Classification of Weeds of Paddy Fields using Deep Learning

Radhika Kamath, Mamatha Balachandra, Amodini Vardhan, Ujjwal Maheshwari

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


Weed management is one of the important tasks in agriculture. Weeds in rice fields are usually managed using three ways-chemical herbicides, mechanical weeders, and manual weeding. Manual weeding becomes a prob-lem when there is a shortage of agricultural laborers. Mechanical weeders are not suitable for direct-seeded rice fields. Chemical herbicides are not advisable especially when farmers do not know about site-specific weed management. Site-specific weed management is using the right herbicide in the right amount. Therefore, this paper investigates computer vision-based deep learning techniques with transfer learning classifying three types of weeds in paddy fields, namely sedges, grasses, and broadleaved weeds so that the right herbicide is recommended to the farmers. This would reduce the broadcast application and the overuse of the herbicides, thereby lim-iting the negative impact of the chemical herbicides on the environment. This research work shows promising results with an accuracy around 90% and thus encourages the development of digital agriculture.

Original languageEnglish
Pages (from-to)365-377
Number of pages13
JournalECTI Transactions on Computer and Information Technology
Issue number4
Publication statusPublished - 02-09-2022

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications
  • Information Systems and Management
  • Electrical and Electronic Engineering


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