放牧システムにおける農業環境の研究 : 家畜行動のセンシングと空間モデリング

広島大学(Hiroshima University)博士(農学)Doctor of Agriculturedoctora

Bayesian Modeling for Estimating Cattle’s Dung position in Pasture

Livestock excrement is one of the major sources of greenhouse gas (GHG) emission in pasture. As a first step in evaluating its contribution to overall GHG emissions, an understanding of excretion distribution patterns in pastures is required. Betteridge et al. (2010) describe a urine sensor that detects and logs each urination event of female sheep and cattle. The urine sensor records time and ambient temperature at one-second intervals however, patters of dung distribution are not specified. The objective of this study was to predict spatial distribution of cattle dung. The knowledge of livestock excrement position may be useful for farmers to minimize overall GHG emissions

Hyperspectral Assessment for Legume Content and Forage Nutrient Status in Pastures

Symposium mini revie

Hyperspectral Assessment for Legume Contents and Forage Nutrient Status in Pasture

Oral Session

Intensive Livestock Farming on New Zealand Hill Country Farms Creates Critical Source Areas of Potential Pollution

Oral Session

Intensive Livestock Farming on New Zealand Hill Country Farms Creates Critical Source Areas of Potential Pollution

Oral Session

Portable LiDAR-Based Method for Improvement of Grass Height Measurement Accuracy: Comparison with SfM Methods

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