A Review of Application of Deep Learning for Weeds and Crops Classification in Agriculture

Syed I. Moazzam, Umar Shahbaz Khan, Mohsin Islam Tiwana, Javed Iqbal, Waqar S. Qureshi, Syed Irfan Shah

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

25 Citations (Scopus)

Abstract

Weeds are major cause due to which farmers get poor harvest of crops. Many algorithms are developed to classify weeds from crops to autonomously destroy weeds. Color-based, threshold-based and learning-based techniques are deployed in the past. From all techniques, deep-learning-based techniques stand out by showing the best performances. In this paper, deeplearning-based techniques are reviewed in the case where these are applied for weed detection in agricultural crops. Sunflower, carrot, soybean, sugar beet and maize are reviewed with respect to the weeds present in them. Deep learning structures and parameters are presented, and research Gaps are identified for further research.

Original languageEnglish
Title of host publication2019 International Conference on Robotics and Automation in Industry, ICRAI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728130583
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes
Event3rd International Conference on Robotics and Automation in Industry, ICRAI 2019 - Rawalpindi, Pakistan
Duration: 21 Oct 201922 Oct 2019

Publication series

Name2019 International Conference on Robotics and Automation in Industry, ICRAI 2019

Conference

Conference3rd International Conference on Robotics and Automation in Industry, ICRAI 2019
Country/TerritoryPakistan
CityRawalpindi
Period21/10/1922/10/19

Keywords

  • Convolutional Neural Networks
  • Deep learning
  • Image processing
  • Precision agriculture
  • Smart farming
  • Weed detection

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