Unmanned Aerial Vehicle (UAV) platforms and associated sensing technologies are extensively utilized in precision agriculture. Using LiDAR and imaging sensors mounted on multirotor UAVs, we can observe fine-scale crop variations that can help improve fertilizer management and maximize yields. In this study we used UAV mounted LiDAR and multispectral imaging sensors to monitor two sugarcane field trials with variable nitrogen (N) fertilization inputs in the Wet Tropics region of Australia. From six surveys performed at 42-day intervals, we monitored crop growth in terms of height, density and vegetation indices. In each survey period, we estimated a set of models to predict at-harvest biomass at fine scale ($2\mathrm{m}\times 2\mathrm{m}$ plots). We compared the predictive performance of models based on multispectral predictors only, LiDAR predictors only, a fusion of multispectral and LiDAR predictors, and a normalized difference vegetation index (NDVI) benchmark. We found that predictive performance peaked early in the season, at 100–142 days after the previous harvest (DAH), and declined closer to the harvest date. At peak performance (i.e. 142 DAH), the multispectral model performed slightly better (${\overline{R}}^2=0.57$) than the LiDAR model (${\overline{R}}^2=0.52$), with both outperforming NDVI benchmark (${\overline{R}}^2=0.34$). This, however, flipped later in the season, with LiDAR performing slightly better than the multispectral imaging and NDVI benchmark. Interestingly, the fusion model did not perform significantly better than the multispectral model at 100–142 DAH, nor better than LiDAR in subsequent periods. We also estimated a model to predict contemporaneous leaf $N$ content (%) using multispectral imagery, which demonstrated an ${\overline{R}}^2$ of 0.57. Our results are of particular interest to nutrient management programs aiming to deliver $N$ fertilizer guidelines for sustainable sugarcane production, as both fine-scale biomass and leaf $N$ content predictions are feasible when management interventions are still possible (i.e. as early as at 100 DAH).

无人机（UAV）平台和相关的传感技术已广泛用于精密农业。使用安装在多旋翼无人机上的LiDAR和成像传感器，我们可以观察到小规模的作物变化，从而有助于改善肥料管理并最大程度地提高产量。在这项研究中，我们使用了安装在无人机上的LiDAR和多光谱成像传感器，以监测澳大利亚湿热带地区氮肥输入可变的两个甘蔗田间试验。通过以42天为间隔进行的六次调查，我们在高度，密度和植被指数方面监测了作物的生长。在每个调查期内，我们估算了一组模型，以精细地预测收获时的生物量（$2\mathrm{米}\times 2\mathrm{米}$情节）。我们比较了仅基于多光谱预测器，仅基于LiDAR预测器，多光谱和LiDAR预测器的融合以及归一化差异植被指数（NDVI）基准的模型的预测性能。我们发现，预测性能在季节初达到高峰，在上次收获（DAH）后100-142天，并在接近收获日期时下降。在最高性能（即142 DAH）下，多光谱模型的性能稍好一些（${\overline{\mathrm{[R}}}^2=0.57$）比LiDAR模型（${\overline{\mathrm{[R}}}^2=0.52$），两者均胜过NDVI基准测试（${\overline{\mathrm{[R}}}^2=0.34$）。然而，这一情况在本季度晚些时候发生了变化，LiDAR的性能略好于多光谱成像和NDVI基准。有趣的是，在100-142 DAH时，融合模型的性能并没有比多光谱模型好得多，在随后的时期也没有比LiDAR好。我们还估计了预测同期叶片的模型$ñ$ 含量（％）使用多光谱图像显示， ${\overline{\mathrm{[R}}}^2$为0.57。我们的结果对于旨在提供营养的营养管理计划特别有意义$ñ$ 可持续甘蔗生产的肥料指南，包括细规模的生物质和叶片 $ñ$ 当仍然可以进行管理干预时（即早在100 DAH时），内容预测是可行的。