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ABSTRACT

The demand for food is increasing day by day to feed the ever-growing population. To meet this demand, farmers are continuously applying nutrients in an excess amount, which leads to poor soil health, low crop yield, and other environmental impacts. To mitigate these issues, sensor-based nutrient management practices have been introduced to optimize the supply and demand of nutrients according to their spatio-temporal variations. These sensors are placed in contact with or close to the crop, which can provide a rapid and real-time assessment of nutrient status. The most widely used digital tools are chlorophyll meter (SPAD), Green seeker, Crop-circle, etc. Remote sensing (RS) techniques such as spectro-radiometer, Unmanned aerial vehicle (Drone), Satellites, and Robots are being used for nutrient management using a single wavelength or combination of wavelengths.

Keywords: Remote sensing, Precision farming, nutrient management, sensors

With a growing global population, the world food demand is expected to increase as well (FAO, 2004). To compensate this demand, farmers are continuously applying nutrients in an excess amount which leads to poor soil health, low crop yield, and other environmental impacts. Apart from this, a lack of synchronization between plant nutrient demand and soil nutrient supply leads to poor nutrient use efficiency. Therefore, a new concept, precision farming has been introduced where nutrients are supplied according to the spatial and temporal variability of nutrients in the field. Sensors are an important tool in precision agriculture to optimize the supply and demand of nutrients according to crop demand. These sensors are placed in contact with or close to the crop which can provide a rapid and real-time assessment of nutrient status.

 Classification of sensors

                Proximal optical sensors are classified into three categories: transmittance based sensors, reflectance based sensors, and machine vision systems (Padilla et al., 2018). Transmittance based sensors estimate the relative leaf chlorophyll content by measuring the absorbance and transmittance of red and near infra-red radiation by the leaf (SPAD). Absorbance of red radiation increases with chlorophyll content, resulting higher chlorophyll meter values. Reflectance based sensors estimate nutrient concentration by measuring specific wavelengths of radiation absorbed or reflected from plant canopy. These reflectance based sensors are further classified on the basis of the platform at which they are being used. Green seeker, CM1000 and spectro-radiometer are used on the ground platform. Unmanned aerial vehicle (UAV) also known as drones are used at airborne platforms whereas satellites are used at space-borne platforms. Third category i.e. machine vision systems, captures the image over crop field, and after image analysis using different software, they estimate the nutrient content in plants (Robots).                   

Soil Plant Analysis Development (SPAD Meter)

Minolta Co. (Osaka, Japan) has developed the chlorophyll meter (SPAD-502), which effectively measures the relative greenness or chlorophyll content of leaves (Turner and Jund, 1994). It is a simple, quick, and non-destructive method for estimating leaf chlorophyll content. It has two diodes, one producing a peak wavelength near 650 nm (red), and the other, a peak near 940 nm wavelength (NIR) (Ali et al., 2017). The SPAD meter estimates the relative chlorophyll concentration in a leaf by measuring the differential transmittance of light through it. Chlorophyll concentrations of leaves are correlated with SPAD meter values.

Chlorophyll Meter 1000

It is a chlorophyll reflectance meter and hand-held working on parallel principles to the SPAD meter. The Spectrum meter works on the fine-leafed turf stands canopy. This allows larger area assessment and integrates many leaf surfaces.

Green Seeker

Green seeker is an incorporated system of optical sensor and application system for optimizing N application. This unit emits light in two wavelengths and the light reflectance from the target (plants in the soil) is measured. The Green seeker active lighting optical sensor uses high-intensity light-emitting diodes (LEDs) that radiate light at 780 mm (NIR) and 600 nm (red) as light sources. The normalized difference vegetation index (NDVI) is calculated from the NIR and red values.

Crop-circle

It measures plant reflectance using a light sensor. It used up to 6 spectral bands: Blue (450 ± 20 nm), Green (550 ± 20 nm), Red 1(650 ± 20 nm), Red 2 (670 ± 11 nm), Red edge (730 ± 10 nm), and NIR (760 nm). As a result, a variety of spectral vegetation indices can be derived. Some of these indices have been found to be better than the traditional NDVI and RVI indices for estimating crop N status.

 Advanced Remote Sensing Sensors

Electromagnetic energy incidents on the surface features are partially reflected, absorbed or transmitted through it. The fractions that are reflected, absorbed, or transmitted vary with material type and the condition of the feature. The spectral properties of vegetation are strongly determined by their biophysical and biochemical attributes such as leaf area index (LAI), the amount lives and senesced biomass, pigment and moisture content. In the visible domain (400–700 nm), absorption by leaf pigments is the most important process leading to low reflectance and transmittance values. The main light-absorbing pigments are chlorophyll a and b, carotenoids, xanthophylls and polyphenols, and all pigments have overlapping absorption features. Chlorophyll a (Chl a) is the major pigment of higher plants and together with chlorophyll b (Chl b) accounts for 65% of the total pigments.

 In the near-infrared domain (near-IR: 700–1300 nm) leaf pigments and cellulose are almost transparent so that absorption is very low and reflectance and transmittance reach their maximum values. This is caused by internal scattering at the air–cell–water interfaces within the leaves. In the midinfrared domain (mid-IR: 1300–2500 nm), also called shortwave-infrared (SWIR), leaf optical properties are mainly affected by water and other foliar constituents. The major water absorption bands occur at 1450, 1940 and 2700 nm and secondary features at 960, 1120, 1540 1670 and 2200 nm (Sahoo et al., 2015).

Nutrient stress can be detected using spectral reflectance pattern of plant canopy. A healthy plant has more chlorophyll resulting in more light absorption and less reflectance in red band while stressed leaves have low chlorophyll which leads to higher reflectance in red band. In NIR band stressed plant has low reflectance compared to a healthy plant. Therefore, this information is useful to monitor the nutrient stress in plants.

Spectro-radiometer

Spectro-radiometer is a transportable battery-powered spectrometer. It has a spectral range 350–2500 nm wavelength with 25 degree field of view (FOV). It has 2151 channels and detectors are specific for each band. It has silicon array for VNIR (350-1000 nm), InGaAs (Indium, Gallium, Arsenide) Photodiode for SWIR 1 (1001-1800 nm) and SWIR 2 (1801-2500 nm) bands. Calibration or optimization of the instrument is done using a standard spectral on white reference panel. Resultant data are processed using ASD View Spec Pro software to produce values at 1 nm interval.

Unmanned Aerial Vehicles (UAV) / Drone

Unmanned Aerial Vehicles (UAV’s) has emerged as an efficient supplement to remote sensing data. An unmanned aerial vehicle is a pilotless aircraft which is flown without a pilot on board and it is remotely controlled. It’s typical flying at altitudes ranging from 100 m to 500 m.

Satellite Remote Sensing

A satellite has capacity to collect images covering a much greater area. Multispectral and hyper-spectral images are commercially available from satellites. There are many satellites that have been launched which provide data day and night.

 Robotics and Nutrient Management

It is more desirable to develop a real-time plant and quality monitoring system with no need to move the plants sample to the laboratory. This could be achieved by an autonomous robot equipped with a digital camera which moves between the plant rows (Amrutha et al., 2016). A robotic-based framework is consist of -moving module-  DC motor gearbox, an image acquisition unit- CCD color camera, a data analysis/storage/transfer module, GPS module etc.

Advantages of Sensor based Nutrient Management

Optimal nutrient management is essential for profitable crop production and to minimize nutrient losses to the environment that are a consequence of an excessive nutrient supply. Sensors play a vital role to enhance nutrient uptake and nutrient use efficiency.  It provides a real-time quick nutrient status in plants which is not possible by soil and plant test-based methods. It detects stress earlier than the visual appearance of nutrient deficiency symptoms. Therefore, farmers can supply the nutrient prior to the yield reduction. It responds quickly to the deficiency of nutrients during crop growth. Application of fertilizer according to the spatio-temporal variability of nutrients in the field resulting in higher nutrient use efficiency. Some negative environmental impacts such as leaching of excessive nitrogen are also reduced. These sensors synchronize fertilizer application with crop nutrient demand resulting in better uptake of nutrients by plants and finally higher crop yield. Sensors also reduce the accumulation of nutrients at toxic level in various plant parts resulting healthy life.

Limitations of sensor-based techniques

The stress-induced sampling errors may influence chlorophyll contents in the plants. The varietal / species based difference may lead to variable results from different plant species using the same sensor. These techniques are not helpful for detecting luxury nutrient uptake or over-fertilization. The high cost of sensors is also a major limitation. Remote sensing sensors like drone, satellite etc. have specific limitations like clear weather conditions, sensor resolution, specialist software for data analysis and professional operators.

Conclusions

High NUE can be achieved by replacing blanket fertilizer recommendations with an optical sensor-based strategy. Digital meter devices, such as SPAD, Crop-Circle and Green seeker are widely used but more expensive as well. Currently, drones are more popular to detect the early sign of nutrient stress in plants. For a regional scale, satellite or aircraft-based techniques are the most feasible option to monitor the nutrient status in crops. Site-specific nutrient management strategies based on sensors are the most powerful tools to enhance nutrient efficiency.

References

Ali, M., Ani, A., Eamus, D. and Tan, Daniel K. Y. (2017) Leaf nitrogen determination using non-destructive techniques–A review. Journal of Plant Nutrition 40, 928-953.

Amrutha, A., Lekha, R. and Sreedevi, A. (2016). Automatic Soil Nutrient Detection and Fertilizer Dispensary System. International Conference on Robotics: Current Trends and Future Challenges (RCTFC).

Food and Agricultural Organization of the United Nations (FAO): Food and Population: FAO Looks ahead, 2004

Padilla, F. M., Gallardo, M., Peña-Fleitas, M. T., De Souza, R. and Thompson, R. B. (2018). Proximal optical sensors for nitrogen management of vegetable crops: A review. Sensors, 18(7), 2083.

Sahoo, R. N., Ray, S. S. and Manjunath, K. R. (2015). Hyperspectral remote sensing of agriculture. Current Science, 848-859.

Turner, F. T. and Jund, M. F. (1994). Assessing the nitrogen requirements of rice crops with a chlorophyll meter. Australian Journal of Experimental Agriculture, 34(7), 1001-1005.

 

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