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Determination of soil nutrients (NPK) using optical methods: a mini review
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Determination of soil nutrients (NPK) using optical
methods: a mini review
Revati P. Potdar , Mandar M. Shirolkar , Alok J. Verma , Pravin S. More & Atul
Kulkarni
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JOURNAL OF PLANT NUTRITION
https://doi.org/10.1080/01904167.2021.1884702
Determination of soil nutrients (NPK) using optical methods:
a mini review
Revati P. Potdara, Mandar M. Shirolkarb, Alok J. Vermac, Pravin S. Morea, and
Atul Kulkarnib
a
Nano Material Application Laboratory, Department of Physics, The Institute of Science, Dr. Homi Bhabha
State University, Mumbai, India; bSymbiosis Centre for Nanoscience and Nanotechnology, Symbiosis
International (Deemed University), Pune, India; cSociety for Applied Microwave Electronics Engineering and
Research (SAMEER), IIT Campus, Powai, Mumbai, India
ABSTRACT
ARTICLE HISTORY
In the present situation, plants have to meet the food supply demand for
a large and increasing population. In order to get high yield, it is essential
for the plantations to be nourished with soil containing an appropriate
amount of nutrients like Nitrogen (N), Phosphorus (P) and Potassium (K).
Various methods such as, physical (optical) and chemical (electrochemistry)
have been adopted to analyze the soil nutrients. This paper reviewed
optical methods of soil nutrient detection suitable for building a portable
sensor because it can sense nutrients in dry soil samples directly without
the need for complicated sample pretreatments. We concentrate and elaborate on optical methods of experimentation. Starting from laboratory testing standards followed in India we move on to off the lab crude methods
like soil testing kits and colorimetric approaches. Further, we review the
effective and utilitarian spectroscopic approaches and also the technologically advanced and latest methods like imaging systems, microfluidic, and
micro-electromechanical system (MEMs) based sensors. However, optical
methods are affected by environmental factors that affect the accuracy of
sensor results. This paper then discusses boons and curses of optical methods of soil nutrient sensing. It also explains briefly the working of each
method and mentions the most recent advancements made in the given
testing method. We hope that this paper can serve as a guide for the
experimenters and give a direction for carrying out further work required
in developing a portable and efficient soil NPK detection sensor.
Received 10 August 2020
Accepted 29 September 2020
KEYWORDS
Colorimetry; IR
spectroscopy; optical
sensing; portable sensor;
soil nutrients
Introduction
Soil contains 16 essential elements like carbon, hydrogen, oxygen, nitrogen, calcium, etc. for the
proper completion of the plants life cycle. Out of these, three macronutrients Nitrogen (N),
Phosphorus (P) and Potassium (K), (NPK), are especially important because they play a very significant role in the development of plants and are required in large quantities (Basu 2011).
Nitrogen is a major component of chlorophyll and amino acids and is an important factor in
plant growth. Phosphorus is an important constituent of plant deoxyribonucleic acid (DNA) and
ribonucleic acid (RNA), it plays an important role in the development of roots and production of
seeds. Whereas, Potassium plays an indirect role in the plant development like activating over 80
enzymes throughout the plant. Thus, these elements are very important and must be present at
CONTACT Pravin S. More
atul.kulkarni@scnn.edu.in
ß 2021 Taylor & Francis Group, LLC
pravin.more@gov.in; pravin.more@iscm.ac.in; Atul Kulkarni
atul.kulkarni@scnn.edu.in;
2
R. P. POTDAR ET AL.
optimum level in the soil for proper plant growth, and if required, their quantity must be replenished by the application of NPK fertilizers.
Insufficient use of fertilizers results in poor plant yield, whereas the excessive use of fertilizers
makes the soil contaminated. But to maximize crop production and due to lack of infrastructure,
farmers in India are seen to supply fertilizers to the soil blindly. It is not feasible for the farmers
to test their soil nutrient concentration at regular intervals due to economic problems and due to
the nonavailability of the soil testing laboratory in close vicinity of the field. Also, it takes months
for the result of the soil tests to become available to the farmers. Thus, there is a need for rapid
and on-the-go measurement of soil nutrients. A low-cost soil testing probe will help the farming
community to check the nutrient status on the field itself and encourage the judicious use of fertilizers to enhance the yield and hence increase the profitability.
Researchers worldwide are trying to solve the problem of building an in situ sensor and are
constantly increasing the knowledge on measurement of soil macronutrients (NPK). Currently,
techniques like optical sensing, laboratory measurement, ion-selective electrodes, chemical methods, Inductively Coupled Plasma (ICP) spectroscopy, and fluorescence spectroscopy are widely
known. But most of these techniques are either costly, involve complicated setups, or are unsuitable for in situ measurement. The most frequently used methods are:
Electrochemical sensing which makes use of ion-selective electrodes that generate a current or
voltage in response to the activity of selected ions.
Optical sensing which uses either spectroscopic or colorimetric technique.
According to the literature review, optical sensing seems to be a promising candidate for
building a low-cost, portable soil nutrient sensor because optical sensors are portable, easy to use,
and are small in size. They have attractive characteristics like low cost, light-weight, and high
flexibility (Tagad et al. 2013). The optical sensing techniques are being studied by researchers
worldwide because they have several advantages over electrical methods, such as high sensitivity,
selectivity, repeatability, and immunity from electromagnetic interference. This sensing technique
records and analyses the reflectance, absorption, or transmittance spectrum of materials to identify them in a nondestructive manner. In agriculture, for sensing purpose, the amount of energy
reflected from the soil surface is usually measured (Stenberg et al. 2010).
In the light of soil macronutrient sensing, this paper reviews standard protocol followed for
laboratory soil testing in Section 2; setting up the rationale behind portable soil testing sensor.
The paper then moves onto reviewing the optical techniques like spectroscopic, colorimetric technique and imaging technique in detail in Section 3. Followed by conclusion in Section 4.
Soil NPK estimation using standard protocol
An elaborate description of the standard laboratory testing procedure followed in India is given
in the section below. Table 1 provides soil fertility level classification of NPK depending on their
kg/ha values and is required for interpreting the test results.
Table 1. Soil fertility levels of nutrients N, P and K (Government Documentation Manual 2009) (Shah and Pawar 2009).
Soil fertility levels
Very low
Low
Medium
Moderately high
High
Very high
Nitrogen (kg/ha)
Less than 140
141–280
281–420
421–560
561–700
Greater than 700
Phosphorus (kg/ha)
Less than 7
7.1–14
14.1–21
21.1–28
28.1–35
More than 35
Potassium (kg/ha)
Less than 100
101–150
151–200
201–250
251–300
More than 300
JOURNAL OF PLANT NUTRITION
3
Determination of available phosphorus from soil (Olsen method) (Pansu and
Gautheyrou 2006)
Olsen’s reagent is made up of sodium bicarbonate and is used for extracting phosphorus
from soils with pH > 6.5. It also works well with calcareous soils. It separates Ca-phosphates, Al-phosphates and Fe-phosphates in the soil by precipitating Ca as CaCO3. Thus, extract
obtained by adding Olsen’s reagent to soil and filtering the content contains our required phosphorus
which is further treated with ammonium molybdate to obtain blue colored solution of phosphor–
molybdate complex. The intensity of the blue color provides a measure for the concentration of P, in
the test solution.
Extraction and estimation
1.
2.
3.
4.
5.
6.
Add two spoons of Darco-G-60 followed by 50 ml of 0.5 NaHCO3 solution to 2.5 g soil
Cork the flask and shake for 30 minutes then filter the contents to collect the filtrate.
Pipette out 5 ml of the NaHCO3 exact into 25 ml volumetric flask.
Add two drops of 2, 4-paranitrophenol and 5 N H2SO4 drop by drop with intermittent shaking till yellow color disappears.
Dilute the contents to about 20 ml with distilled water and then add 4 ml solution containing
ammonium molybdate, antimony potassium tartarate and ascorbic acid.
Make up the volume, shake it and measure the intensity of blue color at 660 nm on
Spectronic 20 or using red filter on colorimeter.
Calculations
Available P kg=ha ¼ R Total volume of extract
1
2:24 106 106
Volume of aliquot taken weight of soil
where R is the concentration in parts per million (ppm) from standard curve.
Determination of available potassium in soil (ammonium acetate extractable) by flame
photometer (Pansu and Gautheyrou 2006)
Available potassium in soil is extracted using ammonium acetate solution. Ammonium acetate
when mixed in soil reacts with potassium compounds in the soil to form potash. The potassium
from potash is then detected using flame photometry.
Extraction and estimation
1.
Add 25 ml extracting solution to 5 g soil and shake it for 5 min. Filter the contents and collect the filtrate.
2. Atomize the above extract on flame photometer and record the readings.
Calculations:
Available K ¼ R Volume of extracted soil solution
2:24 106 106
Soil weight
where R is the concentration in ppm from the standard curve.
4
R. P. POTDAR ET AL.
Soil available nitrogen detection by alkaline permanganate method (Hussain and
Malik 1985)
Ammonia is removed from soil by oxidization process. For the oxidation reaction to take place,
potassium permanganate (KMnO4) is mixed with sodium hydroxide (NaOH). Removed ammonia
is collected in boric acid to form ammonium borate. For quantitative identification of nitrogen,
ammonium borate is titrated with sulfuric acid (H2SO4). The volume of acid required for titration
is substituted in the formula to get result.
Extraction and estimation
1.
2.
3.
4.
Put 20 ml distilled water and 20 g soil in a 1000 ml distillation flask.
Add 100 ml of potassium permanganate (0.32%) and 100 ml of sodium hydroxide solution
(2.5%) to the flask.
Stopper the flask immediately and start distillation. The tip of the condenser should dip in
the 20 ml of boric acid solution in the beaker. On heating, ammonia will be liberated which
will be absorbed in the boric acid. The original wine red/pink red color turns to green with
the absorption of ammonia.
Collect nearly 100 l of the distillate in about 30 min and add to 1 l of 0.02 N H2SO4 to get
the original pink red wine color and record the burette reading.
Observations and calculations
H2SO4 required for titrating sample¼X ml
H2SO4 required for blank sample¼Y ml
Available nitrogen Kg=ha ¼ ðX–YÞ Normality of H2 SO4 0:014 2:24 106
1
Weight of the soil
As can be seen above, the laboratory testing depends heavily on chemical extraction, with the
selection of extractant (a chemical solution) based on soil properties like pH and soil chemistry.
Following the chemical extraction, the concentration of nutrients is measured using a setup procedure. Lastly, the concentration of the extracted nutrients is converted into the standard unit of
kg/ha so that it can be correlated with soil fertilizer needs. Thus, laboratory testing is an ordeal
which is not so used by farmers.
Most of the small-scale farmers in India do not usually check the quality and health of their
soils. Laboratory testing services are scarce and irregular, also the test results take very long
(many months) to become available.
For the reasons stated above, it is better to have an alternate, reliable soil testing approach
with added benefits like portability so that it can be used for in situ soil testing. This approach
can be used to supplement the laboratory chemistry analysis. Hence, the paper henceforth focuses
on rapid testing methods that have the advantage of portability, ease of testing and quicker results
as compared to conventional laboratory testing.
Soil NPK estimation by optical measurements
The optical soil measurement methods in this paper can be classified into six types.
Visible–infrared (Vis–IR) spectroscopy, inductively coupled plasma spectroscopy, fluorescence
spectroscopy; and colorimeters are used for measuring soil nutrients. In IR spectroscopy, infrared
radiation interacts with soil matter, and the transmittance/absorption of IR radiation coming out
JOURNAL OF PLANT NUTRITION
5
from the sample is analyzed to get nutrient values. Reflectance spectroscopy detects the level of
energy reflected by soil particles and relates it to the concentration of soil elements. The colorimetric technique correlates the color of the soil extract to reference charts and measures the level
of soil nutrients. In fluorescence spectroscopy, Ultraviolet (UV)/visible radiation absorbed by the
soil sample results in fluorescence emission, which is recorded to measure nutrient values.
Chappelle et al. (1984) explored the use of laser-induced fluorescence spectroscopy for nutrient
deficiencies of plants. Plants with Phosphorus and Nitrogen deficiencies had a decrease in the
intensity in their fluorescence spectrum at 690 and 740 nm. In Inductively coupled plasma optical
emission spectroscopy (ICP-OES), electromagnetic radiation at wavelengths characteristic of a
particular element is emitted. Yang et al. (2018) used ICP-OES for determining Phosphorus
amount in the soil. The subsequent sections provide an exhaustive review of the most frequently
used and widely researched optical techniques.
Vis–IR spectroscopy
Vis–IR spectroscopy is a physical nondestructive, rapid, reproducible and low-cost technique that
characterizes materials according to the energy absorbed by the material in the wavelength range
700 nm 1 nm. Each soil nutrient has its own and unique spectral feature which helps in its
quantification and identification. It is a type of nonliquid nutrient testing method which makes it
a cutting-edge technique for soil analysis. The procedure is very convenient and truly portable in
the sense that, it requires almost no sample preparation. To extract quantitative information out
of the IR spectra it is required to use calibration curve obtained from multivariate techniques.
The calibration curve is used to find unknown concentration of a solution by using the graph of
intensity of spectrum versus known concentration. Thus, over the other methods, spectroscopic
methods for soil analysis are advantageous and easy. The only limitation of the method is the soil
mapping and generation of appropriate database. The application of IR spectroscopy to soil is
being studied from 1960 and is used extensively for determining soil Carbon and Nitrogen content. Vis–IR spectroscopy is now being studied for its use in determining soil Phosphorus and
Potassium content. Table 2 enlists prominent and potential literature on NPK detection based on
Vis–IR spectroscopy.
Masrie et al. (2018) presented a device in Institute of Electrical and Electronics Engineers
(IEEE) 2018 conference. It was an Arduino UNO and light emitting diode (LED) based portable
device for measurement of soil NPK. The LEDs used had wavelengths of 470 nm (for N), 660 nm
(for K) and 950 nm (for P) and the absorption response for N was found to be 32 V, for P 4.6 V
and for K 19.8 V, respectively. The device uses intensity of absorbed radiation and rate of light
absorption values to quantify the nutrients in the soil. This work has laid the foundation for
developing portable optical NPK detection sensors based on Vis–IR spectroscopy. Xiao and He
(2019) in their 2019 paper reported that the accuracy of detecting soil nitrogen by near-infrared
(NIR) spectroscopic methods is largely affected by soil particle size, soil type, color and other
physiochemical properties. They found that smaller the particle size, stronger is the intensity of
reflection in the soil NIR spectra. Thus, they showed that there are multiple environmental and
other factors affecting the detection of nitrogen which have to be minimized in order to better
the detection results. Pretreatment of soils was found to be extremely helpful in removing the
dependency of soil nitrogen detection on the unwanted environmental factors. On a similar note
Peng et al. (2019) studied different pretreatment methods for analytical grade detection of soil
nutrients through NIR spectroscopy using the AvaField spectrometer. They discovered that genetic algorithm – back propagation neural network optimization results in accurate detection of
soil total nitrogen, total phosphorus and total potassium. This nutrient determination was verified
through semi-micro Kjeldahl method, molybdenum blue UV spectrophotometer method and
flame photometer method for NPK respectively. Also, they found that partial least-squares
Couteaux, Berg, and
Rovira (2003)
Dinakaran et al. (2016)
Ehsani et al. (1999)
Feng (2011)
Fystro (2002)
Jahn et al. (2006)
Lee et al. (2003)
Linker et al. (2004)
Mouazen, Baerdemaeker,
and Ramon (2006)
Mouazen et al. (2010)
Nie et al. (2017)
Qiao and Zheng (2011)
Shi et al. (2015)
Udelhoven, Emmerling, and
Jarmer (2003)
Wetterlind, Stenberg, and
Soderstrom (2010)
Zornoza et al. (2008)
3.
6.
7.
8.
9.
10.
11.
12.
13.
14.
15.
16.
17.
18.
4.
5.
Citation
Bogrekci and Lee (2007)
Chacon Iznaga et al. (2014)
Sr. no.
1.
2.
0.60 P 0.68 K
0.80 N
0.79
In situ Vis–NIR spectroscopy
0.85
0.72–N
0.84–P
0.68–K
0.95
0.9865, 0.99, 0.99
0.11
5.8 mg/g P
5.3 mg/g K
0.02% N
Potassium
Total N, P and K
Total potassium
Total nitrogen
0.0168
0.47 g/kg
Nitrogen, phosphorus
and potassium
Nitrogen
Phosphorus
and potassium
Phosphorus, potassium
Nitrate
Phosphorus
and potassium
Soil nitrate ion
Total nitrogen
Total nitrogen
Nitrogen
Soil NO3––N
Total nitrogen
Element detected
Phosphorus
Phosphorus and K2O
1 %–4 %
0.0033, 0.02860, 0.00275
1.353–1.667 P
0.202–0.224 K
1.77–1.94
93 ppm
–
0.50–0.69 P
0.33–0.43 K
0.68–0.74
18.60 mg/kg
8.76 mg/kg
0 to 140 mg/L
0.36 g/kg
0.0684
0.01
6–38 mg/kg
<20%
–
RSMEP/RMSEC
0.72–P
0.24–K
0.93
0.87
0.9724
0.85
>0.9
0.955
R2
0.78–0.92
0.16–0.63 for K
0.68–0.83 for P
In situ Vis–NIR spectroscopy
with PLS. ASD FieldSpec-II
spectrometer
Vis–NIR spectroscopy and
relating total N to SOM
Vis–NIR spectroscopy with
BPNN-LVs
IR spectroscopy 900 to
1700 nm. Based on soil
pretreatment and PLS, UVE
and CARS
NIR spectroscopy with principal
component analysis and
LS-SVM
MEMS based NIR absorption
spectroscopy
Relating chemical properties of
soil to spectral characteristic
400 to 2498 nm
ATR FTIR mid infrared range
with PLSR model
In situ Vis–NIR
ATR-FTIR mid infrared
NIR spectroscopy with MPLS
NIR absorbance 1800 to
2300 nm with Fast
Fourier Transform
Vis short wavelength near
infrared spectroscopy
Vis–NIR
NIR spectroscopy MPLSR
Details of spectroscopic
technique used
UV–Vis–NIR spectroscopy
Vis/NIR spectroscopy with
LWR, SVM
Table 2. Summary of Vis–IR spectroscopic methods for NPK measurement.
Successful detection of N but
poor calibration results for P
and K. Data calibration is
taken for only 25 samples.
Distinguishes between high
and low levels
Requires soil preprocessing
and relatively large
prediction errors
Only nitrogen detection. Not
portable only the source is
MEMS based
Only potassium measurement
Requires intensive soil
pretreatment
Medium correlation for K and
poor results for P
Requires more accuracy
Requires spectrometer
Requires bulky
spectrophotometer
Cannot measure low
concentrations
Requires spectrophotometer
Only nitrogen detection
Soil moisture affects results
Requires site specific
calibration
Overlap of some wavelengths
between phosphorus
and K2O
Requires spectrophotometer
Limitations for in situ
measurement
6
R. P. POTDAR ET AL.
JOURNAL OF PLANT NUTRITION
7
regression (PLSR) the most effective multivariate analysis technique employed most frequently till
now is not an effective technique for nutrient detection. Xiao et al. (2018) showed that instead of
the entire spectrum using the subset of required and material sensitive wavelengths enhances the
nitrogen detection by a large extent. On a similar note Kawamura et al. (2019) worked on detection of nutrients using wavebands. They used genetic algorithm for selecting wavebands. They
worked on detecting soil oxalate extractable Phosphorus. Now, phosphorus is not directly detectable so it is correlated with Aluminum (Al) and Iron (Fe) elements in soil for detection purpose.
Out of the complete wavelength range 400 2400 nm only 4.7% was found to be useful in the
prediction. Selected waveband range is 400–600 nm. Also, they found that PLS regression combined with waveband selection improved detection of soil oxalate extractable Phosphorus. Jin
et al. (2020) studied 29 pretreatment methods and 8 regression algorithms for removing the
dependency of spectroscopic determination of soil potassium on environmental factors.
Combination of three methods namely: Savitzky–Golay, standard normal variate and dislodge
tendency was found to work the best. Laboratory verification of potassium detection was done
using flame photometer. Wavelength range used was 350–1700 nm. Yet there was some dependence on environmental factors which doesn’t make the method very reliable. Yao et al. (2019)
analyzed NIR spectrum of different soil types. They found that the absorbance values differed
due to soil organic matter and color of the soil. They worked on developing a model to get rid of
these dependencies using radial basis neural network and Monte-Carlo noninformation variable
elimination and continuous projection algorithm.
Reflectance spectroscopy
Reflectance spectra are of three types: internal reflectance, diffuse reflectance and specular reflectance. Most of the soil nutrient detection is done using diffuse reflectance spectroscopy.
Table 3 shows briefly reviews some prominent reflectance-based sensing techniques:
Based on the near-infrared reflection spectroscopy, Du et al. (2019) published a set of detailed
instructions and research on diffuse reflectance technique for soil nitrogen detection. They developed a portable nitrogen detector. They used a small, compact Fourier transform infrared (FTIR)
coupled spectroscope with a supporting software for data acquisition and spectral analysis. They
identified wavelengths for soil nitrogen as 1500 1850 and 2000 2400 nm for nitrogen containing groups. Their reported work has 0.934 coefficient of determination and root mean square
error (RMSE) 1.923 indicating good quality nitrogen determination by diffuse reflectance spectroscopy. Hu et al. (2016) studied the effect of using a small region of wavelength 1100–2450 nm
for sensing soil phosphorus and potassium and found that narrower sensing range is beneficial in
making sensors. They also discovered that the inclusion of direct orthogonal signal correction
pretreatment method reduces the error by 25 to 39%. Mukherjee and Laskar (2019) developed a
Vis–NIR diffuse reflectance spectroscopy-based sensor for measurement of NPK in soil extracts.
Absorbance for nitrogen was observed at 850, 620 630 nm for Phosphorus and 460 470 nm
for potassium.
Raman spectroscopy
Raman spectroscopy is a rapid soil nutrient testing tool. It uses a strong beam of visible or ultraviolet light to illuminate the sample and collect the scattered Raman spectra. Based on vibrations
and rotations of radiation excited molecules, Raman spectra signature can provide structural
information which serves as a key for sample identification.
Surface enhanced Raman spectroscopy (SERS) based water-soluble nitrogen detection was
reported by Dong et al. (2018). The characteristic peaks of nitrogen were found to be 1028, 1370,
1436 and 1636 cm1 using SERS based on Opto trace Raman (OTR) 202. The calibration
5.
6.
3.
4.
2.
1.
Sr. no.
Bogrekci and
Lee (2005)
Dalal and
Henry (1986)
Ehsani et al. (1999)
Guerrero
et al. (2010)
Shi et al. (2015)
Vagen, Shepherd,
and
Walsh (2006)
Citation
Diffuse reflectance spectroscopy
Vis–NIR reflectance
NIR reflectance spectroscopy
1100 to 2500 nm
NIR reflectance with PLSR
NIR reflectance spectroscopy with PLSR
UV/Vis/NIR reflectance spectroscopy
Details of technique used
0.9184
0.96
0.953
0.88–0.95
Total nitrogen
Soil mineral nitrogen
Total nitrogen
nitrogen
Total nitrogen
6.5 ppm
0.2–0.5 g/kg
–
0.64 g/kg
0.014%
> 0.92
Element detected
Phosphorus
RSMEP/RMSEC
0.61–0.93 13.5–850.3 mg/kg
R2
Table 3. Summary of reflectance spectroscopy methods for NPK measurement.
Limitations for in situ measurement
Portable
Requires site specific soil spectral libraries
At lower concentrations the prediction is poor. Results differ
for different colors of soil.
Requires site specific calibration
Regional – scale study
Requires spectrophotometer
8
R. P. POTDAR ET AL.
JOURNAL OF PLANT NUTRITION
9
equation developed is y ¼ 93.491x þ 1771.5 pointing out the capability of using SERS for watersoluble nitrogen detection. Vogel et al. (2017) reported using deep ultraviolet Raman microspectroscopy for characterization of phosphorus compounds. Lee and Bogrekci (2007) invented and
patented a portable Raman sensor for soil nutrient detection. The device comprises of BTC111E
miniature thermoelectric (TE) cooled fiber coupled charge coupled device (CCD) spectrometer,
sample compartment and power supply. They used Raman spectrum range of 340 3640 cm1 in
their invention. The sensor is capable of remote detection and quantification of soil nutrients like
phosphorus, nitrogen and potassium. Larar et al. (2012) studied soil phosphorus concentration
using Raman spectroscopy. They extracted the useful signal from the original Raman spectrum
for each sample by using bior 4.4 wavelet packet on a platform of Matlab R2011. Spectrum was
recorded in the range of 239 4045 cm1. PLS models were used to forecast the phosphorus concentration. It was found that environmental factors affect Raman signature and hence results.
Also, the spectrum of the cuvette and base sand was found to interfere a lot with the phosphate
Raman signature. Reported R2 is 0.937 and root mean square error of prediction (RMSEP) is
244.57 indicating effectiveness of Raman spectroscopy for phosphorus detection.
Colorimetric
Soil testing kits perform a quick, on-the-spot, and approximate measurement of nutrients present
in the soil (McCoy and Donohue 1979). These kits use the principle of colorimetric technique for
analyzing the soil nutrients. The colorimetric method compares the color change of the solution
with calibrated reference color charts. The shade of the color on the color chart indicates a range
of concentration. By relating color of the solution to concentration of nutrients, colorimetric
method measures the fertility level of nutrients (NPK) in the soil.
The universal method followed in all the soil testing kits is: mixing the soil sample with
extracting solutions and filtering through a filter paper to get an extracted solution containing
nutrients. Adding coloring reagents to get a colored solution. Evaluating the colored solution
using the colorimetric technique described above to measure the concentration of nutrients
(NPK) present in the soil sample. The color change associated with the soil sample indicates the
range of nutrient concentration in the solution, i.e., low, medium, or high nutrients in the soil.
These kits are semi-quantitative, economical, simple, and convenient to use, which makes
them famous and readily available in the market. Results obtained from soil testing kit are
deduced by visual color observations making these kits somewhat unreliable. Also, we can get
only approximate values and not actual concentration from these kits.
To remove the dependency of the soil testing kit’s color interpretation by naked eye and to
improve its accuracy, a combination of sensors, microcontrollers, and optical fiber can be used to
make an independent, portable, and accurate soil sensor. The color sensing element used in the
colorimeter is photocells converts the light intensity into current and records the output on a galvanometer. To further automate the process, microcontrollers can be used to compare these
measured signals with calibrated values and display the result on a liquid crystal display (LCD)
screen. Given below is the detailed review of all such sophisticated colorimetric approaches for
soil nutrient sensing:
Masrie et al. (2017) presented a conference paper entitled detection of NPK soil nutrients
using optical transducer. The optical transducer was made up of three LEDs as a light source and
a photodiode as a light detector. The nutrient absorbs the light from the LED, and the photodiode converts the remaining light to current. An Arduino microcontroller then processed the
current values thus converting the light output from the optical fiber into voltage which is then
displayed as a digital reading on LCD screen. Test results of various soil samples showed that the
optical sensor developed by Masrie et al. could evaluate the amounts of NPK soil content as high,
medium, and low levels. Agarwal et al. (2018) built a NPK measurement sensor based on
10
R. P. POTDAR ET AL.
colorimetry, Arduino Uno and Naive Bayes classification. The system is made up of components
like color sensor, microcontroller Arduino Uno, and soil testing kit. Depending upon the color
intensity of solution, system can sense the amount of soil Nitrogen, Phosphorus, and Potassium
in medium, low and high levels. The results are verified using Naive Bayes classifier. Liu et al.
(2016) in their paper reported a miniature microfluidic channel and MEMS technology based colorimetric sensor for sensing NPK elements. It is a MEMS-based low-cost, high-precision, portable
sensor capable of measuring ppm level concentration of soil nutrients. The portable sensor is
made up of components like micro-fluidic channel, light sources, processing circuit, and a displayer. Photocells are used to convert the light intensity into electrical signals like current, voltage, etc. These signals are lastly filtered to remove noise and amplified. Further, the
microcontroller unit (MCU) deals with these measured signals and displays the results on the
mini-LED displayer. The detection limit for measuring nitrogen was found to be 83.6 ppm, for
phosphorus 143 ppm, and potassium 40.9 ppm using this sensor. Monterio-Silva et al. (2019) built
a compact, modular direct UV–Vis spectroscopy-based sensing system coupled with optical fiber
bundle. It uses deuterium light source-based spectrometer with transmission optical fibers and a
reflection probe for insertion of samples. Aqueous solution of NPK containing fertilizers could be
investigated with the said device. Absorbance wavelength for nitrate and nitrite ion was found to
be 302 and 352 nm. But the system was plagued by interference of competing ions in the spectra
which could not be resolved using linear or logarithmic regression models. Artificial intelligence
algorithm was found to solve the problem of interference in spectrum. Thus, the device could be
used real time by covariance modes to correctly account for interferences in the prediction model.
Yokota, Okada, and Yamaguchi (2007) developed an LED optical sensor for determination of soil
nutrients. The compact optical sensor consists of three LEDs and an input/output (I/O)
data processing peripheral interface controller (PIC) microcontroller and can detect ammonianitrogen, nitrate-nitrogen and phosphorus in aqueous solution. The soil filtrated solution was
color-developed using the soil analyzer – Dr. soil and further processed using the PIC based sensor. Detection limit achieved by the sensor is around 1 mg/100 g.
Optical imaging
Chen et al. (2019) reported a unique technique for determining the nutrient status in plants.
According to them analyzing images of plant leaves could help predict potassium deficiency in
plants. Matrix laboratory (MATLAB) software and support-vector machines (SVM) calibration
model were used together to analyze leaf image and derive potassium level from the given information. Li, Jia, and Le (2019) used 900 1700 nm wavelength for detecting total soil nitrogen
content using a hyperspectral imaging system. They developed a fully automatic and currently
lab-based device. It uses a combination of hyperspectral imaging technology and chemometrics.
Uniform variable elimination – extreme learning machine (UVE-ELM) prediction model was
used. Total nitrogen content was verified using the Kjeldahl method. Aitkenhead et al. (2017)
developed PHYLIS: Portable Hyperspectral Low-Cost Imaging System. It uses Microsoft visual
studio 2010 software and is capable of sensing NPK. But there is a need to develop appropriate
libraries for the device to be used in the field.
Conclusion
The purpose of this review was to study the research done in the field of optical sensing of soil
nutrients–NPK. It is clear from the research reviewed that substantial technological advances
have been made in the field of soil nutrient testing till this date.
As seen in the paper; Vis–IR spectroscopy can detect nitrogen, phosphorus and potassium
with R2 0.99, 0.78 and 0.80, respectively. The concentration of nutrients detected is as low as
JOURNAL OF PLANT NUTRITION
11
1 ppm. But it showed poor results for prediction of Potassium and Phosphorus since Phosphorus
and Potassium could not be directly absorbed in the Vis–NIR region. They have to be correlated
with some soil properties or other elements for detection purpose. Thus, it has great potential for
sensing nitrogen content in the soil and it is a very reliable, sensitive and accurate technique.
However, it was found that the detection of soil nutrients depends on variations in soil and environmental factors resulting in poor detection accuracy. According to review of papers since 2017,
this problem can be solved by applying pretreatment methods and different calibration methods.
The only concern with spectroscopic methods is that typical spectrometers are bulky and expensive, and requirement of site-specific calibration. Overall a lot of progress has happened in the
spectroscopy field which makes it an excellent choice for easy nutrient detection.
There also has been a lot of research conducted on the use of reflectance spectroscopy and
Raman spectroscopy for the determination of the nutrients. Diffuse reflectance spectroscopy can
detect nitrogen with R2 0.99 and phosphorus with R2 in the range 0.61–0.93. Also, it was found
that instead of using the entire range of spectrum, using only a subset of wavelengths could
reduce the detection errors and make the method better. The problem of detecting phosphorus is
solved by using Raman spectroscopy. It can detect phosphorus with 0.937 R2 in the wavenumber
range 200 4000 cm1 Raman spectroscopy thus has excellent phosphorus detection capacity so
much so that it was used as an investigation tool to determine various chemical forms of phosphorus in soil.
From the review it appears that colorimetric methods can be used to develop a portable, costeffective optical sensor for soil macronutrient detection. In general, the colorimetric technique
doesn’t need expensive equipment and perfect measurement conditions or good database or
sophisticated analysis techniques. Most of the results reported are in approximate levels instead of
precise values. But the novel chip-level colorimeter designed by Liu et al. (2016) is capable of
detecting ppm concentration of soil nutrients. Thus, further research on colorimeter-based soil
nutrient detection can be carried out for developing a cost-effective portable sensor. Research
findings suggest that the solution-based soil extractant can be replaced by ion-selective membranes to make the colorimeter-based sensor more compact and convenient.
The latest trend in the field of nutrient testing is the use of imaging techniques. Currently this
method is at an underdeveloped stage and extensive studies will have to be carried out before the
imaging techniques become a prominent name in the nutrient sensing field.
Although much research has happened in the optical sensing field, a cost-effective portable
soil NPK sensor still does not exist in the Indian market. Thus, to this date, a promising, accurate, reliable, capable sensor based on optical methods does not exist in the market. It is clear that
there is an ardent need for developing a sensor. From the review conducted it is found that
MEMS based colorimetry and spectroscopy are the best approaches available in making a sensor.
The colorimetry might be the best available economic approach for doing so because it is cheap
and makes a simple device where site-specific calibration is not required. Whereas, spectroscopy
offers the most accurate and reliable sensing, but this approach tends to be expensive because of
the portable spectrometers required.
Acknowledgments
The authors would like to thank Director, The Institute of Science, Dr. Homi Bhabha State University, Mumbai,
India for providing laboratory facilities.
Funding
Rajiv Gandhi Science and Technology Commission, Government of Maharashtra, India (RGSTC/File2016/DDP146/CR-36).
12
R. P. POTDAR ET AL.
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