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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. [ABSTRACT FROM AUTHOR]

Abstract: In this paper we propose an evolutionary method of association rules discovery (EQAR, Evolutionary Quantitative Association Rules) that extends a recently published algorithm by the authors and we describe its application to a problem of Total Ozone Content (TOC) modeling in the Iberian Peninsula. We use TOC data from the Total Ozone Mapping Spectrometer (TOMS) on board the NASA Nimbus-7 satellite measured at three locations (Lisbon, Madrid and Murcia) of the Iberian Peninsula. As prediction variables for the association rules we consider several meteorological variables, such as Outgoing Long-wave Radiation (OLR), Temperature at 50hPa level, Tropopause height, and wind vertical velocity component at 200hPa. We show that the best association rules obtained by EQAR are able to accurate modeling the TOC data in the three locations considered, providing results which agree to previous works in the literature. [Copyright &y& Elsevier]