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 (R2 = 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 mon-itoring 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.@en

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