Department of Chemical and Biomedical Engineering, University of Missouri, Columbia, MO 65211, USA.
Department of Smart Agricultural System, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea.
Sensors (Basel). 2024 Aug 17;24(16):5326. doi: 10.3390/s24165326.
(1) Background: Yield-monitoring systems are widely used in grain crops but are less advanced for hay and forage. Current commercial systems are generally limited to weighing individual bales, limiting the spatial resolution of maps of hay yield. This study evaluated an Uncrewed Aerial Vehicle (UAV)-based imaging system to estimate hay yield. (2) Methods: Data were collected from three 0.4 ha plots and a 35 ha hay field of red clover and timothy grass in September 2020. A multispectral camera on the UAV captured images at 30 m (20 mm pixel) and 50 m (35 mm pixel) heights. Eleven Vegetation Indices (VIs) and five texture features were calculated from the images to estimate biomass yield. Multivariate regression models (VIs and texture features vs. biomass) were evaluated. (3) Results: Model R values ranged from 0.31 to 0.68. (4) Conclusions: Despite strong correlations between standard VIs and biomass, challenges such as variable image resolution and clarity affected accuracy. Further research is needed before UAV-based yield estimation can provide accurate, high-resolution hay yield maps.
(1) 背景：产量监测系统在粮食作物中得到了广泛应用，但在干草和饲料方面的应用还不够先进。目前的商业系统通常仅限于对单个草捆进行称重，限制了干草产量图的空间分辨率。本研究评估了一种基于无人机的成像系统来估算干草产量。
(2) 方法：数据采集自 2020 年 9 月的三个 0.4 公顷的试验区和一个 35 公顷的红三叶草和梯牧草干草田。无人机上的多光谱相机以 30 米（20 毫米像素）和 50 米（35 毫米像素）的高度拍摄图像。从图像中计算了 11 个植被指数（VIs）和 5 个纹理特征，以估算生物量产量。评估了多元回归模型（VIs 和纹理特征与生物量）。
(3) 结果：模型 R 值范围为 0.31 至 0.68。
(4) 结论：尽管标准 VIs 与生物量之间存在很强的相关性，但图像分辨率和清晰度等变量的变化会影响准确性。在基于无人机的产量估算能够提供准确、高分辨率的干草产量图之前，还需要进一步研究。
