Abstract

Visible-Near Infrared (vis-NIR) spectroscopy can provide a faster, cost-effective, and user-friendly solution to monitor leaf N status, potentially overcoming the limitations of current techniques. The objectives of the study were to develop and validate partial least square regression (PLSR) to estimate the total N contents of fresh and removed leaves of potatoes using the vis-NIR spectral range (350–2500 nm) generated from a handheld proximal sensor. The model was built using data collected from Hancock Agricultural Research Station, WI, USA in 2020 and was validated using samples collected in 2021 for four different conditions. The conditions included two sites (Coloma and Hancock), four potato varieties (Burbank, Norkotah, Goldrush, and Silverton), two N rates (unfertilized and 308 kg N ha⁻¹), and four growth stages (vegetative, tuber initiation, tuber bulking, and tuber maturation). The calibration and validation models had high predictive performance for leaf total N with R² > 0.8 and RPD > 2. The model accuracy was affected by the total N contents in the leaf samples where the model underpredicted the samples with total leaf N contents greater than 6%.

中文翻译：

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可见光和近红外光谱预测不同生长和管理条件下马铃薯品种叶片氮含量

摘要

可见-近红外 (vis-NIR) 光谱可以提供更快、经济高效且用户友好的解决方案来监测叶片氮状态，从而有可能克服当前技术的局限性。该研究的目的是开发和验证偏最小二乘回归 (PLSR)，以使用手持式近端传感器生成的可见近红外光谱范围 (350–2500 nm) 来估计新鲜和去除的马铃薯叶子的总氮含量。该模型是使用 2020 年从美国威斯康星州汉考克农业研究站收集的数据构建的，并使用 2021 年在四种不同条件下收集的样本进行了验证。条件包括两个地点（Coloma 和 Hancock）、四个马铃薯品种（Burbank、Norkotah、Goldrush 和 Silverton）、两个施氮量（未施肥和 308 kg N ha ⁻¹）和四个生长阶段（营养、块茎萌生、块茎）膨大和块茎成熟）。校准和验证模型对叶片总氮具有较高的预测性能，R ²  > 0.8 且 RPD > 2。模型精度受叶片样品中总氮含量的影响，其中模型对叶片总氮含量大于的样品进行了低估。 6%。