Robotic process automation (RPA) is the application of technology that allows employees in a company to configure computer software or a “robot” to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses and communicating with other digital systems. Robotic Process Automation, or RPA, describes the application of technology that “allows employees in a company to configure computer software or a ‘robot’ to capture and interpret existing applications for processing a transaction, manipulating data, triggering responses, and communicating with other digital systems,” according to the Institute for Robotic Process Automation and Artificial Intelligence (IRPAAI). Any company that uses labor on a large scale for general knowledge process work, where people are performing high-volume, highly transactional process functions, will boost their capabilities and save money and time with robotic process automation software.

1. Department of
ARTIFICIAL INTELLIGENCE & DATA SCIENCE
M.Tech. 3rd Semester
TECHNICAL SEMINAR
ROBOTIC PROCESS AUTOMATION
Under the guidance
Dr. SHARANBASAPPA A MADIVAL
Presented by
MARY SUSHMITA
(SG22ADS008)
2023-2024
FACULTY OF ENGINEERING & TECHNOLOGY (CO EDU.)
K a l a b u r a gi

2. CONTENTS
1. Abstract
2. Introduction
3. Literature Survey
4. System Requirements
5. Existing System and Disadvantages
6. Proposed System And Advantages
7. Methodology
8. Conclusion
9. Reference

3. ABSTRACT
• Robotic Process Automation (RPA) is an emerging tool of automation technology based on
notion of software robots or Artificial Intelligence (AI). RPA is the use of software with
artificial intelligence (AI) and Machine Learning (ML) capabilities to handle high-volume,
repeatable tasks that previously required a human to perform. RPA has the capability of
software and services which allow to transact in any IT application, typically in the same
way a human would, to automate complex, rule based work.
• In other words, RPA software allows developers to develop complex automations to suit for
company’s processes. When an RPA robot is at work, it performs tasks just like a human
would: logging in, operating applications, entering data, performing complex calculations
and logging out. ML is an application of Artificial Intelligence (AI) that provides systems
the ability to automatically learn and improve from experience without being explicitly
programmed.

5. INTRODUCTION
• Robotic process automation (RPA) is a technology that mimics the way
humans interact with software to perform high-volume, repeatable tasks.
RPA automates repetitive business processes in industries, including
banking, IT, human resources and healthcare. RPA is growing in popularity
because it can reduce costs, streamline processing and drive better
customer experiences.
• When combined with artificial intelligence (AI) and machine learning,
RPA can capture more context from the content it's working with by
reading text or handwriting with optical character recognition (OCR),
extracting entities like names, invoice terms or addresses using natural
language processing (NLP), and capturing more context from images, such
as automatically estimating accident damage in an insurance claim picture.

7. Jaymala Patil and Rajkumar have given advances to study and find
diseases in plants. They have done literature survey of 24 papers and
found different methods that were used to find diseases. Each time, a
new technique was introduced with the different accuracies. For
example: wavelet based technique, Stereomicroscopic method,
Integrating image analysis, Artificial vision system etc [4].
In this paper S.E.A Raza and N.Rajput[5],develop a machine learning
system to remotely detect infected plants with two types in the same
image. Thermal imaging is a fast and non-destructive way of
scanning plants for diseased regions. In this paper, they aim to
combine information from stereo visible light images with thermal
images to overcome problems like temperature variation, leaf angles,
environmental conditions and present a method for automatic
detection of disease in plants using machine learning techniques.

8. In this paper Patil and Chandavale [6] introduce two interesting topics
which are Detection and classification. Detection and classification of
plant diseases are important task to increase plant productivity and
economic growth. To detect plant disease the image is processed with
pre- processing, segmentation, feature extraction and classification
processes and other different techniques. It classifies the data based
upon selected features.
Rastogi, Arora and Sharma[7] proposed that the system is divided into
two phases, in first phase the plant is recognized on the basis of the
features of leaf, it includes pre- processing of leaf images, followed by
Artificial Neural Network based training and classification for recognition
of leaf. In second phase the disease present in the leaf is classified using
K-Means based segmentation , extraction of

9. SYSTEM SPECIFICATION AND DESIGN
3.1 HARDWARE CONFIGURATION
Processor : Pentium Core I5 11th Gen
RAM : 4GB or more.
Hard Disk : 500GB or more.
Monitor : 21 inch Color Monitor
Keyboard : 102/104 Keys
Mouse : Optical Mouse
3.2 SOFTWARE CONFIGURATION
Operating System :Windows 10 /11
Front End :Python
Framework : Pycharm

10. EXISTING SYSTEM
The yearly production of crops is largely destroyed due to the
plants getting affected by the pathogens like fungal, bacterial
and viral. These pathogens highly effect the growth and
quality of plants. As we all know the region of plant affected
by disease cannot be seen with naked eyes and even if we
identify the disease we cannot accurately know what type of
disease it is. This overall process is time consuming and
nearly impossible to do it manually. So for this reason we have
tried to make the process less time consuming and automatic
for farmers. We will use image processing technique to
identify the disease
Disadvantages
• Not accurate

11. PROPOSED SYSTEM
Proposed method is used to build a system which can automatically
identify the disease and detect the part of plant affected by it. Used
SVM algorithm for classification purpose which classifies into:
Not Infected: If we obtain result as Not infected then disease is not
found in that plant.
Infected: If obtain Infected then disease is detected in the respective
plant.
Advantages
1. Less expensive
2. Give more accuracy

12. METHODOLOGY
• SVM (Support Vector Machine): It is a supervised learning algorithm which
can used for binary classification or regression. It is a coordinate of
individual observations. It is based on decision planes which defines
decision boundaries. It also separated the set of objects having different
class.
• Color moments: Color moments are very much useful for color indexing
purposes. It considers only the first three color moments as feature in
image retrieval applications. It can be used to compare the two images
based on color.
• HOG feature: The histogram of oriented gradients (HOG) is a feature used in
vision and image processing for object detection. The image is divided into
small connected regions called cells. Since it works on local cells, it is
invariant to geometric transformations.
• HSV Feature: The Hue Saturation Value (HSV) represents the color,
dominance of color and brightness. Therefore, the color detection algorithm
can be used to search in terms of color position and color purity. It is used
to detect the pixels.

13. The system architecture, accepts the
image and applies preprocessing then
displays heatmap, where a heat map (or
heatmap) is a 2-dimensional data
visualization technique that represents
the magnitude of individual values
within a dataset as a color. Now
appying feature extraction which is a is
a part of the dimensionality reduction
process, in which, an initial set of the
raw data is divided and reduced to
more manageable groups. Further in
the final step based on texture
processing classifies Diseased fruit or
healthy fruit
SYSTEM ARCHITECTURE

15. Read Image

16. Preprocessing

18. Feature Extraction

19. CONCLUSION
Thus, we have designed such a system which detects disease in plants, which
saves farmer's time and cost, which has more accuracy. It is more faster than
other systems because of the different algorithms which are used to make it
faster. We are using SVM Algorithm which is relatively simple to implement
and can be used in image segmentation, image classification and image
reconstruction. It can work easily during the global optimization which poorly
behave with the objective function.

20. REFERENCES
1. 1.Dr.G. H. Agrawal ,Prof. S.G.galande and Shalaka R.Londhe "Leaf disease detection and climatic parameter monitoring of
plants using IOT." Internationl Journal of Innovative Research in Science, Engineering and Technology, volume-4,issue-
10,pp. 9927-9932,Oct- 2015.
2. 2.Viabhavi S.Bharwad and Kruti J.Dangarwala" Recent research trends of plants disease detection." International Journal of
Science and Research, volume-4,issue-12,pp. 843-845, Dec-2015.
3. 3.Manisha Bhange and H.A.Hingoliwala "Smart framing: pomegranate disease detection using image processing." Second
International Symposium on Computer Vision and the Internet (VisionNet'15), pp.280-288.
4. 4.Jaymala K. Patil and Raj Kumar "Advances in image processing for detection of plant disease." Journal of Advanced
Bioinformatics Applications and Research , pp. 135-141, June-2011.
5. 5.Shan-e-Ahmed Raza,Gillian Prince,John P.Clarkson and Nasir M.Rajpoot "Automatic detection of diseased tomato using
thermal and stereo visibl light images." PLOS-One, April-2015.
6. 6.Sagar Patil and Anjali Chandavale"A survey on methods of plant disease detection." International Journal of Science and
Research, volume-4,issue-2,pp. 1392-1396, Feb-2015.
7. 7.Aakanksha Rastogi , Ritika Arora and Shanu Sharma "Leaf disease detection and grading using computer vision
technology and fuzzy logic."International conference on Signal Processing and Integrated Networks, 2015.
8. 8.Bed Prakash and Amit Yerpude "A survey on plant disease identification."International Journal of Advanced Research in
computer science and software Engineering, volume-15, issue-3, pp. 313-317, March-2015.
9. 9.Rajleen Kaur and Dr. Sandeep Singh Kang "An enhancement in classifier support vector machine to improve plant
disease detection." IEEE 3rd International Conference , pp. 135-140,2015.
10. 10.Anand H Kulkarni and Ashwin Patil R. K "Applying image processing technique to detect plant disease." International
Journal of Modern Engineering Research ,volume-2, issue-5, pp. 3661-3664, 2012.
11. 11.Dheeb Al Bashish , Malik Braik and Sulieman Bani-Ahmad "A frame-work for detection and classification of plant leaf
and stem disease."in International conference on signal and Image Processing
12. 12., pp. 113-118 ,2012.
13. 13.Sachin.D.Khirade and A.B.Patil "Plant disease detection using image processing."in International Conference on
Computing Communication Control and Automation, ,pp. 788-771,2015.
14. 14.Shirke, Suvarna, S. S. Pawar, and Kamal Shah. "Literature Review: Model Free Human Gait Recognition." Communication