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This study assesses push-broom and snapshot hyperspectral sensors for precision viticulture. Both sensor types offer excellent geometric quality for UAV data collection, aiding agricultural insights.

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Area of Science:

  • Agricultural Science
  • Remote Sensing
  • UAV Technology

Background:

  • Hyperspectral aerial imagery is increasingly accessible.
  • Selecting appropriate UAV and hyperspectral sensor combinations for specific applications like precision viticulture remains challenging.
  • Documented support for sensor selection is limited.

Purpose of the Study:

  • To assess the practicality and suitability of push-broom and snapshot hyperspectral sensors in precision viticulture.
  • To provide researchers, agronomists, winegrowers, and UAV pilots with reliable data collection protocols.
  • To enable faster processing techniques and facilitate the integration of multiple data sources.

Main Methods:

  • Qualitative and quantitative analysis of hyperspectral sensors (push-broom and snapshot), UAVs, flight operations, and processing methodologies.
  • Case studies conducted in four vineyards across two countries.
  • Comparison of sensor performance based on field operations complexity, processing time, and accuracy of hyperspectral mosaics.

Main Results:

  • Both push-broom and snapshot hyperspectral sensors demonstrated excellent geometrical quality in generated mosaics, with no distortions or overlapping faults using the proposed mosaicking process.
  • The multi-site assessment facilitated information exchange within the UAV hyperspectral community.
  • Major benefits and drawbacks of each sensor type regarding operation and data features were identified.

Conclusions:

  • The study provides valuable insights into the operational complexity and data characteristics of different hyperspectral sensors for precision agriculture.
  • Dependable data collection protocols and methods are established for UAV-based hyperspectral imaging in viticulture.
  • The findings support informed decision-making for selecting hyperspectral sensor technologies in precision agriculture.