Abstract
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.
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This work was supported by the National Modern Agriculture Industry Technology System Construction Project (CARS-170702). National Natural Science Foundation of China (31560084).
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He, J., Liang, X., Qi, B. et al. Diagnosis of Nitrogen Nutrition in Sugar Beet Based on the Characteristics of Scanned Leaf Images. Int. J. Plant Prod. 14, 663–677 (2020). https://doi.org/10.1007/s42106-020-00109-1
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DOI: https://doi.org/10.1007/s42106-020-00109-1