The document describes a smart farming robot for weed detection and removal. It includes a literature review on existing robotic weed control solutions. The objectives of developing an autonomous robot with weed detection algorithms and a mechanical structure for weed removal are presented. The robot's block diagram and methodology are shown, including its camera-based weed detection module, image processing software, and actuators for targeted herbicide spraying. Hardware components like the Raspberry Pi, motors, pumps and structural design are detailed. The software implementation of image filters, segmentation, and background subtraction for weed identification is explained.

1. ASSALAM O ALaiKUM

2. SMART FARMING ROBOT
FOR WEEDING APPLICATION
PRESENTED BY:
SIKANDER AMIR (FA17-BEE-41)
TALHA ARIF (FA17-BEE-044)
MUHAMMAD FAIQ TANVEER (FA17-BEE-080)

3. ROBOT VIDEO

4. TABLE OF CONTENTS:
Problem statement
Introduction
Literature survey
Objectives
Block diagram & Methodology
Observation, calculation and structure
Structural design of robot & Hardware and Software Implementation
Objectives achieved

5. PROBLEM STATEMENT
The unwanted plants that grow in-between crops are called weeds. Weeds can be harmful
for the crop in many ways such as it takes the space of actual crop and also a significant
amount of nutrients and water. They also over-shadow the crop and stop or minimize the
amount of sunlight and wind to reach the crop.

6. INTRODUCTION
In the coming years, smart farming is projected to make a huge impact on the agricultural economy
by bridging the gap between small and large-scale businesses. The trend is not only pertinent in
developed countries — developing countries have also realized its immense importance as well.
Smart farming and precision agriculture involve the mixing of advanced technologies into existing
farming practices to extend production efficiency and therefore the quality of agricultural products.
Most of the present and impending agricultural technologies fall under three categories that are
expected to become the pillars of the smart farm: autonomous robots, drones or UAVs, and sensors
and the Internet of Things (IoT).

7. LITERATURE
SURVEY

8. LITERATURE SURVEY
Title Advantages Disadvantage
Aerial robot for smart
farming and enhancing
farmers’ net benefit
Small crop fields at lower
flight altitudes focused on it.
It has short flight range and
low flight time limits.
Robotic Weeding It uses seed mapping and
computer vision technology.
Overlapping between crop
seedlings and weed will
complicate the identification
process.
Design and Development of
Automatic Weed Detection
and Smart Herbicide Sprayer
Robot
The weeds identified and
sprayed upon and it takes 3
seconds.
Its dependency on good and
stable lighting conditions

9. OBJECTIVES
 To design and develop a mechanical structure which will move in fields
 To develop an algorithm which will help in robot locomotion
 To develop an automatic weed detection algorithm of deep learning
 To design a mechanical structure for removal of the weeds
 To design a photovoltaic system for powering purposes of the robot.

10. Sensors &
Camera based
Module
IMAGE
PROCESSING
WEED
DETECTION
STERRING
SYSTEM
SPEED
CONTROL
MOTORS
ENCORDER
WHEELS
SONAR
SAFTY FEATURES
ACTUATION
INTERFACE
ACTUATOR
CONTROL
CHEMICAL
WEED
CONTROL
BATTERY
SOLAR
CHARGE
CONTROLLER
SOLAR
PANNEL

11. METHODOLOGY
 Firstly, the literature survey had done after selecting the project.
 Based on the literature review/survey, the overall analysis of appropriate sensor tools and components had
done by looking at the different alternatives and the best optimal solution.
 After cost estimation and selecting the best alternative, the product architecture has been selected which will
include internal and external design of robot.
 Furthermore, the hardware tools had been purchased.
 After that assembling has been done in step-by-step manner first mechanical structure and then joining of
sensors etc.
 After interfacing, algorithm development phase has started using different software and hardware tools. which
has covered weed detection and robot locomotion
 Synchronization of all components has been done after completion of software and hardware components.
 Testing has started after completion of all aspects to check the proper functionality and to remove the bugs.

12. OBSERVATION AND CALCULATIONS IN POTATO
FIELD:
Observations Numerical value
in inches
Length of row 288 inches
Distance between
two rows
27.6 inches
Distance between
one to third row
49.2 inches
Height of the
plantation
16 inches

13. STRUCTURAL DESIGN OF ROBOT
Top View Front View Back View Left/Right
View

14. OVERALL STRUCTURAL VIEW OF ROBOT

15. MEASUREMENTS OF THE STRUCTURE
Observations Numerical Value in Inches
Length 24 inches
Width 24 inches
Height 32.4 inches
Weight 30~35 kg

16. HARDWARE BASED EQUIPMENT'S
• Motor Drivers
We have chosen two types of motor drivers one for locomotives motors and other is for the spraying pumps. First one
is IBT2 motor driver to provide the motor its required current which varies from 5 -12 A for this current IBT2 is nearest
and suitable driver for our robot. Its specifications are Input voltage: 6V-27V, Maximum Current : 43A, Input level : 3.3V-
5V
• Converter (DC-DC)
The proper input voltages for the raspberry pi, we have to step down the input voltages from 12 to 5 volts for this
purpose we purchased the buck converters which gives us the 5V output voltages. Its specifications are as follows:
1. 5 A high-power high efficiency low ripple
2. Module properties: non-isolated step-down module (BUCK)
3. Rectification: non-synchronous rectifier
4. Input voltage: DC 4V ~ 38V
5. Output voltage: DC 1.25V ~ 32V (adjustable)
6. Output current: 0A ~ 5A

17. HARDWARE BASED EQUIPMENT'S
• Raspberry Pi
We used the raspberry pi 3 model B+ for our project for efficient image processing with the pi camera and its own
processing capacity. The Raspberry Pi 3 Model B+ is the final revision in the Raspberry Pi 3 range. Its specifications are
listed below:
• Broadcom BCM2837B0, Cortex-A53 (ARMv8) 64-bit SoC @ 1.4GHz
• Extended 40-pin GPIO header
• Full-size HDMI
• 4 USB 2.0 ports
• CSI camera port for connecting a Raspberry Pi camera
• DSI display port for connecting a Raspberry Pi touchscreen display
• 4-pole stereo output and composite video port
• Micro SD port for loading your operating system and storing data
• 5V/2.5A DC power input

18. HARDWARE BASED EQUIPMENT'S
• Locomotion
To give power and torque to the tire we selected the WM72 motor. the motor is suitable for various land
robots and high-powered projects. Motor speed is 55 Rpm. Its features are as follows:
• Motor Operation Voltage:12V
• Speed: 55 Rpm
• No load current: 2A
• Stall current: 10A
• Stall Torque: 45 kg-cm
• Motor Power: 120W
• Shaft diameter: 10mm
• Shaft dimension: 29mm
• Weight:1280gr

19. HARDWARE BASED EQUIPMENT'S
• Steering System
In first step we faced the difficulty in designing the steering system that how we can turn
or move our robot. After visiting field, we came to know that we can not make the back
wheel drive because it will damage the furrow and crop. Then we made our steering
system such that we attached both tires of each side left and right with each other
through chain having single motor between them. So, when we want to turn right left
motor rpm will increase and move the robot towards right and vice versa.
• Spraying System
Spraying system includes the 4 pumps, 2 tanks, 4 nozzles and pipes for the chemical. For
pumping of chemical, we used the 5V DC pump attached with the water tanks of cars. It
serves the purpose of pressurizing the chemical so that it can be sprinkled or sprayed on
to the crops. The reason behind taking 4 pumps each for each nozzle so that accuracy in
weeding can be achieved and pressure do not divide between any 2 nozzles. The tanks
have the capacity 1-1.5 liter liquid and has space for 2 pumps which is best in our
condition because we need four pumps to operate with 2 tanks. These nozzles have both
functions it can sprinkle and shoot the liquid in straight line. The purpose of attaching
the nozzles individually so that if weed is detected under any 1 or 2 nozzles then those
nozzles will only operate instead of others. Pipe for the transportation of chemical 6mm
to provide sufficient space and also create pressure for the nozzles.

20. SOFTWARE IMPLEMENTATION

21. IMAGE PROCESSING
Till now we have done with the implementation
of following filters which will help our project to
detect weeds and classify them from plantation.
• HSV:
The HSV (which stands for Hue Saturation Value)
scale provides a numerical readout of your image
that corresponds to the color names contained
therein. Hue is measured in degrees from 0 to
360.
• Edge Detection:
We have done with the three algorithms for
edge detection.
• Sobel Filter
• Auto Canny
• Discrete Fourier Transform using circular mask
filtering.

22. IMAGE PROCESSING
• Segmentation:
An image is a collection or set of different pixels. We
group together the pixels that have similar attributes
using image segmentation. The process of
partitioning a digital image into multiple segments
(image objects) is segmentation. We are doing
segmentation by binary thresholding method
(OTSU).
• SHADOW SUBTRACTION:
final_img, background = subtract_background_rolling_ball(v,
radius, light_background=True, use_paraboloid=False,
do_presmooth=True)
This algo subtracts the background from the object and gives
the object and background separately. This algo will help us in
the fields where we can take images and subtract shadow due to
sunlight.

23. IMAGE PROCESSING
• SHARPENING FILTER:
The results of all the combined filters are
attached. Where we can clearly distinguish
that if we don’t do image sharpening and
directly apply segmentation and DFT filter the
image object is not properly segmented while
after sharpening we can easily classify our
object segment.

24. IMAGE PROCESSING
• Background Subtraction:
Background subtraction is a
technique for separating out
foreground elements from the
background and is done by
generating a foreground mask.
This technique is used for
detecting dynamically moving
objects from static cameras.
Background subtraction technique
is important for object tracking.

25. IMAGE
PROCESSING
ERROSION FOLLOWED BY DIALATION:

26. IMAGE PROCESSING
Result Number : 1 Result Number : 2

27. OVERALL FINAL FORM OF ROBOT

28. OBJECTIVES ACHIEVED
 To design and develop a mechanical structure which will move in fields.(achieved)
 To develop an algorithm which will help in robot locomotion. (achieved)
 To develop an automatic weed detection algorithm of deep learning. (Achieved)
 To design a mechanical structure for removal of the weeds. (achieved)
 To design a photovoltaic system for powering purposes of the robot. (achieved)

29. THANK YOU !