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Reseach Article

Analysis on Weed Identification using Deep Learning and Image Processing in Vegetable Plantation

by Rahul, Rajeev Thakur
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 1
Year of Publication: 2023
Authors: Rahul, Rajeev Thakur
10.5120/ijca2023922612

Rahul, Rajeev Thakur . Analysis on Weed Identification using Deep Learning and Image Processing in Vegetable Plantation. International Journal of Computer Applications. 185, 1 ( Apr 2023), 19-23. DOI=10.5120/ijca2023922612

@article{ 10.5120/ijca2023922612,
author = { Rahul, Rajeev Thakur },
title = { Analysis on Weed Identification using Deep Learning and Image Processing in Vegetable Plantation },
journal = { International Journal of Computer Applications },
issue_date = { Apr 2023 },
volume = { 185 },
number = { 1 },
month = { Apr },
year = { 2023 },
issn = { 0975-8887 },
pages = { 19-23 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number1/32669-2023922612/ },
doi = { 10.5120/ijca2023922612 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:24:58.317106+05:30
%A Rahul
%A Rajeev Thakur
%T Analysis on Weed Identification using Deep Learning and Image Processing in Vegetable Plantation
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 1
%P 19-23
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This analysis should determine which weeds are present in the field and the density of those weeds so that herbicides targeting those weeds may be selected. The process of identifying the weeds become more challenging when both plant and weed regions overlap (inter-leaves). The proposed system addresses this problem by creating a sophisticated means for weed identification. The major components of this system are composed of three processes: Image Segmentation, Feature Extraction, and Decision-Making. In the Image Segmentation process, the input images are processed into lower units where the relevant features are extracted.

References
  1. Xiaojun Jin , Jun Che , And Yong Chen “Weed Identification Using Deep Learning and Image Processing in Vegetable Plantation” 2021.
  2. Md. Najmul Mowla, Mustafa Gok “Weeds Detection Networks” 2021.
  3. Chung-Liang Chang , Bo-Xuan Xie and Sheng-Cheng Chung “Mechanical Control with a Deep Learning Method for Precise Weeding on a Farm” Volume 11 Issue 11 2021.
  4. Chiranjeevi Muppala, Velmathi Guruviah“Machine vision detection of pests, diseases and weeds” 2020.
  5. Aichen Wang , Yifei Xu , Xinhua Wei , And Bingbo Cui “Semantic Segmentation of Crop and Weed using an Encoder-Decoder Network and Image Enhancement Method under Uncontrolled Outdoor Illumination”2020.
  6. Rekha Raja , Thuy T. Nguyen, David C. Slaughter, Steven A. Fennimore “Real-time weed-crop classification and localisation technique for robotic weed control in lettuce”2020.
  7. Asha KR, Aman Mahore, Pankaj Malkani and Akshay Kumar Singh “Robotics-automation and sensor-based approaches in weed detection and control”2020.
  8. Wen-Hao Su “Advanced Machine Learning in Point Spectroscopy, RGB- and Hyperspectral-Imaging for Automatic Discriminations of Crops and Weeds: A Review” Published: 01 August 2020.
  9. Tanzeel U. Rehmana, Md. Sultan Mahmudb, Young K. Changb, Jian Jina , Jaemyung Shin “Current and future applications of statistical machine learning algorithms for agricultural machine vision systems” Volume 156, January 2019, Pages 585-605 2019.
  10. Aichen Wang , Wen Zhangb , Xinhua Weia “A review on weed detection using ground-based machine vision and image processing techniques” Volume 158, March 2019, Pages 226-240
  11. Om Tiwari, Vidit Goyal, Pramod Kumar, Sonakshi Vij “An experimental set up for utilizing convolutional neural network in automated weed detection” 2019.
  12. Mahdi Maktabdar Oghaz , Manzoor Razaak, Hamideh Kerdegari, Vasileios Argyriou, Paolo Remagnino “Scene and Environment Monitoring Using Aerial Imagery and Deep Learning” 2019.
  13. Shinji Kawakura “Accuracy Analyses for Detecting Small Creatures Using an OpenCV-Based System with AI for Caffe’s Deep Learning Framework” Vol. 6, No. 3, September 2019.
  14. Radhika Shetty , Roopa G “Weed Detection using Image Filtering in Vegetables Crops” ISSN: 2278-0661,p-ISSN: 2278-8727, Volume 21, Issue 1, Ver. I (Jan - Feb 2019), PP 61-64 2019.
  15. R. P. L. Durgabai, P. Bhargavi & S. Jyothi “Pest Management Using Machine Learning Algorithms: A Review” ISSN (P): 2249-6831; ISSN (E): 2249-7943 Vol. 8, Issue 1, Feb 2018, 13-22 2018.
Index Terms

Computer Science
Information Sciences

Keywords

Weed identification deep learning image processing genetic algorithms color index.