Sugar beet is an important economic crop in Northwest China. In this area, efficient use of nitrogen (N) fertilizer has become crucial due to decreased profits associated with both under- and oversupply relative to sugar beet requirements. Thus, fast and non-destruction diagnostic tools for estimating plant N status have an important role in reducing N inputs while maintaining sugar beet yield and qualify. The objective of our study was to quantify leaf color characterization of sugar beet with an inexpensive scanner and establish the relationship with yield, leaf nitrogen content (LNC), plant total nitrogen content (PTNC), chlorophyll content (CC), soil nitrate nitrogen content (SNNC) and soil plant analysis development (SPAD) readings in sugar beet. In 2017 and 2018, field experiments were conducted with five N treatments ranging from 0 to 180 kg N ha −1 . The main results showed the following: The SPAD readings (SPR) and CC exhibited a significant or highly significant correlation (maximum = 0.70, P < 0.01), both of which reflected well the N nutrient status of the entire plant. Furtherly, a detailed association analysis revealed that there was a close relationship (maximum = − 0.63, P < 0.01) of LNC, SPR, PTNC, CC and yield with leaf color parameter Red/Blue (R/B), which was recommended as leaf color parameters for N diagnosis in sugar beet. In addition, based on the distribution of R/B value under different N rate, the yield was low with greater R/B value than 1.36 indicating an insufficient N supply, and with the R/B value was lower than 1.36, the theoretical yield reached its peak indicating an adequate supply of N fertilizer. To summarize, compared to the complicated and expensive of hyperspectral and other remote sensing technologies, scanned leaf image (SLI) processing technique was a simple, inexpensive and reliable method of determining sugar beet N status that has potential as a diagnostic tool for determining crop N requirement.

中文翻译：

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基于叶片扫描图像特征的甜菜氮营养诊断

甜菜是西北地区重要的经济作物。在该领域，由于与甜菜需求相关的供过于求导致利润下降，因此有效使用氮 (N) 肥料变得至关重要。因此，用于估计植物 N 状态的快速和非破坏性诊断工具在减少 N 输入同时保持甜菜产量和合格方面具有重要作用。我们研究的目的是用廉价的扫描仪量化甜菜的叶片颜色特征，并建立与产量、叶片氮含量 (LNC)、植物总氮含量 (PTNC)、叶绿素含量 (CC)、土壤硝态氮含量的关系(SNNC) 和甜菜中的土壤植物分析发展 (SPAD) 读数。在 2017 年和 2018 年，使用范围从 0 到 180 kg N ha -1 的五个 N 处理进行了田间试验。主要结果表明：SPAD读数（SPR）与CC呈显着或极显着相关（最大值=0.70，P<0.01），均能较好地反映整株氮素营养状况。此外，详细的关联分析表明，LNC、SPR、PTNC、CC 和产量与叶色参数 Red/Blue (R/B) 之间存在密切关系（最大值 = − 0.63，P < 0.01），推荐为用于甜菜氮诊断的叶色参数。另外，从不同施氮量下R/B值的分布来看，产量偏低，R/B值大于1.36说明供氮不足，R/B值小于1.36，理论产量达到峰值，说明氮肥供应充足。总而言之，与高光谱和其他遥感技术的复杂和昂贵相比，扫描叶片图像 (SLI) 处理技术是一种简单、廉价且可靠的确定甜菜 N 状态的方法，具有作为确定作物 N 的诊断工具的潜力要求。