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

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.

Keywords

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

Published in

Computers and Electronics in Agriculture
2018, Volume: 144, pages: 154-163 Publisher: ELSEVIER SCI LTD

SLU Authors

• Zhou, Zhenjiang

  • Aarhus University
  • Department of Agricultural Research for Northern Sweden, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Agricultural Science


Publication identifier

DOI: https://doi.org/10.1016/j.compag.2017.12.005

Permanent link to this page (URI)

https://res.slu.se/id/publ/94352