Plant stress adversely affects container seedling quality, productivity, and thus nursery profit margins. Early detection of seedling stress is of interest to nursery managers because it facilitates corrective changes to cultural practices before stress is well established and thus reduces the risk of financial loss [1]. Many nurseries visually monitor seedlings for stress symptoms during daily operations and periodic inventories, and some submit randomly selected tissue samples for nutrient analysis [2]. A possible alternative to visual plant health monitoring and tissue tests is the use of optical sensors that rely on light to assess the physiological status of plants either at the leaf or canopy level. Canopy, when compared to leaf level optical sensing, is more affected by confounding factors such as variation in biomass or soil background. However, optical sensors that operate at the canopy level have a considerably higher sampling efficiency.

Passive optical sensors traditionally used for optical plant analysis require reference measurements to standardize spectral readings to ambient light conditions. Since light conditions tend to change frequently, passive optical sensors constantly require time-consuming reference measurements, limiting their operational use. In addition, they are costly and may require special training [3]. A more cost-effective and user friendly alternative might be the use of rugged active ground optical remote sensing (AGORS) devices that are used in agriculture for variable-rate nitrogen management [4].

Light emitting diodes in the AGORS devices actively emit modulated light and integrated photodetectors measure only reflected light that has the sensor specific modulation frequency. Sunlight is not measured by the photodetectors since it is not modulated. These technical properties of the AGORS devices allow them to take up to 100 spectral measurements per second, independent of ambient light conditions and without the need for time consuming spectral reference readings. Hence, AGORS devices could autonomously measure canopy reflectance from irrigation booms on-the-go, independent of light conditions (Figure 1). To objectively evaluate the physiological status of plants, AGORS relies on spectral indices that combine canopy reflectance values measured at different wavelengths. Wavelengths in the visible domain (400–700 nm) have proven to be most suitable to spectrally detect plant stress that is due to disease, nutrition, pests, and anthropogenic pollution [5,6]. This has been explained by the fact that most plant stress leads to the breakdown of chlorophyll, which dominates the properties of foliar reflectance throughout the visible domain [5,7–9].

A wide variety of spectral indices have been successfully used to spectrally detect variations of chlorophyll at the leaf level [10]. However, isolating the spectral chlorophyll signal at the canopy level has proven to be more challenging due to a complex interaction of spectral signals that compose the canopy spectral response [4,11]. Traditionally, AGORS relied solely on the red (590–670 nm) and near-infrared (>760 nm) wavebands to measure the Normalized Difference Vegetation Index (NDVI) [12].The latter index is reported to be highly sensitive to variations in biomass or Leaf Area Index (LAI), yet insensitive to chlorophyll a+b concentration (Chl_ab) [13]. Nevertheless, NDVI has been shown to be correlated with Chl_ab because Chl_ab often co-varies with biomass [14].

Recently, an AGORS device (ACS-470, Holland Scientific, Inc., Lincoln, NE, USA) has become commercially available that provides additional red-edge (700–740 nm) reflectance information. Recent research with passive optical sensors suggests that red-edge reflectance information provides important spectral information to remotely predict Chl_ab [11,13,15]. However, relatively little is known about whether the additional availability of red-edge waveband reflectance information improves AGORS of seedling stress. Our objective in this study was to test the hypothesis that the additional availability of red-edge waveband reflectance information would improve AGORS of seedling stress by means of nursery-grown Scots pine (Pinus sylvestris) seedlings.

Slight differences in greenness amongst seedlings of different fertilizer treatments were visible. Across all treatments, the average Chl_ab was 0.76 ± 0.32 mg g⁻¹ (mean ± standard deviation), the average seedling height was 93.3 ± 46.2 mm, and the average fresh biomass weight was 8.63 ± 4.75 g. The red-edge reflectance employing CCCI and NDRE showed a stronger linear correlation (r² = 0.78 and 0.77, respectively) with Chl_ab than non red-edge reflectance employing NDVI (r² = 0.63; Figure 2). It is important to note that though a non-linear regression model between Chl_ab and NDVI would have resulted in a slightly stronger relationship, the higher r² would have suppressed the low sensitivity of NDVI to high Chl_ab (>0.6 mg g⁻¹).

Some of the scatter across the regression lines might be explained by variability in background reflectance [4]. In cases where variability in background reflectance is high it might become more advantageous to use combined instead of single indices; Eitel et al. [5] showed for example that combined indices were less affected by variations in soil reflectance than single indices.

Since early stress detection requires spectral indices to be sensitive to variations in high Chl_ab [10], we reanalyzed the data after removing low Chl_ab values (<0.6 mg g⁻¹). Not surprisingly, the strength of the spectral index to Chl_ab relationships decreased upon removal of low Chl_ab values. However, it is important to note that the strength of the spectral index to Chl_ab relationship decreased less for red-edge reflectance employing CCCI (r² = 0.53) and NDRE (r² = 0.54) when compared to NDVI (r² = 0.34). This finding suggests that the red-edge reflectance employing CCCI and NDRE are more suitable to detect early stages of stress induced chlorophyll breakdown than the non red-edge reflectance employing NDVI.

The results of the cross-validation further suggested the superior performance of the red-edge reflectance employing indices: regression models employing CCCI and NDRE provided more precise Chl_ab predictions (r² ≥ 0.73 and RMSE = 0.17) than did the regression model employing NDVI (r² = 0.57 and RMSE = 0.21) (Figure 3). Regression models employing CCCI and NDRE also provided more accurate Chl_ab predictions (intercept = 0.04/slope = 0.95 and intercept = 0.04/ slope = 0.96) than the regression model employing NDVI (intercept = 0.06/slope 0.93).

The results presented here suggest that availability of red-edge waveband reflectance information improves AGORS of plant stress, particularly at early stages, and thus support our hypothesis. To operationally use AGORS devices in a nursery setting, we envision that AGORS devices linked with either a combined rotary encoder and measuring wheel [18], or optoelectronic non-contact linear encoder positioning device, could be mounted on automated overhead irrigation boom systems to acquire spatially referenced spectral indices on-the-go (Figure 1). The spatially referenced spectral indices could then be used to locate seedlings with low Chl_ab that need attention. Furthermore, spatial patterns in the Chl_ab of seedlings, due to non-uniform irrigation spray nozzle performance, local variation in temperature or air circulation within the greenhouse, and other growth factors, could also be identified and mapped. However, it is important to note that although spectral indices can help to detect and monitor seedling quality, they do not provide information about the specific causes of plant stress.

Since the width of the sensor's field of view will likely not cover the entire width of a growing table with one boom pass, several passes might be necessary, with the sensor’s location along the boom advanced after each pass. Further studies with other plant species are necessary to verify our results and evaluate the operational use of AGORS in nursery settings.