当前位置: X-MOL 学术J. Near Infrared Spectrosc. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
The use of near infrared spectroscopy to predict foliar nutrient levels of hydroponically grown teak seedlings
Journal of Near Infrared Spectroscopy ( IF 1.8 ) Pub Date : 2021-07-08 , DOI: 10.1177/09670335211025649
William Andrew Whittier 1 , Gary R Hodge 1 , Juan Lopez 1 , Carole Saravitz 2 , Juan Jose Acosta 1
Affiliation  

Due to a combination of durability, strength, and aesthetically pleasing color, teak (Tectona grandis L.f.) is globally regarded as a premier timber species. High value, in combination with comprehensive harvesting restrictions from natural populations, has resulted in extensive teak plantation establishment throughout the tropics and subtropics. Plantations directly depend on the production of healthy seedlings. In order to assist growers in efficiently diagnosing teak seedling nutrient issues, a hydroponic nutrient study was conducted at North Carolina State University. The ability to accurately diagnose nutrient disorders prior to the onset of visual symptoms through the use of near infrared (NIR) technology will allow growers to potentially remedy seedling issues before irreversible damage is done. This research utilized two different near infrared (NIR) spectrometers to develop predictive foliar nutrient models for 13 nutrients and then compared the accuracy of the models between the devices. Destructive leaf sampling and laboratory grade NIR spectroscopy scanning was compared to nondestructive sampling coupled with a handheld NIR device used in a greenhouse. Using traditional wet lab foliar analysis results for calibration, nutrient prediction models for nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), sulfur (S), copper (Cu), molybdenum (Mo), magnesium (Mg), boron (B), calcium (Ca), manganese (Mn), iron (Fe), sodium (Na), and zinc (Z) were developed using both NIR devices. Models developed using both techniques were good for N, P, and K (R2 > 0.80), while the B model was adequate only with the destructive sampling procedure. Models for the remaining nutrients were not suitable. Although destructive sampling and desktop scanning procedure generally produced models with higher correlations they required work and time for sample preparation that might reduce the value of this NIR approach. The results suggest that both destructive and nondestructive sampling NIR calibrations can be useful to monitor macro nutrient status of teak plants grown in a nursery environment.



中文翻译:

使用近红外光谱预测水培柚木幼苗的叶面营养水平

由于耐用性、强度和美观的颜色相结合,柚木 ( Tectona grandisLf) 是全球公认的首要木材品种。高价值加上自然种群的综合采伐限制,导致在整个热带和亚热带地区建立了广泛的柚木种植园。种植园直接依赖于健康幼苗的生产。为了帮助种植者有效诊断柚木幼苗营养问题,北卡罗来纳州立大学进行了一项水培营养研究。通过使用近红外 (NIR) 技术在视觉症状出现之前准确诊断营养障碍的能力将使种植者能够在造成不可逆转的损害之前解决幼苗问题。该研究利用两种不同的近红外 (NIR) 光谱仪开发了 13 种营养素的预测叶面营养素模型,然后比较了设备之间模型的准确性。将破坏性叶子采样和实验室级 NIR 光谱扫描与非破坏性采样以及温室中使用的手持式 NIR 设备进行比较。使用传统湿实验室叶面分析结果进行校准,氮 (N)、磷 (P)、钾 (K)、钙 (Ca)、硫 (S)、铜 (Cu)、钼 (Mo)、镁的养分预测模型(Mg)、硼 (B)、钙 (Ca)、锰 (Mn)、铁 (Fe)、钠 (Na) 和锌 (Z) 是使用这两种 NIR 设备开发的。使用这两种技术开发的模型对 N、P 和 K(R 将破坏性叶子采样和实验室级 NIR 光谱扫描与非破坏性采样以及温室中使用的手持式 NIR 设备进行比较。使用传统湿实验室叶面分析结果进行校准,氮 (N)、磷 (P)、钾 (K)、钙 (Ca)、硫 (S)、铜 (Cu)、钼 (Mo)、镁的养分预测模型(Mg)、硼 (B)、钙 (Ca)、锰 (Mn)、铁 (Fe)、钠 (Na) 和锌 (Z) 是使用这两种 NIR 设备开发的。使用这两种技术开发的模型对 N、P 和 K(R 将破坏性叶子采样和实验室级 NIR 光谱扫描与非破坏性采样以及温室中使用的手持式 NIR 设备进行比较。使用传统湿实验室叶面分析结果进行校准,氮 (N)、磷 (P)、钾 (K)、钙 (Ca)、硫 (S)、铜 (Cu)、钼 (Mo)、镁的养分预测模型(Mg)、硼 (B)、钙 (Ca)、锰 (Mn)、铁 (Fe)、钠 (Na) 和锌 (Z) 是使用这两种 NIR 设备开发的。使用这两种技术开发的模型对 N、P 和 K(R 硫 (S)、铜 (Cu)、钼 (Mo)、镁 (Mg)、硼 (B)、钙 (Ca)、锰 (Mn)、铁 (Fe)、钠 (Na) 和锌 (Z)使用两种 NIR 设备开发。使用这两种技术开发的模型对 N、P 和 K(R 硫 (S)、铜 (Cu)、钼 (Mo)、镁 (Mg)、硼 (B)、钙 (Ca)、锰 (Mn)、铁 (Fe)、钠 (Na) 和锌 (Z)使用两种 NIR 设备开发。使用这两种技术开发的模型对 N、P 和 K(R2  > 0.80),而 B 模型仅适用于破坏性采样程序。其余营养素的模型不合适。尽管破坏性采样和桌面扫描程序通常会生成具有更高相关性的模型,但它们需要工作和时间来进行样品制备,这可能会降低这种 NIR 方法的价值。结果表明,破坏性和非破坏性采样 NIR 校准均可用于监测苗圃环境中生长的柚木植物的宏观营养状况。

更新日期:2021-07-09
down
wechat
bug