This document discusses an automated fruit sorting technique using machine learning. It proposes a model where fruits are imaged using multiple cameras and analyzed for parameters like size, color, texture using image processing and machine learning algorithms. Features are extracted from images using techniques like segmentation, and fruits are classified into categories like size or ripeness using algorithms like SVM, KNN. This automated sorting is presented as more efficient and consistent than manual sorting. Future applications to other crops like rice and pulses are discussed.

1. APPLICATION OF MACHINE LEARNING -
AUTOMATED FRUIT SORTING TECHNIQUE
B.Anudeep V. Gowtham Chandra
(102U1A0503) (102U1A05537)
IVth CSE
GEETHANJALI INSTITUTE OF SCIENCE & TECHNOLOGY
NELLORE
anudeep.badam@gmail.com
gowtham.viper9@gmail.com
ABSTRACT:
Machine learning is one of the discipline in
Data Mining. Machine learning and data
mining are relatively similar. But the
machine learning focuses on prediction, ,
based on known properties learned from
training data. In this we have explained
about classification of machine learning
algorithms, list of machine learning
algorithms and applications of machine
learning. In addition to we have explained
about APPLICATION OF MACHINE
LEARNING-FRUIT SORTING TECHNIQUE.
Using image processing, computer vision,
machine learning techniques we have
done fruit sorting. Based on quality of fruit
with data/parameters CLASSIFICATION is
done using machine learning algorithms
like Support vector measure (SVM ), K-
Nearest neighbor (KNN),fuzzy logic
approach, Artificial Neural networks.
Finally Automated Fruit sorting is latest
trend and efficient technique in modern
Agriculture.
Keywords:
Machine learning, Data mining, supervised
learning, unsupervised learning, reinforcement
learning, semi-supervised learning, computer
vision, image processing, KNN, LDA,SVM
algorithms, Transduction, Induction.
Introduction
Machine learning is one of the discipline in data
mining.It is a “Field of study that gives computers the
ability to learn without being explicitly
programmed”.
“A computer program is said to learn from
experience E with respect to some class of tasks T
and performance measure P,if its performance at
tasks in T,as measured by P,improves with
experience E”

2. “A core objective of learner is to GENERALIZE
from EXPERIENCE”.
Generalization means ability to learn the machine to
perform accurately on new,unseen tasks after having
experienced a learning data set.
Machine learning Vs Data Mining:
Many of them confuse about data mining and
machine learning that both are same. But the terms
are different .
What is Machine learning?
Machine learning focuses on Prediction, based on
known properties learned from training data.
What is Data mining?
Data mining focuses on discovery of unknown
properties in the data.It is also known as Knowledge
discovery in data base.
Why we use Machine learning?
Machine learning systems attempt to eliminate the
need for human intuition in data analysis. However
some human interaction can be reduced.
Classification of algorithms in Machine
learning:
 Supervised Learning
 Unsupervised Learning
 Semi-supervised Learning
 Transduction
 Reinforcement Learning
 Inductive Learning
 Developmental Learning
Supervised Learning:
In this algorithms are trained on labelled
examples, i.e, inputs where the desired
outputs is known. supervised learning
algorithms attempts to generalize a function
or mapping from inputs to outputs.
Unsupervised Learning:
In this algorithms operate on unlabeled
examples i.e, input where the desired output
is unknown.in this main objective is to
discover structure in data(Cluster analysis).
Semi-supervised Learning:
In this it combines both labelled and
unlabelled examplesto generate a function or
classifier.
Transductive Learning:
It predicts new outputs on specific,fixed test
cases and training cases.
Reinforcement Learning:
In this agent attempts to gather knowledge
from environment.The agents executes
actions which cause the observable state of
the environment to change.
Inductive Learning:
It is learning from based on previous
experience.
Developmental Learning:
This is latest Learning known as Robot
learning.
List of Machine Learning
Algorithms:
 Support vector machines
 Artificial neural networks
 Clustering
 Decision tree learning
 Association rule learning
 Bayesian networks
 Metric learning algorithms
Applications of Machine learning:
 Speech recognition
 Drive automobiles
 Play world-class backgammon
 Program generation
 Routing in communication networks
 Understanding handwritten text
 Data mining

3.  face detection and face recogniastion
 Automated fruit sortiong technique
Automated Fruit Sorting Application
In general farmers and distributors do conventional
quality inspection and handpicking to sort and grade
agricultural and food products.
This manual method is time-consuming, laborious,
less efficient, monotonous, slow and inconsistent.
Automated fruit sorting is done Using techniques
like :
 Image Processing
 Computer Vision
 Machine Learning
Advantages of Automated fruit
sorting:
 Cost effective.
 Consistent
 Greater product stability.
 Safety.
 Speed and accurate sorting can be
achieved.
 Improve the quality of the product.
 Abolish inconsistent manual
evaluation.
 Reduce dependence on available
traditional inspection.
Quality of fruit sorting
Parameters:
Flavor such as sweetness, acidity content in the
product, grading through appearance on bases
color,size, shape, blemishes and glossiness of
product, and texture that is assorted on its firmness or
product’s mouth feel. Below tables summarize some
of the very recent grading and sorting systems.
COMPUTER VISION
Computer vision systems provide rapid, economic,
hygienic, consistent and objective assessment. Diffi
culties still exist in this fi eld due to relativity slow
commercial uptake of computer vision technology
and processing speeds still fail to meet modern
manufacturing requirements in all sectors. A model
for sorting is proposed in order to overcome
drawback of current grading systems. so we use
machine learning techniques.
Parameters considered in Computer
vision:
Drawbacks in computer vision:
• Current sorting systems are not accurate.
• Very few parameters like size and color are
considered for grading systems.
• Still all are under research laboratories.
• Most of research and development of automated
agriculture product sorting has been done outside
India.
• The sorting of fruits is still performed manually in
India.
• No grading system is yet available for fruits like
chikoo, sugarcane and grapes etc that are exported to
other countries from India

4. Proposed Model
Fig: Proposed model for automated fruit
sorting
As shown in figure, first fruits are collected in
a chamber. From the chamber it moves through
escalators safely where the weight of the fruit gets
estimated. It moves towards another chamber where
the image of fruit is captured by more than one
camera in different angle.
Detecting fruit growth:
For detecting fruit growth (raw or ripped), smell of
the fruit is detected by sensors of wireless sensor
network.
Usage of image processing:
Image is then processed where various algorithms are
applied on image for finding expected features like
size, depth, 3D model, texture and color. For finding
different features of fruit image following steps
should be applied,
 Image segmentation algorithm can be
applied on captured image.Histogram
thresolding, feature space clustering, Region
basedapproach; Edge detection
approach,fuzzy approach and neural
network approach are the examples of
segmentation methods.
 Size parameter:
From the segmented image, size parameter
can be identified using machine vision by
measuring projected area, perimeter or
diameter.
 Shape parameter :
can be identified using contour based
methods like chain code, B-spine, Hausdorff
distance ,Fourier descriptor, etc. or region
based methods like convex hull, medial axis,
Legendre moments, shape matrix etc.
 color parameter:
On moving up to next, color feature can be
identified using color features of fruits and
vegetables included mean, variance, ranges
of the red, green, and blue color primaries
(RGB color model) and the derived hue,
saturation, and intensity values (HIS color
model).
 Skin texture parameter:
Skin disease and defects can be found out
using skin texture identification methods.
 Interior appearance parameter:
Image descriptors like global color
histogram; Unser’s descriptors, color
coherence vectors, border/interior,
appearance descriptors etc. can be used for
classification of fruits and vegetables.
 MACHINE LEARNING ALGORITHMS
USED:
Finally, machine-learning algorithm is used
for classification of parameters. Machine
learning algorithms are neural network,
fuzzy logic, genetic algorithm, fractal
dimensions, Support Vector Machine
(SVM), K- Nearest Neighbor (KNN), Linear
Discriminant Analysis (LDA) etc.
Based on the decision drawn after the
process on the above steps, the fruit is
classified into different categories like big,
small, medium sized, ripe/unripe or
defectives.

5. Finally automatic packaging system packs
the fruit according to the categories
provided.
FUTURE APPLICATIONS:
o Rice Sorting
o Paddy Sorting
o Pulses Sorting
Conclusion and Future Direction:
 Automated fruit sorting is
speedy, inexpensive, safe
and accurate.
 Proposed model is
generalized and it is
considering far more feature
parameters than available
sorting systems.
 Currently, research in the
automated fruit sorting and
grading has been conducted
by experimenting them in
laboratories only.
References:
[1] Dah-Jye Lee, James K. Archibald, and
Guangming Xiong, "Rapid Color Grading for Fruit
Quality Evaluation Using DirectColor Mapping,"
IEEE TRANSACTIONSON AUTOMATION
SCIENCE AND ENGINEERING, vol. 8, no. 2, pp.
292-302,November 2011.
[2] Mahendran R, Jayashree GC, Alagusundaram K,
"Application of Computer Vision Technique on
Sorting and Grading of Fruits and Vegetables ", Abd
El-Salam et al., J Food Process Technology 2011.
[3] Pib.nic.in/archieve/others/2012/mar/
d2012031302.pdf, Date: 11.7.2013.
[4] Tajul Rosli Bin Razak, Mahmod Bin Othman
(DR), Mohd Nazari Bin Abu Bakar (DR), Khairul
Adilah BT Ahmad, and AB.Razak Bin Mansor,
"Mango Grading By Using Fuzzy Image Analysis,"
In proceedings of International Conference on
Agricultural, Environment and Biological Sciences,
Phuket, 2012.
[5] http://www.ibef.org/industry/agriculture-
india.
[6] Xu Liming and Zhao Yanchao, "Automated
strawberry grading system based on image
processing," Computers and Electronics in
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April 2010.