Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China.
PLoS One. 2018 Aug 21;13(8):e0202649. doi: 10.1371/journal.pone.0202649. eCollection 2018.
This paper presents a method for predicting the total nitrogen content in sandalwood using digital image processing. The goal of this study is to provide a real-time, efficient, and highly automated nutritional diagnosis system for producers by analyzing images obtained in forests. Using images acquired from field servers, which were installed in six forest farms of different cities located in northern Hainan Province, we propose a new segmentation algorithm and define a new indicator named "growth status" (GS), which includes two varieties: GSMER (the ratio of sandalwood pixels to the minimum enclosing rectangle pixels) and GSMCC (the ratio of sandalwood pixels to minimum circumscribed circle pixels). We used the error-in-variable model by considering the errors that exist in independent variables. After comparison and analysis, the obtained results show that (1) The b and L channels in the Lab color system have complementary advantages. By combining this system with the Otsu method, median filtering and a morphological operation, sandalwood can be separated from the background. (2) The fitting degree of the models improves after adding the GS indicator and shows that GSMCC performs better than GSMER. (3) After using the error-in-variable model to estimate the parameters, the accuracy and precision of the model improved compared to the results obtained using the least squares method. The optimal model for predicting the total nitrogen content is [Formula: see text]. This study demonstrates the use of Internet of Things technology in forestry and provides guidance for the nutritional diagnosis of the important sandalwood tree species.
本文提出了一种利用数字图像处理预测檀香木材总氮含量的方法。本研究的目的是通过分析在森林中获取的图像，为生产者提供一个实时、高效、高度自动化的营养诊断系统。利用在海南省北部六个不同城市的林场安装的现场服务器获取的图像，我们提出了一种新的分割算法，并定义了一个新的指标，称为“生长状态”（GS），它包括两个品种：GSMER（檀香木像素与最小外接矩形像素的比值）和 GSMCC（檀香木像素与最小外接圆像素的比值）。我们通过考虑自变量中存在的误差，采用了带有误差的变量模型。经过比较和分析，得到的结果表明：（1）Lab 颜色系统中的 b 和 L 通道具有互补优势。通过将该系统与 Otsu 方法、中值滤波和形态学运算相结合，可以将檀香木从背景中分离出来。（2）加入 GS 指标后，模型的拟合度提高，表明 GSMCC 比 GSMER 表现更好。（3）在使用带有误差的变量模型估计参数后，与使用最小二乘法得到的结果相比，模型的准确性和精度都有所提高。预测总氮含量的最优模型为 [Formula: see text]。本研究展示了物联网技术在林业中的应用，为重要檀香树种的营养诊断提供了指导。
