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Validation of In-House Imaging System via Code Verification on Petunia Images Collected at Increasing Fertilizer

Kahlin Wacker1, Changhyeon Kim2, Marc W van Iersel1

  • 1Department of Horticulture, University of Georgia, Athens, GA 30602, USA.

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A custom imaging system effectively quantifies plant stress from fertilizer changes. This low-cost system analyzes plant canopy reflectance, aiding in early stress detection for improved crop yield.

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Area of Science:

  • Horticulture
  • Plant Physiology
  • Agricultural Engineering

Background:

  • Delayed stress recognition in production environments can decrease crop yield.
  • Imaging technologies, like normalized difference vegetation index (NDVI), offer rapid plant stress assessment.
  • Commercial imaging systems lack customization for specific post-processing needs.

Purpose of the Study:

  • To develop and validate a low-cost, customizable imaging system for plant stress analysis.
  • To verify the image analysis code's ability to quantify changes in plant canopy reflectance.
  • To assess the system's effectiveness in quantifying nutrient deficiency-induced variability.

Main Methods:

  • A custom, low-cost imaging system was developed and validated.
  • Petunia plants ('Supercascade Red', 'Wave© Purple', 'Carpet Blue') were subjected to varying fertilizer rates and substrate pH levels.
  • Images were captured using both commercial and custom systems, and analyzed with in-house software calculating pixel-level indices.

Main Results:

  • Increasing fertilizer rates significantly impacted projected canopy size, biomass, and nitrogen concentration across all cultivars.
  • The image analysis code successfully quantified fertilizer-induced changes in plant canopy reflectance.
  • No significant relationship was found between substrate pH and NDVI values, canopy size, or chlorophyll fluorescence for most cultivars.

Conclusions:

  • The custom imaging system and analysis code effectively quantify plant stress responses to nutrient variations.
  • The system provides a customizable and low-cost solution for monitoring plant health and optimizing agricultural practices.
  • Substrate pH had a limited impact on the measured plant parameters compared to fertilizer levels.