Application-specific evaluation of a weed-detection algorithm for plant-specific spraying

Thijs Ruigrok*, Eldert van Henten, Johan Booij, Koen van Boheemen, Gert Kootstra

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Robotic plant-specific spraying can reduce herbicide usage in agriculture while minimizing labor costs and maximizing yield. Weed detection is a crucial step in automated weeding. Currently, weed detection algorithms are always evaluated at the image level, using conventional image metrics. However, these metrics do not consider the full pipeline connecting image acquisition to the site-specific operation of the spraying nozzles, which is vital for an accurate evaluation of the system. Therefore, we propose a novel application-specific image-evaluation method, which analyses the weed detections on the plant level and in the light of the spraying decision made by the robot. In this paper, a spraying robot is evaluated on three levels: (1) On image-level, using conventional image metrics, (2) on application-level, using our novel application-specific image-evaluation method, and (3) on field level, in which the weed-detection algorithm is implemented on an autonomous spraying robot and tested in the field. On image level, our detection system achieved a recall of 57% and a precision of 84%, which is a lower performance than detection systems reported in literature. However, integrated on an autonomous volunteer-potato sprayer-system we outperformed the state-of-the-art, effectively controlling 96% of the weeds while terminating only 3% of the crops. Using the application-level evaluation, an accurate indication of the field performance of the weed-detection algorithm prior to the field test was given and the type of errors produced by the spraying system was correctly predicted.

Original languageEnglish
Article number7262
JournalSensors (Switzerland)
Volume20
Issue number24
DOIs
Publication statusPublished - 18 Dec 2020

Keywords

  • Agricultural robotics
  • Deep learning
  • Field test
  • Weed detection
  • Weed removal

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