Author(s):
1. Abdul Majid:
Department of Computer Systems Engineering, University of Engineering & Technology,Peshawar,Pakistan
2. Ebtihaj Abdullah:
Department of Computer Systems Engineering, University of Engineering & Technology,Peshawar,Pakistan
3. Waseem Ullah Khan:
Institute of Computer Sciences and Information Technology (ICS/IT), The University of Agriculture, Peshawar, Pakistan
4. Uzair Khalil:
Department of Journalism & Mass Communication, University of Peshawar, Pakistan
Abstract:
Detection of unique plant species leads to important effect on research linked to precise agriculture. Recently, more research is going on for identification of different species of plants, crops and weeds using machine learning and deep learning. Up to 35% people in Pakistan are associated with agriculture, and the mint is also important crop alongside wheat, rice, fruits and vegetables. This thesis presents a detection of new born baby mint plant and other types of weed in its surroundings. Therefore, this thesis is related to detection of two classes. I) Mint Crop II) Weeds. For the detection of weeds, camera will be used. Different crops and weeds are classified through textures, seeding, colors and shapes. Studying plants features leaf is important part for any recognition or detection researches. In such research cases images of special plant and special weeds mostly captured through drones, but smartphone can also be used. And then captured images are further analyzed for dataset purpose. The research purpose is detection of new born mint is necessary as surrounding weeds also compete for neutrals and food of mint. That's why eradication of weeds and feeding uranium to mint is necessary. Referencing and finding location of each mint plant and each weed for remote purpose is also the vital part of this research.
Page(s):
1-1
DOI:
DOI not available
Published:
Journal: Second International Conference on Computing Technologies, Tools and Applications (ICTAPP-24), June 4-6,2024 (Abstract Book), Volume: 0, Issue: 0, Year: 2024
Keywords:
weeds
,
deep learning
,
Precision Agriculture
,
Machine learning
References:
References are not available for this document.
Citations
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