Agriculture weeding robot with gesture control

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Agriculture weeding robot with gesture control


Automated processes in the field of agriculture have become more and more reliable and efficient. There are many difficulties faced when manpower is used. It is time-consuming and becomes tedious. Robotic systems integrated with various control methods can be very useful in doing repetitive work, such as the seed sowing process where the same movement is continuous. Previous weed removal robots included optical image sensing which makes the system -costlier. Our robot is cost-effective which eliminates optical sensors. In this project, we have developed a Trainable automatic robot that helps in removing unwanted weeds on agricultural fields using gestures to control a three-axis robotic arm to do the necessary work. The arm is placed on a rover which is controlled Wirelessly using Bluetooth. The arm is taught to do the repetitive motion with a gesture using a hand glove to do the necessary work. The setup of the rover with the attached robotic arm is tested and evaluated under normal environmental conditions. Agriculture weeding robot with gesture control

Agriculture weeding robot with gesture control


In the field of agriculture, various operations for handling heavy materials are performed. For example, in vegetable cropping, workers should handle heavy vegetables in the harvest season. Additionally, in organic farming, which is fast gaining popularity, workers should handle heavy compost bags in the fertilizing season. These operations are dull, repetitive, or require strength and skill for the workers. In the 1980?s many agricultural robots were started for research and development. Kawamura and co-workers developed fruit harvesting in the orchard. Grand and co-workers developed the apple harvesting robot. They have been followed by many other works. Many of the works focus on structure systems design (e.g., mechanical systems design) of robots and report the realization of the basic actions in actual open fields. However, many of the robots are not in the stages of diffusion but are still in the stages of research and development. It is important to find rooms to achieve higher performance and lower the cost of the robots. Agriculture involves the systematic production of food, feed, fiber, and other goods. In addition to producing food for humans and animals, agriculture also produces cut flowers, timber, fertilizers, animal hides, leather, and industrial chemicals.

Existing System

  • RF technology while the robot is moving along the predefined path by using IR and motion sensors.
  • Humans can directly interact with the field

Proposed System

  • Rebuild interface for system and device.
  • Field monitoring and Weeding detection
  • Improve accuracy
  • Detect Weeding
    • New advanced cameras.
    • More complicated defects will be detected.
  • Build a control model for farmland.
  • Human work is less

Block diagram

User Node

Agri 1


Robot node

Agri 2

Block diagram explanation

The system architecture of the proposed glove, harvester, and interface. The glove is the Human Control Interface (HCI). The user wears the glove which is embedded with an accelerometer, gyrometer, and flex sensors. The accelerometer gives the acceleration and tilt and the gyrometer provides the angular velocity and orientation. The harvester is the rover over which the arm is fixed. The sensors are embedded in the hand glove and are interfaced to the microcontroller unit (MCU). Three accelerometers to control the 3 links of the 4 DoF arm and two flex sensors to control the cutter are embedded in the glove. The user HCI is the transmitter and the robotic harvester is the receiver. The transmitter block contains all the sensors, Bluetooth, and an MCU, all integrated into a wearable device. The wearable device transmits real-time joint angles of the user’s arm to the robotic arm.

Hardware tools

  • Arduino
  • Raspberry pi
  • Gyroscope
  • Flex sensor
  • Mems?
  • Camera
  • Bluetooth
  • Robot?

Software tools

  • Arduino IDE
  • Embedded C
  • Raspberry pi OS: Raspbian stretch
  • Programming Platform: python 3 IDLE
  • Programing language: python 3
  • Library: OpenCV
Agri 2 removebg preview


Agri 3 removebg preview
Agri 4 removebg preview
Agri 1 removebg preview


[1] Nasser H. Dardas and Nicolas D. Georganas, ?Real-Time Hand Gesture Detection and Recognition Using Bag-of-Features and Support Vector Machine Techniques?, IEEE transactions on instrumentation and measurement, vol. 60, no. 11, November 2011.

[2] L. Yun and Z. Peng,?An automatic hand gesture recognition system based on Viola-Jones method and SVMs,? in Proc. 2nd Int. Workshop Comput. Sci. Eng., 2009, pp. 72?76.

[3] W. Chung, X. Wu, and Y. Xu, ?A real-time hand gesturerecognition based on Haar wavelet representation,? in Proc. IEEE Int. Conf. Robot. Biomimetics, 2009, pp. 336? 341.

[4] L. Juan and O. Gwen,?A comparison of SIFT, PCA-SIFT and SURF,? Int. J. Image Process. (IJIP), vol. 3, no. 4, pp. 143? 152, 2009.

[5] L. Yeffet and L. Wolf, ?Local trinary patterns for human action recognition,? in Proc. IEEE ICCV, 2009, pp. 492?497.

[6] Y. Ren and C. Gu, ?Real-time hand gesture recognition based on vision,? in Proc. Edutainment, 2010, pp. 468?475.

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