利用智能手机相机快速识别盐渍土中水稻常量养分含量

A. N. Putra, Albert Fernando Sitorus, Quid Luqmanul Hakim, Martiana Adelyanti, Istika Nita, Sudarto
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引用次数: 3

摘要

由于一些因素,印度尼西亚的大米产量在过去五年中(2015 - 2019年)平均下降了6.83%。除气候变化(21%)、干旱(9%)和其他因素(28%)外,盐度(42%)是导致水稻减产的主要因素之一。智能手机摄像头作为一种替代技术,可以防止因盐分而导致的大量营养素缺乏。本研究使用可见光(R, G, B)的android航拍照片,图像从5 m的高度拍摄。采用自由网格法对稻田和盐碱地进行了植物生物量宏量营养素含量的观测。通过对生物量常量养分与提取的航拍照片叠置后的数字数进行回归分析和配对t检验得到公式。结果表明,利用智能手机上的数字(DN)预测水稻氮、磷、钾含量的公式为:N = 0.0035 * DN + 0.8192 (R2 0.84)、P = 0.0049 * DN - 0.2042 (R2 0.70)、K = 0.0478 * DN - 2.6717 (R2 0.70)。公式计算的常量营养素估算结果与田间原始数据没有差异。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Rapid Identification of Rice Macronutrient Content in Saline Soils Using Smartphone Camera
Abstract Indonesia’s rice production has decreased by 6.83% (on average) in the last five years (2015 – 2019) because of some factors. Salinity (42%) is one of the leading factors that cause decreasing rice production besides climate change (21%), drought (9%), and other factors (28%). The smartphone camera serves as an alternative technology to prevent macronutrient deficiencies due to salinity. This study used aerial photos from android with visible light (R, G, and B), and the image was taken from a height of 5 m. The observation of macronutrient content in plant biomass was carried out using a free grid to adjust rice fields and saline soil. The formula was obtained from regression analysis and paired t-test between the biomass macronutrient and the extracted digital number of aerial photographs that have been stacked. The results showed that digital number (DN) from a smartphone was reliable to predict nitrogen (N), phosphorus (P), and potassium (K) content in rice with formula N = 0.0035 * DN + 0.8192 (R2 0.84), P = 0.0049 * DN – 0.2042 (R2 0.70), and K = 0.0478 * DN – 2.6717 (R2 0.70). There was no difference between the macronutrient estimation results from the formula and the field’s original data.
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