Evaluating a hand‐held crop‐measuring device for estimating the herbage biomass, leaf area index and crude protein content in an Italian ryegrass field

Abstract

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.

Constructing Italian ryegrass yield prediction model based on climatic data by locations in South Korea

“…However, previous studies on crop yield prediction were mainly focusing on food and cash crops by using crop growth models, which need lots of environmental variables because the models simulate the crop growth and development through mathematical functions of environmental conditions and management practices, or grasslands with large area which are suitable for using remote sensing to predict the biomass yield (Hoogenboom et al 2004;Basso et al 2013;Lin and Zhang 2013;Ahn et al 2014). However, these sorts of studies rely on facilities that are inconvenient to farmers (Watanabe et al 2014;Lim et al 2015;Fan et al 2016). However, these sorts of studies rely on facilities that are inconvenient to farmers (Watanabe et al 2014;Lim et al 2015;Fan et al 2016).…”

“…Several studies on using optical devices to predict forage crop biomass were carried out. However, these sorts of studies rely on facilities that are inconvenient to farmers (Watanabe et al 2014;Lim et al 2015;Fan et al 2016). Therefore, for forage crops, yield prediction research that could be helpful for cultivation management and forage production arrangement is very necessary.…”

Simple in‐field evaluation of moisture content in curing forage using normalized differece vegetation index (NDVI)

“…Moreover, in the linear regression analyses between the MC and NDSIs, good correlations were revealed mainly for the combinations with these chlorophyll‐related bands (Figure ). These combinations have been widely used as a vegetation index to assess the vegetation greenness—for example, the pair of green and red, that is, the green‐red vegetation index (GRVI) (Falkowski, Gessler, Morgan, Hudak, & Smith, ; Tucker, ), and the pair of red and NIR, that is, the NDVI (Rouse, Haas, Schell, & Deering, )—to determine the biophysical and biochemical properties of plant such as the plant crude protein mass, the aboveground biomass and the leaf area index (Lim et al, ; Watanabe et al, ). Although the NDVI is the most widely used as the vegetation index in vegetation surveys of living plants, few studies have investigated the utility of the NDVI for the estimation of curing forages' MC in the context of hay production.…”

Discriminating Pennisetum alopecuoides plants in a grazed pasture from unmanned aerial vehicles using object‐based image analysis and random forest classifier

“…The present study used UAV images used in our previous study (Yuba et al., 2020); the UAV image data were acquired at heading stage of P. alopecuroide plants in 24 September 2016, from a grazed paddock (1.4 ha) at the Setouchi Field Science Center, Saijo Station, Graduate School of Biosphere Science, Hiroshima University, Japan (N34º24′, E132º43′). The area is located in a temperate zone with a warm, humid summer, and a cool, dry winter (Lim et al., 2015). The mean annual precipitation is 14.6ºC, and the annual precipitation was 1960 mm in 2016.…”