Explainable Droplet Recognition System for Precision Sprayer Applications

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AI-driven detection systems are playing an increasingly important role in the advancement of precision agriculture. In this paper, we have implemented a transfer learning pipeline for water droplet detection with the intent to develop quantifiable and real-time detection of post-spray areas for precision spraying applications. The object detection pipeline effectively identified multiple features for water droplet detection from the three curated datasets. We have used two pre-trained convolutional backbones as the feature extractor and achieved an overall detection mean average precision across the three curated datasets of 0.409 and 0.277 for the ResNet50, and MobileNetV3-Large backbones respectively. Additionally, for visual explanations and interpretation, we implemented EigenCAM class activation mapping techniques to highlight the regions of the input images that are important for predictions.
Original languageEnglish
Number of pages2
Publication statusPublished - 13 Jul 2022
EventFARSCOPE CDT Conference - Bristol, Bristol, United Kingdom
Duration: 11 Jul 202215 Jul 2022


ConferenceFARSCOPE CDT Conference
Country/TerritoryUnited Kingdom
Internet address


  • Explainable AI
  • Precision Spraying
  • Computer vision

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