Object detection and recognition is a demanding work belonging to the field of computer vision. Objects in the images are detected and recognized using machine learning models when trained on a sufficient number of available images. When applying deep learning models in this task when we have a large number of training images, the accuracy of object recognition is improved. This concept motivates us in developing such a model which can recognize a fruit and predicts its name. There may be a variety of applications of fruit recognition in agricultural work when we are to recognize thousands of fruit images in a less amount of time. It can also be applied in automating the billing process at a fruit shop where the model can recognize the fruit and calculate its price by multiplying with weight.
In this article, we will recognize the fruit where the Convolutional Neural Network will predict the name of the fruit given its image. We will train the network in a supervised manner where images of the fruits will be the input to the network and labels of the fruits will be the output of the network. After successful training, the CNN model will be able to correctly predict the label of the fruit.
The Data Set
The data set used in this article is taken from ‘Fruit Images for Object Detection’ dataset that is publicly available on Kaggle. This is a small data set consisting of 240 training images and 60 test images. All the images belong to the three types of fruits – Apple, Banana and Orange.
Implementing Fruit Recognition
This code was implemented in Google Colab and the .py file was downloaded.
# -*- coding: utf-8 -*- """Fruit.ipynb Automatically generated by Colaboratory. Original file is located at https://colab.research.google.com/drive/1aIDTOiVdKbSrSHqDxxxxxxxxxxxxxxxx """
The dataset was uploaded to google drive and the drive was mounted with the Colab notebook. The below code snippets are used for that purpose.
#Setting google drive as a directory for dataset from google.colab import drive drive.mount('/content/gdrive')
The directory of the Fruit image dataset
dir_path = "gdrive/My Drive/Dataset/Fruit Images/"
Importing some required libraries
#Importing Library import numpy as np import pandas as pd import cv2 import os from PIL import Image
Here, we will check the files in the directory
#Checking the directory import os for dirname, _, filenames in os.walk(dir_path): for filename in filenames: print(os.path.join(dirname, filename))
We can verify the contents of the directory in this way. Using the below code snippet, we will get all the images and their labels. These labels will be obtained from the names of the image files.
images =  labels =  train_path = 'gdrive/My Drive/Dataset/Fruit Images/train_zip/train' for filename in os.listdir('gdrive/My Drive/Dataset/Fruit Images/train_zip/train'): if filename.split('.') = = 'jpg': img = cv2.imread(os.path.join(train_path,filename)) arr = Image.fromarray(img,'RGB') img_arr = arr.resize((50,50)) labels.append(filename.split('_')) images.append(np.array(img_arr))
After obtaining all the labels, we will print them.
#Image Labels np.unique(labels)
All the labels in the text form stored in the labels array will be encoded by label encoding to transform them as the output labels.
from sklearn.preprocessing import LabelEncoder lb_encod = LabelEncoder() labels = pd.DataFrame(labels) labels = lb_encod.fit_transform(labels) labels #Visualizing image import matplotlib.pyplot as plt figure = plt.figure(figsize = (8,8)) ax = figure.add_subplot(121) ax.imshow(images) bx = figure.add_subplot(122) bx.imshow(images) plt.show() #In the next step, we will preprocess the image data
#Saving the image array and corresponding labels images = np.array(images) np.save("image",images) np.save("labels",labels) #Loading the images and labels that we have saved above image = np.load("image.npy",allow_pickle = True) labels = np.load("labels.npy",allow_pickle = True) img_shape = np.arange(image.shape) np.random.shuffle(img_shape) image = image[img_shape] labels = labels[img_shape]
Now, we will define the train and the test data set.
num_classes = len(np.unique(labels)) len_data = len(image)
x_train, x_test = image[(int)(0.1*len_data):],image[:(int)(0.1*len_data)] y_train,y_test = labels[(int)(0.1*len_data):],labels[:(int)(0.1*len_data)] import keras y_train = keras.utils.to_categorical(y_train,num_classes) y_test = keras.utils.to_categorical(y_test,num_classes)
Convolutional Neural Network
After defining the training and test sets, we will define and train the convolutional neural network model. As a kernel regularization, we will use the L2 regularization method.
from keras.models import Sequential from keras.layers import Dense,Conv2D,MaxPooling2D,Dropout,Flatten,MaxPool2D from keras.optimizers import RMSprop,Adam from keras.layers import Activation, Convolution2D, Dropout, Conv2D,AveragePooling2D, BatchNormalization,Flatten,GlobalAveragePooling2D from keras import layers from keras.regularizers import l2 from keras.callbacks import ModelCheckpoint,ReduceLROnPlateau l2_reg = 0.001 opt = Adam(lr = 0.001) #Defining the CNN Model cnn_model = Sequential() cnn_model.add(Conv2D(filters = 32, kernel_size = (2,2), input_shape = (50,50, 3), activation = 'relu',kernel_regularizer = l2(l2_reg))) cnn_model.add(MaxPool2D(pool_size = (2,2))) cnn_model.add(Conv2D(filters = 64, kernel_size = (2,2), activation = 'relu',kernel_regularizer = l2(l2_reg))) cnn_model.add(MaxPool2D(pool_size = (2,2))) cnn_model.add(Conv2D(filters = 128, kernel_size = (2,2), activation = 'relu',kernel_regularizer = l2(l2_reg))) cnn_model.add(MaxPool2D(pool_size = (2,2))) cnn_model.add(Dropout(0.1)) cnn_model.add(Flatten()) cnn_model.add(Dense(64, activation = 'relu')) cnn_model.add(Dense(16, activation = 'relu')) cnn_model.add(Dense(4, activation = 'softmax')) #CNN Model Summary cnn_model.summary() #Compiling the model cnn_model.compile(loss = 'categorical_crossentropy', optimizer = opt, metrics = ['accuracy']) #Training the CNN Model
file1 = ‘weights.hdf5’ checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min') history = model.fit(x_train,y_train,batch_size = 128,epochs = 110,verbose = 1,validation_split = 0.33) #Check the performance scores = cnn_model.evaluate(x_test, y_test, verbose = 1) print('Test loss:', scores) print('Test accuracy:', scores) #Visualize the performance figure = plt.figure(figsize = (10,5)) ax = figure.add_subplot(121) ax.plot(history.history['accuracy']) ax.plot(history.history['val_accuracy']) ax.legend(['Training Accuracy','Val Accuracy']) bx = figure.add_subplot(122) bx.plot(history.history['loss']) bx.plot(history.history['val_loss']) bx.legend(['Training Loss','Val Loss'])
After the successful training, we will test the model in predicting the class labels for the fruit images.
#Test test_path = 'gdrive/My Drive/Dataset/Fruit Images/test_zip/test' t_labels =  t_images =  for filename in os.listdir('gdrive/My Drive/Dataset/Fruit Images/test_zip/test'): if filename.split('.') = = 'jpg': img = cv2.imread(os.path.join(test_path,filename)) arr = Image.fromarray(img,'RGB') img_arr = arr.resize((50,50)) t_labels.append(filename.split('_')) t_images.append(np.array(img_arr)) test_images = np.array(test_images) np.save("test_image",test_images) test_image = np.load("image.npy",allow_pickle = True) pred = np.argmax(model.predict(test_image),axis = 1) prediction = la.inverse_transform(pred) test_image = np.expand_dims(test_image,axis = 0) pred_test = np.argmax(model.predict(test_image),axis = 1) prediction_test = la.inverse_transform(pred_test) print(prediction_test) plt.imshow(test_images)
You can run this test for more number of fruit images and check whether the predicted label is correct or not. As we have received very good accuracy, the CNN model will certainly predict the correct label. We can run the entire process by tuning the parameters to get better performance if we get some incorrect labels. This article motivates us to extend this work in recognizing more classes of foods and vegetables.
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Vaibhav Kumar has experience in the field of Data Science and Machine Learning, including research and development. He holds a PhD degree in which he has worked in the area of Deep Learning for Stock Market Prediction. He has published/presented more than 15 research papers in international journals and conferences. He has an interest in writing articles related to data science, machine learning and artificial intelligence.