Abstract Fertilizer overuse is a common phenomenon in global agroforestry production, and this overuse causes ecological destruction. The ability to accurately estimate the nutrient content of plant leaves in real-time would be a wonderful solution to reduce the degree of environmental damage. In recent years, remote sensing technology has been widely used in the diagnosis of crop nutrition in many countries. Most studies focus on optimal band selection or create new vegetation indices, but these studies have ignored the random impact of natural environmental factors on the estimated results. This paper proposed an estimation model of total nitrogen content (TNC) in sandalwood leaves that takes sampling season and site conditions as the dummy variable and random effect, respectively. Three forestry farms with different locations and site conditions were selected as study areas to enhance the universality of this model. Multispectral images of leaves were obtained using a low-cost five-band camera (RedEdge3, MicaSense, USA), and the experimental results indicate the following: (1) the growth of the tree height, crown width and stem effectively increased under the medium gradient level (N2), whereas a high gradient level (N3) significantly promoted all aspects except tree height; (2) the mean and variance of some image texture features of the G, RE and NIR band were significantly correlated with TNC at the 0.05 and 0.01 levels, and the texture mean value index (TMVI) proposed in this paper can improve the correlation with TNC; and (3) the results obtained using the nonlinear mixed-effects model with dummy variables improved the fitting degree and estimation accuracy compared with results of SVR and BPNN. This study demonstrates the advantages of using the nonlinear mixed-effects model with dummy variables to obtain a more reliable estimation model for the nutritional diagnosis of rare tree species.

中文翻译：

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基于非线性混合效应和虚拟变量的多光谱图像估计檀香叶总氮含量模型

摘要 化肥过度使用是全球农林业生产中普遍存在的现象，这种过度使用会造成生态破坏。实时准确估计植物叶片营养成分的能力将是减少环境破坏程度的绝佳解决方案。近年来，遥感技术在许多国家广泛应用于作物营养诊断。大多数研究侧重于最佳波段选择或创建新的植被指数，但这些研究忽略了自然环境因素对估计结果的随机影响。提出了以采样季节和立地条件为虚拟变量和随机效应的檀香叶片总氮含量(TNC)估算模型。选择了三个不同位置和立地条件的林场作为研究区域，以增强该模型的普遍性。使用低成本的五波段相机（RedEdge3，MicaSense，USA）获得树叶的多光谱图像，实验结果表明：（1）在培养基下有效增加了树高、树冠宽度和茎的生长梯度水平（N2），而高梯度水平（N3）显着提升了除树高以外的所有方面；(2) G、RE和NIR波段部分图像纹理特征的均值和方差在0.05和0.01水平上与TNC显着相关，本文提出的纹理均值指数(TMVI)可以提高与TNC的相关性。跨国公司; (3)与SVR和BPNN的结果相比，使用带有虚拟变量的非线性混合效应模型得到的结果提高了拟合程度和估计精度。本研究证明了使用带有虚拟变量的非线性混合效应模型为稀有树种的营养诊断获得更可靠的估计模型的优势。