Jihyun Lim

Mapping herbage biomass and nitrogen status in an Italian ryegrass (Lolium multiflorum L.) field using a digital video camera with balloon system

Improving current precision nutrient management requires practical tools to aid the collection of site specific data. Recent technological developments in commercial digital video cameras and the miniaturization of systems on board low-altitude platforms offer cost effective, real time applications for efficient nutrient management. We tested the potential use of commercial digital video camera imagery acquired by a balloon system for mapping herbage biomass (BM), nitrogen (N) concentration, and herbage mass of N (Nmass) in an Italian ryegrass (Lolium multiflorum L.) meadow. The field measurements were made at the Setouchi Field Science Center, Hiroshima University, Japan on June 5 and 6, 2009. The field consists of two 1.0 ha Italian ryegrass meadows, which are located in an east-facing slope area (230 to 240 m above sea level). Plant samples were obtained at 20 sites in the field. A captive balloon was used for obtaining digital video data from a height of approximately 50 m (approximately 15 cm spatial resolution). We tested several statistical methods, including simple and multivariate regressions, using forage parameters (BM, N, and Nmass) and three visible color bands or color indices based on ratio vegetation index and normalized difference vegetation index. Of the various investigations, a multiple linear regression (MLR) model showed the best cross validated coefficients of determination (R2) and minimum root-mean-squared error (RMSECV) values between observed and predicted herbage BM (R2 = 0.56, RMSECV = 51.54), Nmass (R2 = 0.65, RMSECV = 0.93), and N concentration (R2 = 0.33, RMSECV = 0.24). Applying these MLR models on mosaic images, the spatial distributions of the herbage BM and N status within the Italian ryegrass field were successfully displayed at a high resolution. Such fine-scale maps showed higher values of BM and N status at the bottom area of the slope, with lower values at the top of the slope.

Retrieval of Chlorophyll-a and Total Suspended Solids Using Iterative Stepwise Elimination Partial Least Squares (ISE-PLS) Regression Based on Field Hyperspectral Measurements in Irrigation Ponds in Higashihiroshima, Japan

Concentrations of chlorophyll-a (Chl-a) and total suspended solids (TSS) are significant parameters used to assess water quality. The objective of this study is to establish a quantitative model for estimating the Chl-a and the TSS concentrations in irrigation ponds in Higashihiroshima, Japan, using field hyperspectral measurements and statistical analysis. Field experiments were conducted in six ponds and spectral readings for Chl-a and TSS were obtained from six field observations in 2014. For statistical approaches, we used two spectral indices, the ratio spectral index (RSI) and the normalized difference spectral index (NDSI), and a partial least squares (PLS) regression. The predictive abilities were compared using the coefficient of determination (R2), the root mean squared error of cross validation (RMSECV) and the residual predictive deviation (RPD). Overall, iterative stepwise elimination based on PLS (ISE–PLS), using the first derivative reflectance (FDR), showed the best predictive accuracy, for both Chl-a (R2 = 0.98, RMSECV = 6.15, RPD = 7.44) and TSS (R2 = 0.97, RMSECV = 1.91, RPD = 6.64). The important wavebands for estimating Chl-a (16.97% of all wavebands) and TSS (8.38% of all wavebands) were selected by ISE–PLS from all 501 wavebands over the 400–900 nm range. These findings suggest that ISE–PLS based on field hyperspectral measurements can be used to estimate water Chl-a and TSS concentrations in irrigation ponds.

A simple visible and near-infrared (V-NIR) camera system for monitoring the leaf area index and growth stage of Italian ryegrass

Abstract Crop growth stage is critical for making decisions in nutrient management and for evaluating crop productivity. In this study, a simple visible and near-infrared (V-NIR) camera system was developed for monitoring the leaf area index (LAI) and quantifying the quick growth stage (QGS) of Italian ryegrass. RAW format images in the red, green and NIR channels over two growing seasons of 2014–15 and 2015–16 were captured hourly each day by the V-NIR camera system installed in three Italian ryegrass fields at the farm of Hiroshima University. Multiple linear regression (MLR) models that predict the forage LAI from the imagery data were calibrated and validated, with high coefficient of determination ( R 2 = 0.79 ) and low root-mean-square error ( RMSE = 1.09 ) between the measured and predicted LAIs. The predicted LAI to which three vegetation indices were compared was fitted against a logistic model to extract forage QGS from smoothed time-series data under various micro-meteorological and nutrient conditions. The result shows the time-series data of LAI can be applied for monitoring seasonal changes regardless of the environmental conditions. The RMSE of the predicted phenology dates against the field-measured LAI was 0.58 and 5.2 days for the start- and end-QGS, respectively, under the high-yield condition in season 1. However, in season 2, only the start-QGS was identifiable, with an RMSE of 2.65 days under the nutritional stress condition. The forage LAI and QGS were predicted and identified with acceptable accuracy and reliability, which suggests that the V-NIR camera system can be employed as a cost-effective approach for monitoring seasonal changes in crop growth, aiding in better personalized crop and nutrient management.