,predicted): print("confusion Matrix:\n ",metrics.confusion_matrix(expected,predicted)) def save_XMLRESULT(pred_cls): for p in pred_cls: pass #*************************************************************************************************************************** #Deep Convolutional Neural Network Model (VGG-16) def cnn_model_fn(features,labels,mode): input_layer=tf.reshape(features["x"],[batch_size,...

Food Classification and Quality Inspection   System using Deep Convolutional Neural Network The proposed system performs computer vision task called Food Classification and Quality Inspection in different public/private Food Seller Purchaser Organizations. Food Classification and Quality Inspection system is web application using pre-trained VGG-16 Deep learning classification model. This system is a cross platform which is built on different tools and technologies. This system is divided into two main modules:- Food Classifier Food Classification plays a vital role for the Food Seller and Buyers Organization. In our Ensemble of Deep networks for the classification of Food from Fruit images using the FID30 DataSet. These images are taken at different times of the day and different times of the year. Additionally, the images are taken from a different angle, scale and resolution. All individual networks takes preprocessed images of 224 x 224 x 3 and batch norma...

In this era, number of Organizations are increasing their businesses using Artificial Intelligence Services to overcome the manual and time consuming tasks for better performance with Quality. So, food Classification and Quality Inspection is also a manual and time consuming task for food Sellers and Pur-chasers Organizations, to address this issue many people propose different systems that can automate such Classification and Inspection process. Automated food classification is a procedure in which foods are classified in several categories in order to generate automated reports about the food categorize. Food Quality Inspection plays critical role in these systems. To automate such Classification and Quality Inspection we propose a system that consist of two main modules, Food Classification and its Quality Checker. For Food classification we design/ trained a model on 30 types of fruit classes including (Acerolas, Apples, Apricots, Avocados, Bananas, Blackberries, Blueberries, Can...