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Using ground-based spectral reflectance sensors and photography to estimate shoot N concentration and dry matter of potato

  • Zhenjiang Zhou
  • , Mohamed Jabloun
  • , Finn Plauborg
  • , Mathias Neumann Andersen

    Publication: Contribution to journalJournal articlepeer-review

    Abstract

    Two years experiments were set up to evaluate the performance of different vegetation indices (VI) to estimate shoot N concentration (N-c) and shoot dry matter (DM) for a potato crop grown under different nitrogen (N) treatments. Possibilities to improve the performance of VI using normalization by leaf area index (LAI) or camera-derived ground cover fraction (GC) were also investigated. Results indicated that N-c was significantly correlated to RRE (Near-infrared divided by red edge reflectance) and RRE/GC with a coefficient of determination (R-2) of 0.62 and 0.78, respectively, indicating that inclusion of auxiliary parameter GC together with RRE substantially improved the correlation as compared to using only RRE. However, no significant correlation between N-c and RVI (Ratio Vegetation Index, near-infrared divided by red reflectance) or NDVI (Normalized Difference Vegetation Index) was found. However, DM was highly correlated to RVI and NDVI. Moreover, DM showed significant relationship (R-2 = 0.86) with GC, highlighting its versatile usefulness in estimating agronomic variables DM and No which are the core variables to assess N status of crops for a better N application.
    Original languageEnglish
    Pages (from-to)154-163
    Number of pages10
    JournalComputers and Electronics in Agriculture
    Volume144
    DOIs
    Publication statusPublished - 2018

    Keywords

    • Amera image analysis
    • Normalized vegetation index
    • Drip irrigation
    • Ground coverage

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