Fruit Classification and Quality Prediction using Deep Learning Methods

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 09 Issue: 01 | Jan 2022 www.irjet.net p-ISSN: 2395-0072
© 2022, IRJET | Impact Factor value: 7.529 | ISO 9001:2008 Certified Journal | Page 778
Fruit Classification and Quality Prediction using Deep Learning
Methods
Shivani R1, Tanushri2
1Student, CEG, Anna University, Chennai, India
2Student, CEG, Anna University, Chennai, India
---------------------------------------------------------------------***----------------------------------------------------------------------
Abstract - In the agriculture industry, a lot of manpower
and resources are wasted in manually classifying the fruits
and vegetables and predicting their quality. A traditional
method for fruit classification is manual sorting which is
time-consuming and labor-intensive and hence requires
human presence. Image processing and computer vision can
be incorporated to automate these tasks.
In this paper, we aim to develop an automated system with
the help of deep learning models and image processing
techniques for accurate predictions. Various deep learning
convolutional neural network architectures such as VGG16,
Inception V3, ResNet Mobilenet were used to train the model
using the transfer learning approach. The model
performance and results were compared, and its real-world
implementation was studied. Different model performances
were evaluated, and Inception V3 seemed to be the most
accurate and effective model, with an accuracy of up to
98%. Outcomes of this study show that using the
Convolutional Neural Network model; Inception V3 gives
promising results compared to traditional machine learning
algorithms.
Key Words: Fruit Classification, Image Processing,
Deep Learning, Inception V3, Quality Prediction
1. INTRODUCTION
For around 58 percent of India's population, agriculture is
their major source of income. Agriculture, together with its
linked industries, is indisputably India's primary source of
income, particularly in the country's vast rural areas.
Agriculture's contribution to GDP has surpassed nearly
20% for the first time in 17 years, making it the only
bright point in GDP performance in 2020-21.
India is endowed with vast swaths of fertile land divided
into 15 agro-climatic zones, each with its own set of
weather conditions, soil types, and crop potential. The
varied climate of India assures the availability of a wide
range of fresh fruits and vegetables. After China, it is the
world's second-largest producer of fruits and vegetables.
As a result, the fruit sector plays a critical role in India's
economic growth and is an important component of the
food processing industry.
Fruits and vegetables are an essential element of a well-
balanced diet that help humans stay healthy. Vitamins,
minerals, and plant components are abundant in fruits and
vegetables. They also contain fiber which lowers
cholesterol levels and regulates blood sugar levels. There
are numerous fruit and vegetable kinds to choose from, as
well as multiple ways to prepare, cook, and serve them. A
fruit and vegetable-rich diet can help you avoid cancer,
diabetes, and cardiovascular disease.
Unlike digital technology, which has acquired traction,
agricultural biotechnology is still developing. Lack of low-
cost technology and equitable access has resulted in
productivity decline. Despite large-scale agricultural
mechanization in some regions of the nation, most
agricultural operations are still done by hand using
conventional and straightforward tools. Manual sorting is
carried out by farmers, which is time-consuming and
labor-intensive. It is critical to mechanize agricultural
processes to prevent labor waste and make farming more
convenient and efficient. Machinery is an essential
component of agricultural activities that are efficient and
timely. This paper aims to automate the tasks of
classification of fruits and vegetables and their quality
prediction.
2. LITERATURE REVIEW
A study proposed by Bhavini J. Samajpati aimed to detect
and classify apple fruit diseases. Segmentation of infected
apple fruit was carried out by the K-mean clustering
technique. Different features such as global color
histogram(GCH), color coherence vector(CCV) and local
binary pattern(LBP) were extracted. Upon testing images
with various classifiers, SVM proved to be the most
efficient in terms of accuracy and performance.
In [2], the fruit classification system is implemented using
the Support Vector Machine classifier. It involved the
extraction of statistical and texture features from wavelet
transforms.It consisted of three phases: preprocessing,
feature extraction and classification phase. In the feature
extraction phase, the statistical feature of color and
texture feature from wavelet transform were derived.
Winda Astuti[3] proposed an SVM-based fruit
classification system that used Fast Fourier Transform as
input. The system differentiated the fruit based on their
shapes.The results showed that the technique produced

better accuracy than the existing technique based on an
artificial neural network. The SVM also required less
training time than an artificial neural network..
The convolutional neural network approach was adopted
by Zaw Min Khaing [4] to achieve the task of fruit
detection and recognition.Alexnet architecture was used
to perform classification, and simulation was carried out
with the help of MATLAB.
A model by H. Muresan et al. [5] introduces the Fruits-360
dataset, which presently has 49561 photos of 74 different
types of fruit. They used deep learning to construct
software that can detect fruit from photographs. For
recognition, their suggested model uses CNN. Their major
goal was to report the findings of a numerical experiment
used to train a neural network to recognize fruits. Fruits-
360 comprises just single object fruits for both training
and testing. Therefore their system does not require any
detection. As a result of this flaw, the system is unsuitable
for real-time use. Only Fruits360 works better with their
recommended model.
3. CONVOLUTIONAL NEURAL NETWORKS
The term "deep learning" refers to a type of machine
learning in which data is processed via numerous layers to
extract increasingly higher-level characteristics.
It consists of different architectures which comprise
artificial neural networks.Convolutional Neural Networks
(ConvNet/CNN) are a type of Deep Learning technique
used in computer vision.The amount of pre-processing
required by a ConvNet is much less than that required by
other classification techniques.While in primitive
methods, filters are hand-engineered, with enough
training, ConvNets could learn these
filters/characteristics.CNN employs 2D convolutional
layers to combine learned features with input data,
making it ideally suited to processing 2D data like photos.
Because CNN eliminates the requirement for human
feature extraction, the characteristics necessary to
categorize the pictures do not need to be selected.
Convolutional neural networks have a better performance
than other types of neural networks with images, voice or
audio signal inputs. The convolutional layer, Pooling layer,
and Fully-connected (FC) layer are the three primary
types of layers.
CNN's most critical component is the convolutional layer.
The three components are input data, a filter, and a feature
map. A feature detector, sometimes known as a kernel or a
filter, detects features and looks for the existence of a
feature in the image's receptive fields. This method is
known as convolution. A CNN performs a Rectified Linear
Unit (ReLU) transformation to the feature map after each
convolution process, bringing nonlinearity into the model.
Pooling layers is a dimensionality reduction technique that
reduces the number of factors in the input.
The pooling process sweeps a filter across the whole input,
but this filter is weightless.
Max pooling is a type of pooling in which the filter picks
the pixel with the maximum value to transmit to the
output array as it traverses the input. This method is more
frequently employed than average pooling.
Average pooling is a type of pooling in which the filter
calculates the average value within the receptive field to
transmit to the output array as it traverses the input.
Each node in the output layer is connected directly to a
node in the preceding layer in the fully-connected layer.
This layer conducts categorization using the
characteristics retrieved by the preceding layers and their
associated filters. While convolutional and pooling layers
often employ ReLu functions, Fully Connected layers
typically employ a softmax activation function to
accurately categorize inputs, generating a probability
between 0 and 1.
4. INCEPTION V3
Inception v3 focuses on reducing computational power
consumption with modifications to prior Inception
designs. This concept was first proposed in the 2015
article Rethinking the Inception Architecture for Computer
Vision. Different model architectures such as VGG16,
ResNet 50, MobileNet, Inception v3 were implemented
using the Datasets, and their performances were
evaluated. Inception Networks have been demonstrated to
be more computationally efficient, both in terms of the
number of parameters created and the cost incurred
(memory, loss and accuracy). An inception network is a
type of deep neural network with an architectural design
consisting of repeating components referred to as
Inception modules.
The inception module has a Convolution layer
(1x1,3x3,5x5), Max pooling layer, Concatenation layer.
Fig-1:Inception Module

4.1 1 x 1 Convolution
The purpose of 1 x 1 convolution is to reduce the
dimensions of data passing through the network,
This gives the added advantage of increasing the
network's breadth and depth.
It acts as a channel collapser, allowing subsequent
procedures to be performed on a two-dimensional picture
rather than a costly three-dimensional one, significantly
reducing the number of operations required while
maintaining color recognition.
4.2 3 x 3 and 5 x 5 Convolutions
As a consequence of the varied convolutional filter sizes of
3 x 3 and 5 x 5 Convolutions, the network will be able to
learn distinct spatial patterns at different scales.Inception
network has the luxury of leveraging different filter sizes
within their convolutional layers.Within a convnet,
different convolution filter sizes learn spatial patterns and
detect features at varying scales.3x3 and 5x5 learn spatial
patterns across all input's dimensional components
(height, width and depth).
4.3 Concatenation layer
The Inception module comprises a concatenation layer
that combines all outputs and feature maps from the
convolutional filters into a single object to form the
module's output. It accepts a list of tensors with identical
shapes except for the concatenation axis as input and gives
a single tensor that is the concatenation of all inputs.
5. DATASET
The primary concept of machine learning is collecting
image datasets for training and model building. The
dataset “Fruit and Vegetable Image Recognition”(from
Kaggle) was used for the task of Fruit and Vegetable
Classification. It consists of images of fruits including
Apple, Banana, Watermelon and Vegetables, including
Eggplant, Onion, Bell pepper. It contains three folders:
train (100 images each), test (10 images each), validation
(10 images each). For the prediction of the quality of
fruits, the dataset “IEEEFRUITSDATA_train&test” was
used.The Dataset consists of 12,050 images comprising
fruits such as Apple, Banana, Guava, Orange, Lime and
pomegranate categorized based on their quality as Good
or Bad.
6. METHODOLOGY
The Inception v3 model with pre-trained weights was
imported and loaded for training the classifiers using
transfer learning.
Transfer Learning is a method where we use a pre-trained
model. This Inception-v3 model has been trained on a vast
dataset, and we transferred weights accumulated during
hundreds of hours of training on many high-performance
GPUs. We then set the parameters in the pre-trained
model to be untrainable, optimizing only the parameters
in the succeeding dense layers during training.
For our problem statement, the last layer -The softmax
layer, was modified to have the required number of classes
as the original model was trained on the ImageNet dataset.
It significantly decreases training time and needs far less
data to improve performance.
6.1 Training Parameters
The loss metric used was Categorical cross-entropy , a loss
function used in multi-class classification tasks.
Adam optimizer was implemented, a stochastic gradient
descent replacement technique for training deep learning
models. Adam combines the finest qualities of the AdaGrad
and RMSProp methods to provide a sparse gradient
optimization technique for noisy problems.
The activation function utilized in the hidden layer
substantially impacts how well the network model learns
the training data. The type of predictions the model may
produce is determined by the activation function used in
the output layer. The type of predictions that the model
makes is heavily influenced. Hidden layer: Rectified
Linear Activation (ReLU) was used because it performs
better than other activation functions and overcomes
problems such as the vanishing gradient problem. ReLU is
represented by the formula y=max(0,x). The ReLU
activation function changes the negative values of the
neuron to 0, and positive values remain as it is.
In the output layer, the activation function depends on the
type of prediction problem. For multilabel or multiclass
such as fruit prediction SoftMax function is used.
6.2 Image Augmentation
Image augmentation is the technique of altering the
existing data to create some more data for the model
training process. Different Augmentation techniques such
as Image rotation, Image flipping, and Image shifting have
been used. The ImageDataGenerator class allows you to
instantiate generators of augmented image batches (and
their labels) via. flow(data, labels) or
.flow_from_directory(directory).
7. RESULT
The Fruit and Vegetable Classification model was trained
for 50 epochs. The Validation loss incurred is 0.4914, and
the accuracy achieved was 0.9832.
The Fruit Quality Prediction model was trained for 10
epochs. The Validation loss incurred is 0.2925, and the
accuracy achieved was 0.9851.

Chart-1: Model Performance Loss
Chart-2: Model Performance Loss
8. CONCLUSION
This paper focuses on developing an automated system for
Fruit and Vegetable Classification and their quality
prediction. Image processing methods and several CNN
models were experimented with, and transfer learning
was utilized. Inception V3 exhibited the best results and
showed an accuracy of 0.98. Automation technology
enables produce to reach customers faster, more freshly,
and sustainably. Increased productivity due to automation
increases the output and pace of production, lowering
consumer costs. Routine, manual tasks may be
mechanized using robot technology, lowering labor costs
and reducing the number of workers required in a farm
business facing a labor crisis.
ACKNOWLEDGEMENT
1. Fruit and Vegetable Image Recognition, Kaggle
2. IEEEFRUITSDATA_train&test, IEEE
REFERENCES
[1] Bhavini J. and Sheshang D., “A Survey on Apple Fruit
Diseases Detection and Classification”, International
Journal of Computer Applications (0975 – 8887)
Volume 130, No.13, November 2015
[2] R Shantha and V Gomathy, “Fruit Classification using
Statistical Features in SVM Classifier,” IEEE 2018 4th
International Conference on Electrical Energy Systems
(ICEES), doi:10.1109/ICEES.2018.8442331
[3] Winda Astuti, Satrio Dewanto, Khristian Edi Nugroho
Soebandrija and Sofyan Tan, “Automatic fruit
classification using support vector machines: a
comparison with artificial neural network”,” ICEED
2018,doi:10.1088/1755-1315/195/1/012047
[4] Zaw Min Khaing, Ye Naung and Phyo Hylam Htut3,
“Development of Control System for Fruit
Classification Based on Convolutional Neural Network
”,2018 IEEE 978-1-5386-4340-2/
[5] H. Muresan and M. Oltean, “Fruit recognition from
images using deep learning,” Acta Universitatis
Sapientiae, Informatica 10(1), 26–42 (2018).

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