Rapid and accurate biomass and yield estimation facilitates efficient plant phenotyping and site-specific crop management. A low altitude unmanned aerial vehicle (UAV) was used to acquire RGB and hyperspectral imaging data for a potato crop canopy at two growth stages to estimate the above-ground biomass and predict crop yield. Field experiments included six cultivars and multiple treatments of nitrogen, potassium, and mixed compound fertilisers. Crop height was estimated using the difference between digital surface model and digital elevation models derived from RGB imagery. Combining with two narrow-band vegetation indices selected by the RReliefF feature selection algorithm. Random Forest regression models demonstrated high prediction accuracy for both fresh and dry above-ground biomass, with a coefficient of determination (r²) > 0.90. Crop yield was predicted using four narrow-band vegetation indices and crop height (r² = 0.63) with imagery data obtained 90 days after planting. A Partial Least Squares regression model based on the full wavelength spectra demonstrated improved yield prediction (r² = 0.81). This study demonstrated the merits of UAV-based RGB and hyperspectral imaging for estimating the above-ground biomass and yield of potato crops, which can be used to assist in site-specific crop management.

快速，准确的生物量和产量估算有助于有效的植物表型和特定地点的作物管理。使用低空无人机（UAV）在两个生长阶段获取马铃薯作物冠层的RGB和高光谱成像数据，以估算地上生物量并预测作物产量。田间试验包括六个品种，并对氮，钾和混合复合肥进行了多种处理。使用数字表面模型和从RGB图像得出的数字高程模型之间的差异来估算作物高度。结合由RReliefF特征选择算法选择的两个窄带植被指数。随机森林回归模型显示了新鲜和干燥地上生物量的高预测精度，并且具有确定系数（r ²）> 0.90。使用四个窄带植被指数和作物高度（r ²  = 0.63）预测作物产量，并在种植后90天获得图像数据。基于全波长光谱的偏最小二乘回归模型显示出了更高的产量预测（r ²  = 0.81）。这项研究证明了基于无人机的RGB和高光谱成像在估算马铃薯作物地上生物量和产量方面的优势，可用于协助特定地点的作物管理。