No abstract

This study explored a new hand-held crop growth measuring device to estimate forage quality and quantity of pasture. The device's photosensors (550 nm [green], 650 nm [red] and 880 nm [near-infrared; NIR] regions of the spectrum) are set up in both upward and downward directions, which shorten the measuring time in the field even under unstable weather conditions. The sward canopy reflectance measurements and forage sampling were conducted at 50 sites of pasture in July 2007 and at 20 sites in the same pasture at 4-week intervals from May to October in 2006 and 2007. Using the 50-site dataset, the linear regression analyses between the measured spectral reflectances or the vegetation indices (VIs) and the forage properties were examined to determine the best combinations. Based on the 4-week interval datasets, the following points were examined: (i) effectiveness of the selected combinations throughout stocking seasons; (ii) influence of the sun angle during the spectral measurement; and (iii) integration of the regression models obtained from each dataset. The relationships between the forage properties (total biomass, green biomass and crude protein mass [CP mass , g dry matter m À2 ] in natural and logarithmic [ln] forms) and all of the spectral reflectances or VIs were significant in the cross-validated coefficient of determination (R 2 CV ). In particular, the mean R 2 CV values between the ln CP mass and each of red/NIR ratio, normalized difference vegetation index and modified soil-adjusted vegetation index were high (0.74-0.75), ranging from 0.46 to 0.94 throughout the stocking seasons. The influence of the sun angle on the regression models was not significant in 13 cases out of 14. Additionally, in May and October, the integration of the regression models was statistically accepted, respectively. These results demonstrated that the device is effective for estimating forage CP mass throughout stocking seasons with a little effort.

Automated monitoring systems with different temporal and spatial resolutions can achieve precision agriculture management. Unmanned aerial vehicle (UAV) systems open new possibilities for effectively characterizing the variability within cropping systems with high spatial and temporal resolution. In this study, a UAV with a low‐cost visible and near‐infrared camera assessed the spatial variability in the herbage biomass (BM) and leaf area index (LAI) in an Italian ryegrass field. Using multiple linear regression (MLR) models, high coefficients of determination (R2) and low root‐mean‐squared error (RMSE) values were obtained between the observed and predicted herbage BM (R2 = 0.84, RMSE = 90.43 g m−2) and LAI (R2 = 0.88, RMSE = 0.82). The MLR models successfully recovered high‐resolution spatial distributions of the herbage BM and LAI from the ortho‐photos. The reconstructed maps verified that the proposed method can effectively characterize spatial field variations and assess forage growth to optimize field‐level forage crop management.

The objective of this study is to evaluate the ability of a newly developed hand‐held crop‐measuring device and vegetation indices (VIs) to estimate the herbage biomass (BM), leaf area index (LAI) and forage crude protein mass (CPmass) in an Italian ryegrass (Lolium multiflorum Lam.) field, Japan. The device uses bi‐directional passive sensors (550, 650 and 880 nm) upward and downward to overcome the major drawback of optical remote sensing as influenced by weather conditions. The canopy reflectance and plant sample data were collected 11 times during two winter growing seasons in 2010–11 and 2011–12. Seven VIs were compared to estimate the forage parameters in the normal and logarithmic forms. The predictive ability of the VIs was assessed by the cross‐validated coefficient of determination (R2cv) and the residual prediction (RPD) values. Acceptable RPD values (>1.5) were found in most of the log‐transformed forage parameters with all of the VIs but not in most of the normal‐form. The highest R2cv and RPD were obtained in the normalized difference vegetation index (NDVI) for the ln BM (R2cv = 0.76, RPD = 2.04) and ln LAI (R2cv = 0.80, RPD = 2.25), and the highest modified soil‐adjusted vegetation index (MSAVI) was obtained for the ln CPmass (R2cv = 0.78, RPD = 2.16). By evaluating the limitation of NDVI or MSAVI sensitivity with ranges of cumulative data, the log‐transformed forage parameters showed good R2cv values in the ln BM (0.76–0.79), ln LAI (0.80–0.84) and ln CPmass (0.71–0.84) with acceptable RPD values throughout most of the range, whereas in the normal‐form, the NDVI or MSAVI was saturated at moderate‐high BM, LAI or CPmass, causing the R2cv and RPD values to decrease with increasing plant parameters. These results suggest that this device is applicable in log‐transformed BM, LAI and CPmass throughout the growing season without cloud effect.

Plant height is a key indicator of grass growth. However, its accurate measurement at high spatial density with a conventional ruler is time-consuming and costly. We estimated grass height with high accuracy and speed using the structure from motion (SfM) and portable light detection and ranging (LiDAR) systems. The shapes of leaf tip surface and ground in grassland were determined by unmanned aerial vehicle (UAV)-SfM, pole camera-SfM, and hand-held LiDAR, before and after grass harvesting. Grass height was most accurately estimated using the difference between the maximum value of the point cloud before harvesting, and the minimum value of the point cloud after harvesting, when converting from the point cloud to digital surface model (DSM). We confirmed that the grass height estimation accuracy was the highest in DSM, with a resolution of 50–100 mm for SfM and 20 mm for LiDAR, when the grass width was 10 mm. We also found that the error of the estimated value by LiDAR was about half of that by SfM. As a result, we evaluated the influence of the data conversion method (from point cloud to DSM), and the measurement method on the accuracy of grass height measurement, using SfM and LiDAR.