A review of the state of the art in agricultural automation. Part IV: Sensor-based nitrogen management technologies

Rodriguez et al. (2006) performed canopy reflectance measurement using a spectrometer (FieldSpec, ASD, 350-2500 nm) to evaluate a range of reflectance indices for the measurement of N stress, calculated from shoot N and shoot dry weight. The canopy chlorophyll content index (CCCi) and modified spectral ratio (mSRPi) indices were able to explain 68% and 69% of the variation in N stress, despite variation in water stress and canopy density. Chen et al. (2010) evaluated a number of vegetation indices calculated from hyperspectral reflectance measurements for the measurement of plant N concentration in wheat and corn. The results of the regression analysis (R2) were generally low for all indices in wheat (0.17-0.56) although measurements were diverse and included multiple growth stages. A new N index was later proposed, the Double-peaked Canopy Nitrogen Index (DCNI), which could explain 72% and 44% of the variation in plant N concentration for corn and wheat, respectively. In a later study (Chen, 2015) correlation of spectral indices with N concentration in wheat statistics improve on a growth stage basis with approximately 60% and 80% of the variation in N content explained by all indices at the Feekes 4 and Feekes 7-8 growth stages, respectively. Mechanistic models relating spectral indices and N nutrition index (NNI) validated well (R2: 0.82-0.94) and were independent of phenology.

Mistele and Schmidhalter (2008) used a spectrometer to measure wheat canopy reflectance and calculate the red edge inflection point (REIP). REIP and the N nutrition index NNI were highly correlated (R2 =0.95). The NIR/NIR indices (R760/R730) was reported to be the most reliable index for measurement of N status (Erdle et al., 2011). The response of the NDVI indices become saturated and was unable to distinguish between high N treatments. While active sensors provide greater versatility in operation, the wide spectral information that can be obtained from passive sensors may be needed for phenotyping certain traits (Erdle et al., 2011).

There are limitations in the use of reflectance sensor for measurement of plant N content. Reflectance sensors are ultimately a measure of biomass/chlorophyll content, and this can be slow to change in response to most environmental changes and is not solely related to stress (Tremblay et al., 2012). The chlorophyll content and biomass of the crop may only be indirectly related to the adequacy of N supply, and therefore empirical calibration of applied N to sensor response during crop growth is necessary for inference. Methods for predicting N requirements and calculating N response index using reflectance sensors are described by Whelan (2015). Calculating a response index is typically performed using reference strips where fertilizer is applied in non-limiting rates and sensor response in this area is used to inform application rates in the remainder of the field. Limitations for this approach are selecting a reference strip area that is representative of the field (e.g., Samborski et al., 2017) and that it is effective only when N is the main growth limiting factor (e.g., Zillmann et al., 2006). Further limitations of this sensor technique for N management include chlorophyll saturation where sensors are unable to distinguish between adequate and excess supply of N (Ruiz Diaz et al., 2008) and they are sensitive only when the fraction of vegetation cover is low (Maier and Günther, 2003). The available commercial sensors also differ in their use of active or passive techniques, wavelength ranges and the signal output can also differ with measurements based on different vegetation indices. Crop sensors need to be calibrated for different cultivars, sites and seasons (Craigie et al., 2013).

Transmittance (multispectral)

Measurement of chlorophyll can also be performed using transmission based (absorption) methods. The basic principle is as for the reflectance based methods described earlier. Commercial sensors are available, including the SPAD chlorophyll sensor (Minola Osaka Company, Ltd., Japan), which measures absorption using red (650-nm) and infrared (940-nm) LEDs in a leaf clip arrangement. SPAD measurements correlate well (R2 >0.70) with plant N concentration in both corn and wheat combined and over multiple years, and growth stages (Chen et al., 2010). Prost and Jeuffroy (2007) evaluated the use of SPAD measurements in the assessment of wheat N status as an alternative to the N nutrition index (NNI) and found that a significant relationship (R2 = 0.89) exists between SPAD measurements and NNI (at flowering), which was independent of cultivar. Debaeke et al. (2006) related normalized SPAD measurements to NNI and found them to be closely related irrespective of year, cultivar or growth stage.

Measurement of flavonoids in the leaf epidermis can also be used as an indicator of N status because plants produce these compounds under stress conditions (e.g., N stress) (Muñoz-Huerta et al., 2013). UV light is also absorbed by these compounds in the leaf epidermis and so flavonoid content can be estimated using absorption methods (e.g., Duelex® sensor, Force-A, Orsay, France) (Tremblay et al., 2009; Cerovic et al., 2012). The Duelex® sensor uses both fluorescence and light transmission through the leaf to calculate both chlorophyll content and epidermal UV-absorbance (Flv). This forms the basis of the N balance index (NBI) calculated as a ratio of the chlorophyll to flavonoid content. A limitation of transmission techniques is that they are contact-based measurements, and therefore not suitable for on-the-go or large area sensing. Similarly, automated measurements are also difficult to perform. The technique measures chlorophyll concentration as a proxy for N content and so limitations in this assumption will also apply (as for reflectance measurements).

Transmission methods can also be applied post-harvest for the measurement of grain protein and water content, and commercial sensors are available (e.g. the CropScan 3000H) for on harvester measurement and mapping. Combined with yield data, these measurements provide useful information on N use and may be used to inform variable rate N management (Long et al., 2005).

Chlorophyll fluorescence

The principles of chlorophyll fluorescence sensing are described in Tremblay et al. (2012). Of the incident solar radiation in the photosynthetically active region (PAR) (400-700 nm), 75% is absorbed by leaves with the majority of this energy dissipated thermally and a small fraction (3%) converted to organic matter. On absorbing energy at a given wavelength chlorophyll molecules also re-emit this energy at a longer wavelength through electron excitation (fluorescence). Measurement of chlorophyll fluorescence at these wavelengths is related to leaf chlorophyll content (Tremblay et al., 2012). Chlorophyll fluorescence can be measured in two ways: (1) by using the variable (dynamic) changes in fluorescence corresponding to changes in the photosynthetic reactions (Kuckenberg et al., 2009; Tremblay et al., 2012), and (2) using the ratio of fluorescence emissions at different wavelengths, which are more suitable for in-field application and measurements at the canopy level. These measurements can be passive, using sun-induced fluorescence or active; e.g., laser-induced fluorescence (Schächtl et al., 2005). Commercial sensors are available, including the Multiplex® (Force-A, Orsay, France) and the MiniVeg-N laser-induced fluorescence sensor (Fritzmeier, Germany). Chlorophyll fluorescence can also be used to measure polyphenolic compounds (e.g., flavonoids) compounds in the leaf epidermis. UV light is also absorbed by these compounds in the leaf epidermis and by comparing chlorophyll fluorescence induced by both UV and red light; the flavonoid content can be estimated (e.g., Quemada et al., 2014).

When used for sensing plant N status, an increase in fluorescence emission indicates a reduction in photosynthetic efficiency, which increases at the early stage of most stress conditions (Chaerle et al., 2007). As measurements are based on fluorescence signals (and not reflected), fluorescence sensing can provide highly sensitive information on plant N status that is independent of soil interference, leaf area or biomass status (Tremblay et al., 2012). Tremblay et al. (2012) suggest that fluorescence sensors could be used for non-invasive detection of stress long before reflectance based measurement of chlorophyll content or biomass, which are long-term effects of stress and also potentially a response to other environment changes. The sensing technique has seen limited application in N management, possibly due to the relative ease of reflectance-based measurements (e.g., for on-the-go). When using variable fluorescence measurements, dark adaptation is required, which is time consuming (10-20 minutes).

Reflectance (hyperspectral)

Spectroscopic analysis of the canopy using visible and near-infrared reflectance (vis-NIR) could provide greater information relating to plant status than indices derived using discrete wavelengths (Meng and Dennison, 2015). For plant analysis, several N absorption features exist below 2500-nm; however, water absorption bands in the NIR region limit use on plant samples that have not been processed (dried) and therefore field application (Chen et al., 2010). Several spectrometers are commercially available and have been used for plant analysis (e.g., FieldSpec, Analytical Spectral Devices (ASD), Boulder, Colorado). Spectrometers are generally expensive, with some exceptions such as the DLP® NIRscanTM sensors (Texas Instruments) covering the 900-2490 nm range. A potential advantage of spectrometers for canopy reflectance measurement is that full spectral information can be retained and multiple vegetation indices estimated simultaneously. Using chemometric approaches, it is also possible to simultaneously derive estimates of water status to separate N from water stress occurring simultaneously (e.g., Kusnierek and Korsaeth, 2015; Lamptey et al., 2017), and potentially the status of other nutrients that are relevant to N decision support (e.g., van Maarschalkerweerd and Husted, 2015).

Machine vision

Most optical sensing mechanisms can also be applied to machine vision (Hague et al., 2000). Image sensors are available that cover visible and near-infrared ranges (e.g., multispectral and hyperspectral cameras). These can be used to image the crop or plant at wavelengths surrounding the ‘red edge’ region, and thus capable of computing the vegetation indices of other reflectance-based optical sensors (e.g., GreenSeeker). Imaging has advantages over these sensors: (1) cameras deployed on remote platforms (UAV) can be used to capture canopy reflectance or thermal measurements with full field coverage at high spatial resolution, (2) ground-based machine vision sensors can better isolate soil influence on the signal by first segmenting leaves and plants within the image, and (3) the image resolution offered by cameras can also be used to resolve structure information at the plant and leaf level that could be utilized for N management of the crop. Several camera systems are commercially available at relatively low cost, and lightweight cameras have been released for UAV application. For example, the Survey2 by MAPIR (San Diego, California) and the RedEdge by MicaSense (Seattle, Washington) that can be installed with a range of filter options for crop vegetation imaging (e.g., NDVI).

Figure 1 (after Li et al., 2010) shows the use of a standard digital camera for measurement of crop N status. Camera measurement of the crop canopy cover correlated well with N status and also with NDVI of the GreenSeeker and Yara N-Sensor, respectively. However, correlation with N status was strong only during the vegetative and early stem elongation phases.

Figure 1. A digital camera image of wheat crop with segmentation of leaf area (after Li et al. 2010).

Thermal imaging can be used to measure the leaf surface temperature, which is influenced by the cooling effects of transpiration due to stomatal conductance (Irmak et al., 2008). This mechanism responds to a number of stress conditions both abiotic and biotic (Chaerle et al., 2007; Yeboah et al., 2017). Water or nutrient deficiency causes increase in leaf temperature by disrupting the transport of nutrient and water throughout the plant, causing the stomata to close to prevent further moisture loss (Mee et al., 2016).

Tilling et al. (2007) used ground-based spectral (vis-NIR) and airborne multispectral and thermal imagery to quantify N and water stress in wheat (Figure 2). The Canopy Chlorophyll Content Index (CCCI) using the ground-based spectrometer was well correlated with a Nitrogen Stress Index (NSI) derived from plant samples, but correlation was lower with airborne spectral measurements. A major limitation of remote sensing is the influence of the soil background when partially vegetated, but the thermal sensing of water stress could be useful for decisions on N application (Tilling et al., 2007).

Figure 2. Airborne images of Canopy Chlorophyll Content Index (CCCI) (left image) derived using a 3-band multispectral camera (670, 720, 790-nm) and thermal imagery (right image) (after Tilling et al., 2007).

The measurement of plant physical properties, or structure, can be used as an indicator of plant N status, yield prediction, and N demand. Measurement of leaf area is important for predicting crop growth and yield, and can be well correlated with canopy N content (e.g., Yin et al., 2003; Gebbers et al., 2011; Tavakoli et al., 2014). LAI can be detected remotely using UAV platforms. For N management, Hunt et al. (2010) used a standard digital camera (without infrared blocking filter) and modified with an interference filter to derive a Green Normalized Difference Vegetation Index (GNDVI). With images captured from the UAV platform correlation of GNDVI to LAI was R2 >0.80, but saturated above a LAI of 2.5 m2 m-2.

Sadeghi-Tehran et al. (2017) developed a computer vision framework for the automated monitoring of ear emergence and flowering in wheat using an RGB camera in-field conditions. The accuracy in detection of ear emergence was reported as >90%, depending on the size of training dataset, and accuracies ranged from 80% to 93% for detection of flowering time. Both hyperspectral and chlorophyll fluorescence sensing techniques can also be implemented in computer vision providing information on plant transpiration and photosynthesis relating to N, water and disease status.

Vigneau et al. (2011) evaluated the use of a hyperspectral camera (push broom CCD) with a spectral range from 400 nm to 1000 nm at 3.7 nm resolution. The camera was tractor-mounted on a gantry at a height of 1-m above the crop canopy, and the camera motion along the gantry was used to provide the second imaging dimension. An empirical calibration was used to estimate the leaf N concentration using a chemometric approach from hyperspectral data. Models developed using measurement of individual leaves in both greenhouse and field environment showed good predictability (R2 >0.85).

Machine vision sensors can be used for detection of biotic stress due to pathogens or insect damage. While this is not a direct measurement of plant N status, such information could be useful in decision support systems; for example, by validating the assumption of N stress as the primary factor affecting other optical measurements of plant chlorophyll or biomass (Bauriegel and Herppich, 2014). Biotic stress may also induce yield loss that is assumed to be N-related, machine vision sensors may therefore assist in identifying the cause of yield loss before using this data in N and crop modelling.

3D imaging

3D Imaging of crops using for example LIDAR, stereo cameras or time of flight (TOF), can provide plant physical or structural information. Application of these techniques to date is mostly in high throughput screening and plant phenotyping for breeding. However, in a field context this information could be used normalize measurements from other sensors, that is, account for biomass and growth stage, to support decisions in N management. Commercial sensors are available, as well as software for photogrammetry and point cloud generation. For plant phenotyping, Duan et al. (2016) used multiview camera imaging to reconstruct 3D images of wheat plants (point clouds) using the Multi-View Stereo and Structure From Motion (MVS-SFM) algorithm (Figure 3). From the 3D point cloud, Duan et al. (2016) were able to automatically determine several phenotyping traits, including: tiller and leaf number, plant height and leaf properties useful for N management and yield prediction.

Figure 3. Measuring plant structure using multiview camera imaging (after Duan et. al., 2016).

Optical sensing of nitrogen stress: confounding factors and sensor fusion

Spectral features assist the detection of plant nutritional-related stress when only one factor is causing such stress (Mee et al., 2017). This makes it challenging when trying to discriminate sources of stress influencing plant physiological performance at the same time. This is the case when sources of stress can have similar impacts on the plant’s physiological response (e.g., N and water) consequently leading to the observation of similar spectral responses (Thomason et al., 2007). Peteinatos et al. (2015) evaluated the use of multiple optical sensors, including reflectance (spectrometer), and calculated multiple indices: the CLAAS Isaria® crop sensor, and the Multiplex® to identify stress due to water and N deficiency, weed and fungal infection in wheat. In their study, it was hypothesized that these sensors could be used to detect individual stressors in the presence of others. It was found that some indices, such as the FRFUV provided by the Multiplex® sensor, responded only to N stress whereas indices such as FERRG and ANTHRG responded only to fungal infection. Other indices from the Multiplex® and HandySpec® sensor respond to multiple stress factors. Variability in sensor response due to plant growth stage was more significant than variability due to stress factors (Peteinatos et al., 2015). Therefore, growth stage needs to be considered to define a stress threshold.

Discussion

Analysis of plant tissue to determine nitrogen (N) status has some advantages over soil testing in that the complexities of other soil, plant and environmental properties influencing plant uptake of N are effectively accounted for by measuring the plant directly (e.g., Antille et al., 2015, 2017). For macronutrients, good correlation is usually found between plant concentrations and nutrient status as measured by yield (e.g., Antille et al., 2014; van Maarschalkerweerd and Husted, 2015). Optical sensors typically measure compounds (such as chlorophyll) that relate well to N status. However, this relationship can break-down when soil N availability is high, and as several other factors affect chlorophyll synthesis, it could be difficult to make environmentally-sound decisions on N application based on optical sensors (Tremblay et al., 2009; Galambošová et al., 2014).

Considering the range of sensing techniques available, reflectance-based measurements are easy to perform, show reasonably good correlation with plant N status, can be performed remotely providing high resolution, and several commercial sensors are available. Consequently, site-specific management of N has traditionally focused on reflectance-based technologies. However, a limitation of these technologies is the inconsistency in the relationship with N status based on crop growth stage, biomass and other growth factors, therefore requiring calibration on a site and seasonal basis. There are also different vegetation indices reported in the literature, and there is little consistency in the reported performance of such indices. Alternative sensing techniques, such as transmission-based measurements appear to be more reliable for estimating N status (e.g., SPAD measurements). However, it may not be possible to apply this technique at the spatial resolution required for N management (e.g., 1-m). Transmission-based measurements applied post-harvest (e.g., on-harvester protein-sensing) could also provide valuable information for N modelling, and in estimating N removal from soil.

For the measurement of N status, fluorescence-based techniques have some advantages over reflectance (Živčák et al., 2014a-b), such as no influence of soil background and measurement of polyphenols. This latter enables the calculation of the N buffer index (NBI). Therefore, these may offer improvements in measurement of N status, although the additional measurement and sensor requirements (close range) may not warrant the use of such sensors alone. It had been reported that an on-the-go version of the Multiplex® sensor (A-Force, France) was under development, but this version has since been discontinued. However, factors limiting deployment of sensor technologies, such as active fluorescence sensing or transmission methods, may be addressed within a future autonomous framework; for example, with such sensor deployed using UGV to provide calibration/ground-based referencing of remote sensor measurement.

Using machine vision sensing of reflectance or fluorescence has potential to address some of the limitations in other techniques, such as segmenting of plants prior to calculation of indices and removing soil background effects, although sensor and computation requirements are greater. Deployed on airborne platforms (e.g., UAV), image sensors can provide reflectance measurements at high spatial and temporal resolution. When deployed on a ground vehicle, the camera can measure plant physical properties such as leaf area and height, potentially enabling independent estimates of biomass and chlorophyll content. At the leaf level, machine vision may also allow detecting biotic stresses. While not directly applicable to N decision support, these measurements could be used in diagnosing yield reduction, improving the quality of data applied to modelling and machine learning.

It is also difficult to directly compare sensing techniques for the measurement of crop N status, with the accuracy reported by studies undertaken in different conditions. However, a general classification of sensor performance for N stress measurement was presented in Table 1, which could be used to assist sensor selection.

Conclusions and recommendations

Optical sensor measurements of nitrogen (N) status are indirect, sensors respond to plant stresses that could be induced by a number of biotic and abiotic factors. Sensor measurements are also often dependent on other dominant factors, such as growth stage. A multi-sensor approach may provide a more accurate or reliable indication of N status, and better isolate those factors.

Optical sensing should also be applied post-harvest (e.g., for protein monitoring), and this could provide information on N uptake during crop growth, and quantifying N removed by the crop. There is a requirement for engagement with the tractor / harvesting manufacturers (e.g., John Deere, CNH, CLAAS) to further develop these applications.

Acknowledgements

The research, which informs this article, was made possible by the significant contribution of Australian grain growers through the support of the Grains Research and Development Corporation. The authors would like to thank them for their continued support. This paper is an extract from the final report for Project USQ00022 ‘Future FARM’ commissioned to the authors by GRDC.

Disclaimer

Reference to commercial names or products in this work is solely for the purpose of providing accurate information and does not represent endorsement or otherwise by the authors or their organisations. The graphical information presented in this review is fully credited to the original sources.

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Contact details

Craig Baillie
Centre for Agricultural Engineering (CAE)
Institute for Advanced Engineering & Space Sciences (IAESS)
Ph: 07 4631 2071
Mb: 0428 750 060
Email: craig.baillie@usq.edu.au

Diogenes L. Antille
Ph: +61 (07) 4631 2948
Email: Dio.Antille@usq.edu.au

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