Weed detection and removal using autonomous mobile robot
Weed plants are the unwanted plants which grow in crop fields where their growth is not wanted. They consume all the nutrients which are necessary for growth of crop plants. Therefore, their removal is mandatory for proper growth of crop plants. The autonomous weed control robot is used to remove these unwanted weed plants. For removal of weed plants, different traditional methods are being used which damages the crop plants and eventually reduces the yield. Autonomous Weed Control Robot is introduced to control weed plants and to remove them without harming crop plants. This weeds control robot consists of an Ackerman Architecture and delta robot. The delta robot along with two cameras and a technique named visual servoing detects the weed plants and their positions respectively. First of all, through an image acquisition process input image will be acquired from camera and image processing will perform different functions on the input image and provide the position coordinates of weed plants. Then, by using visual servoing technique, delta robot moves to desired location of the weed plants. After that, with the help of a hoe tool the detected weed plants are being removed. Ackerman Architecture will work by navigating itself in the field. The results obtained from this work, is controlled growth rate and removal of weed plants along-with the increase in production of crops yield. Thus, this proposed work will be useful in all the agricultural fields where the usage of modern technology will reduce the use of agrochemicals and herbicides in the fields.
Authors
- Fatimah Khalid (ORCID: https://orcid.org/0000-0002-5791-065X)
- Zubair Mehmood (ORCID: https://orcid.org/0000-0003-2413-2422)
- Muhammad Jehanzeb Irshad
- Rabia Malik
- Wasif Muhammad (ORCID: https://orcid.org/0000-0002-4682-0085)
- Nazam Siddique (ORCID: https://orcid.org/0000-0003-4592-8104)
Institutions
- University of Gujrat (PK)
Publication Details
- Journal
- IET conference proceedings.
- Published
- 2025-04-16
- DOI
- https://doi.org/10.1049/icp.2025.1145
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 0.00