TY - JOUR
T1 - Using ground-based spectral reflectance sensors and photography to estimate shoot N concentration and dry matter of potato
AU - Zhou, Zhenjiang
AU - Jabloun, Mohamed
AU - Plauborg, Finn
AU - Neumann Andersen, Mathias
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Amera image analysis
KW - Normalized vegetation index
KW - Drip irrigation
KW - Ground coverage
KW - Amera image analysis
KW - Normalized vegetation index
KW - Drip irrigation
KW - Ground coverage
UR - https://res.slu.se/id/publ/94352
U2 - 10.1016/j.compag.2017.12.005
DO - 10.1016/j.compag.2017.12.005
M3 - Journal article
SN - 0168-1699
VL - 144
SP - 154
EP - 163
JO - Computers and Electronics in Agriculture
JF - Computers and Electronics in Agriculture
ER -