Using active canopy sensors and chlorophyll meters to estimate grapevine nitrogen status and productivity

Abstract

Effects of nitrogen (M) and water supply on grapevine leaf transmittance and canopy reflectance were studied over 2 years (2009)(2010) in two vineyards planted with cvs. Cabernet Sauvignon and Xinomavro (Vitis vinifera L.) located in northern Greece. Three N (0, 60 and 120 kg ha -1 ) and two irrigation (irrigated at 70 % of crop evapotranspiration and non-irrigated) treatments were managed in triplicate in randomized blocks. Measurements with two transmittance-based chlorophyll meters (CM: SPAD-502 and CCM-200) and two active canopy sensors (Crop Circle ACS-210; amber band and Crop Circle ACS-430; red and red edge bands) were conducted on four and two growth stages, respectively. Fertilization increased leaf N and chlorophyll concentrations, as well as CM readings and vegetation indices. The CCM and SPAD values did not vary across growth stages and better estimated leaf N and chlorophyll concentrations in Cabernet Sauvignon than in Xinomavro. The SPAD readings provided better predictions of leaf N concentration. The vegetation indices of the ACS-430 sensor correlated with leaf N and chlorophyll concentrations and dormant wood in Cabernet Sauvignon; the red edge-based indices were generally more efficient than the red-based indices. Consistent relationships were observed for all ACS-430 indices with yield and for red edge-based indices with total phenols early in the season for both vineyards. The correlation between ACS-210 sensor indices and vine properties was low. These results showed that the two technologies examined could provide information on grapevine performance early in the season but have limitations that may restrict their use in vineyards because the acquired relationships were not consistent across vineyards and instruments.

Normalized difference vegetation index obtained by ground-based remote sensing to characterize vine cycle in Rio Grande do Sul, Brazil

“…The NDVI relates the reflectance in the red and near-infrared wavelengths, being considered an indicator of vegetation growth and green biomass accumulation. In vineyards, studies have shown that the NDVI allows interpreting the spatial patterns of leaf area index (Johnson et al, 2003;Drissi et al, 2009), biomass accumulation (Tardáguila et al, 2008;Stamatiadis et al, 2010), diseases (Mazzeto et al, 2010;Di Gennaro et al, 2016) and chlorophyll and nitrogen contents in leaves (Taskos et al, 2015).…”

The development of a low cost UAV-based image acquisition system and the procedure for capturing data in precision agriculture

“…They successfully recorded images using the system and reported that UAV development offers an affordable alternative with respect to conventional remote sensing. Costa Ferreira et al (2007) and Taskos et al (2014) studied similar objectives. Berni et al (2009) demonstrated that rotary wing UAVs equipped with thermal and multispectral sensors have the potential to monitor water stress on crops.…”

Sensors for measuring plant phenotyping: A review

“…GreenSeeker Model 505 (red at 656 nm and NIR at 774 nm) and CropCircle ACS-210 (amber at 590 nm and NIR at 880 nm) were used to collect red and amber canopy NDVI values of winter wheat at three growth stages by Samborski et al [83] , they found that genotype has an effect on both red NDVI values and amber NDVI values at Zadoks 37 to 39 growth stages, and only on amber NDVI values at 55 to 71 growth stages. CropCircle ACS-210 and ACS 430 (red at 630 nm, red-edge at 730 nm and NIR at 780 nm) were compared by Taskos et al [86] , different NDVI values were calculated and analyzed in each individual waveband.…”

“…The new CropCircle ACS-470 provides filters to select different wavelengths and vegetation indices were also highly related to plant nitrogen [87] , red edge-based indices performed better than NDVI and ratio vegetation index [86] . But plant height, measuring distance, temperature, and reflectance from soil or adjacent rows affect the performance of active sensors, the optimal measuring distance should be adjusted depending on plant architecture and the growth stage, and the distance of sensors beyond 40 cm from the canopy is appropriate [88][89][90][91] .…”