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.@en

The objectives of this study were to determine the dominant frequency of plant species, and provide useful information toward the conservation management of animal feed in grazing areas, Hiroshima, Japan. The numbers of plant species were 32 and 21 in spring and summer, respectively. The appearance frequency of plant species more than 50% in the spring, consist of Veronica arvensis (75.57%), Trifolium repens (73.86%), Paspalum dilatatum (69.32%), Lamium purpureum (68.75%), Trifolium dubium (65.34%), Cerastium glomeratum (63.64%), whereas in summer, it consists of Paspalum notatum (98.30%), Trifolium repens (81.25%), Paspalum dilatatum (78.98%), and Kyllinga brevifolia (74.43%). It was observed that the plant high of the plant species were 16.64 and 21.55 cm; vegetation cover rates were 77.18 and 81.36%; chlorophyll content were 41.72 and 36.28 mg/g Fw, and species numbers were 17.91 and 10.18, in spring and summer, respectively. Findings of this research propose that Trifolium repens (clover), Rumex japonicus (weed), and Paspalum dilatatum (grass) are dominant species in the studied areas that can be utilized as animal feeds. @en

Information on the spring growth status of winter forage crops is crucial for evaluating productivity and nutrient management. This study aimed to determine the spring quick growth stage (QGS) of Italian ryegrass using a ground-based camera system. The camera system, installed in two Italian ryegrass fields at the farm of Hiroshima University, captured images automatically three times per day in red, green and blue channels over the growing season in 2012–13. Four presumed color intensities/indices were fitted using a logistic model to construct smoothed time-series data. Among the color intensities/indices, excess green was suggested to be the best parameter for monitoring seasonal changes. The root mean squared error of the estimated phenology dates against plant height was 7.7 days for the start-QGS and 2.8 days for the end-QGS. These results from a single year should be broadened to examine other methodologies for image processing and extended to multi-year data.@en