In forest modeling to estimate the volume of wood, artificial intelligence has been shown to be quite efficient, especially using artificial neural networks (ANNs). Here we tested whether diameter at breast height (DBH) and the total plant height (Ht) of eucalyptus can be predicted at the stand level using spectral bands measured by an unmanned aerial vehicle (UAV) multispectral sensor and vegetation indices. To do so, using the data obtained by the UAV as input variables, we tested different configurations (number of hidden layers and number of neurons in each layer) of ANNs for predicting DBH and Ht at stand level for different Eucalyptus species. The experimental design was randomized blocks with four replicates, with 20 trees in each experimental plot. The treatments comprised five Eucalyptus species (E. camaldulensis, E. uroplylla, E. saligna, E. grandis, and E. urograndis) and Corymbria citriodora. DBH and Ht for each plot at the stand level were measured seven times in separate overflights by the UAV, so that the multispectral sensor could obtain spectral bands to calculate vegetation indices (VIs). ANNs were then constructed using spectral bands and VIs as input layers, in addition to the categorical variable (species), to predict DBH and Ht at the stand level simultaneously. This report represents one of the first applications of high-throughput phenotyping for plant size traits in Eucalyptus species. In general, ANNs containing three hidden layers gave better statistical performance (higher estimated r, lower estimated root mean squared error–RMSE) due to their greater capacity for self-learning. Among these ANNs, the best contained eight neurons in the first layer, seven in the second, and five in the third (8 − 7 − 5). The results reported here reveal the potential of using the generated models to perform accurate forest inventories based on spectral bands and VIs obtained with a UAV multispectral sensor and ANNs, reducing labor and time.

在估计木材体积的森林建模中，人工智能已被证明非常有效，尤其是使用人工神经网络 (ANN)。在这里，我们测试了是否可以使用无人机 (UAV) 多光谱传感器和植被指数测量的光谱带在林分水平预测桉树的胸高 (DBH) 直径 (DBH) 和总株高 (Ht)。为此，使用 UAV 获得的数据作为输入变量，我们测试了 ANN 的不同配置（隐藏层数和每层神经元数），以预测不同桉树物种的立柱水平的 DBH 和 Ht 。实验设计是随机区组，有四个重复，每个实验小区有 20 棵树。处理包括五种桉树种（E. camaldulensis、E. uroplylla、E. saligna、E. grandis和E. urograndis）和Corymbria citriodora。无人机在单独的飞越中测量了林分水平每个地块的 DBH 和 Ht 7 次，以便多光谱传感器可以获得光谱带来计算植被指数 (VI)。然后，除了分类变量（物种）之外，还使用光谱带和 VI 作为输入层构建了人工神经网络，以同时预测林分级别的 DBH 和 Ht。该报告代表了桉树植物大小性状高通量表型分析的首批应用之一物种。一般来说，包含三个隐藏层的 ANN 具有更好的统计性能（更高的估计r，更低的估计均方根误差 -RMSE），因为它们具有更大的自学习能力。在这些人工神经网络中，最好的在第一层包含 8 个神经元，在第二层包含 7 个神经元，在第三层包含 5 个 (8 − 7 − 5)。此处报告的结果揭示了使用生成的模型根据无人机多光谱传感器和 ANN 获得的光谱带和 VI 执行准确森林清单的潜力，从而减少劳动力和时间。