Feature-based comparison of UAVs.
The fiber optic sensors can be used to create a truly distributed chemical sensing capability for selectively detecting metal compounds by spatial and temporal acquisition over large distances in the subsurface. In addition the fiber optic sensors have several advantages such as small size, light weight, immunity to electromagnetic interference (EMI), high temperature performance, large bandwidth, high sensitivity, and environmental ruggedness (Krohn, 1988). Most current technologies capable of detecting contaminants use strategically placed sensing or monitoring devices. This works reasonably well if plausible event location is known, hence settle recording vast amounts of benign data over time until the appearance of the suspected event. This approach remains limited for application in large spatial scales in the geo-environment and subsurface. A simple approach is to suppress all the benign data by triggered transmittal of the signals only at the spatial and temporal vicinity of the event. This, in essence the “truly distributed” sensing capable of delivering the event signal “wherever” and “whenever” it might occur, as opposed to only at strategic places where the sensing devices are pre-located. The revolutionary advances in flexible sensing and distributed data processing permits us sensing in this truly distributed manner.
Sensors based on fiber optic cable functions make use of the following important features of the cable to sense the environment: (1) optical loss: intrinsic and extrinsic energy loss properties, (2) refractive index: index profile in radial direction and the reduction of index fluctuation along the axial direction; (3) shape: cross sectional shape and size, the surface finish and the fluctuation of the size along axial direction. Present fiber optic sensors mostly use energy loss principles (i.e., changes in optical power in linearly positioned wave-guides) for chemical detection. These can be limited for distributed applications if energy depletes over a short stretch of the fiber sensor, or frequent sensor points are needed at a prohibitively expensive cost. Other sensors use the changes in refractive index and/or cross sectional size of the fiber cable that change the light scattering property in optical fibers, known as Brillouin scattering (Horiguchi et al, 1995; Kee et al, 2000).Fiber optic sensing based on Brillouin scattering has been used successfully in civil infrastructure for health monitoring (Bao et al, 2001; Ohno et al, 2001). In this chapter a background on use of optical fibers for chemical sensing and new developments and proposed advancements are discussed.
In an optical fiber sensor a physical, chemical or biological variable can interact with the light and produce a change in one of their parameters. It is desirable to produce an optical signal related uniquely to the parameter of interest. These sensors use the optical fiber either as the sensing element (intrinsic sensors), or as a means of relaying signals from remote sensing area to the signal processor (extrinsic sensor), or both. Optical fiber sensors take advantage of the inherent fiber optic characteristics which include their lightweight, of very small size, passivity, low-power requirement, resistance to electromagnetic interference, environmental impact and corrosion, their bandwidth, and flexibility. They can be installed in areas normally inaccessible by conventional sensors, they can be interfaced with data communication systems and pose no risk of electric shock in live measurements. These attributes have allowed optical fiber sensors to displace traditional sensors for measurement and monitoring of rotation, acceleration, electric and magnetic field, temperature, pressure, acoustics, vibration, linear and angular position, strain, humidity, viscosity, pH, gas and chemical content among many others.
Use of Fiber Optic Sensors is a viable real-time data gathering approach by surface-adhering or embedding the fiber to a specimen under evaluation. The concept of embedding fiber-optic sensors into structures has generated a great deal of interests in aerospace engineering initially and more recently in civil engineering. There are several types of chemical sensing techniques based on optical waveguides (Ho et al, 2001). Among those are fiber Bragg gratings (FBG), which is marking of a fiber with a laser to create a local narrow band pass filter sensitive to environmental parameters (Guemes et al, 1998; Schulz et al., 1998). Optical time domain reflectometry (OTDR) consists of sending a powerful light pulse and observe modification in the reflected light due to local in homogeneties along the fiber. The pulse losses correspond to specific environmental interaction. The evanescent pulse technique is also based on OTDR, in which the fiber cladding is modified to interact with the environment and the pulse travels partially through the cladding. These sensors demand large optical power, due to the cumulative energy loss at the points of contact with the chemicals.
Over the last decade, there has been rapid development in the area of smart sensor technologies, in particular using structurally integrated optical fiber to form the basis for smart structure technology. A variety of configurations have been developed for measurement of strains and deformations in structures, including localized-type such as fiber Bragg gratings and multiplexed long gauge interferometric sensors, and distributed sensing schemes including Stimulated Brillouin Scattering (SBS) or Brillouin Optical Time Domain Analysis (BOTDA) (Bao et al, 2001) and Brillouin Optical Time Domain Reflectometry (BOTDR) (Pamukcu et al, 2006; Anastasio et al, 2007).
Between different types of optical sensors reported, there are those based on sensitive coatings onto the fiber surface, Fabry-Perot interferometers, long-period fiber gratings (LPFG), LPFG with sensitive films, hetero-core devices, fiber Bragg gratings on doped fibers (i.e, Germanium doped). Fiber gratings are structures consisting of a periodic perturbation of the optical and/or geometrical properties of an optical fiber. Depending on the pitch of the perturbation, fiber gratings fall into two distinct categories: short period gratings, known as fiber Bragg gratings (FBGs) and, long period gratings (LPFGs). Stretching the fiber gratings causes a change in grating period, hence the wavelength of the reflected light. This makes the FBGs ideal for localized temperature and strain measurements. Unlike FBGs in which counter directional coupling occurs in the core, co-directional coupling occur in LPFGs between the core and cladding. This feature renders LPFGs sensitive not only to temperature and strain, but also to bending causing a curvature, to hydrostatic pressure, to torsion and to ambient refractive index changes. The closer the ambient refractive index to that of the cladding the stronger the sensitivity to refractive index changes. It is this high sensitivity that has piqued the interest in development of various types of refractive index-based LPFG sensors which constitute most of the chemical sensing applications (Orellana and Haigh, 2008; Kasik et al, 2010).
Point detection fiber optic sensors have been developed successfully for measurement of liquid levels, chemical species, drugs, environmental agents (such as pollutants and pesticides), biochemical reactions, and to monitor a wide variety of chemical processes (Wolfbeis, 2000). A fiber optic laser induced breakdown spectroscopy method was demonstrated in the field using a push-cone device, which is a single point, single time measurement technique. The most common configuration for optical pH sensors, and other environmental parameters, employs a fluorescence indicator (Lee et al, 2000). Among the different types of optical fiber devices used in pH sensing are, hetero-core fibers, U-bend fibers, fiber Bragg and long-period gratings, fibers and fiber tips with active doped cladding, among others (Kocincovaet al, 2007). Some of the substances that can be detected or identified using optical fiber sensors are volatile organic compounds (alcohols, formaldehydes, methane, ketones, COx, O2, and H2), some metallic ions like Ca, Al, Cu, Zn, Hg, V and Pb (Jeronimoet al, 2007; Wolfbeis, 2008).
Wide application of advanced chemical sensing in the environment may suffer from scaling issues. The real-world conditions often require self-referencing, spatially distributed, temporally continuous, and chemically selective sensors for monitoring regions spanning over long lengths or wide areas. When large area monitoring for chemical agent intrusion is required, use of currently available point sensors can be cost prohibitive. Other non-point, distributed detection methods based on energy loss principles (Buerck et al, 2001) may also be inadequate when scaled to wide area monitoring due to extensive energy input requirements.
One of the unique features of the optical fiber technology is the possibility to construct distributed sensors, in which the measuring can be determined along a line of space with a given spatial resolution (Galindez-Jamioy et al, 2012) by, for example, Brillouin optical time domain analysis (BOTDA) (Cui et al., 2009, 2010 and 2011); an hetero-core LPFG sensors. In here, we examine current and proposed application of these techniques to spatially distributed, temporally continuous, and chemically selective sensing applications in soil and water environment. The premise of Brillouin technique goes back to 1920 when physicist Leon Brillouin first studied the diffusion of light by acoustic waves. The phenomenon he observed was a frequency change of scattered light. The first major papers related to distributed fiber optic sensor based on Brillouin were generated in mid-nineties (Bao, et al. 1995; Fellay et al, 1997). Current research on Brillouin sensing may be divided in three categories: photonics (the physics of Brillouin); data processing and post processing to improve signal to noise ratios, and applications of distributed sensing to civil infrastructure and environment.
The interest on optical techniques to measure or detect chemical agents have been continuously extending and growing over the last forty years. Special attention has been focused on the development of optical sensor to detect heavy metals, due to the hazardous effects of these ions on the health of human beings and ecosystems. Optical methods have the advantage of being fast, simple, compact, portable, low-cost, with sensitivities and resolutions improved to detect in the picomolar range.
Combined with other technologies, like microfluidics systems, optical waveguides, or MEMs, optical methods are suitable for application where conventional electrodes cannot be used because of their large size or because of the risk of electrode shock during in vivo measurements. Due to their minute size; these optical microsystems are capable of gathering diverse data with a small amount of analyte. The diversification of optical techniques have made possible to construct novel sensing platforms to detect heavy metals in air, water or soil, food and beverages, or biological samples.
Optical sensors to detect heavy metals employ an optical transduction technique, i. e. an element that “translate” the chemical variable into an optical signal (intensity, wavelength, polarization or phase), to yield analyte information (McDonagh et al, 2008, Grattan and Meggitt, 1999). Optical chemical sensors can be categorized, according to the transduction technique, in direct sensors and reagent-mediated sensing systems. In direct sensors the element of interest is detected directly via an optical property of the sample such as scattering or florescence, for example.
However, most heavy metals optical sensor uses an intermediate agent. Most of the optical chemical techniques to detect heavy metals are based on optical absorption, fluorescence, Raman spectroscopy, or surface plasmon resonance, whereby the perturbed signal is related to the reaction of the intermediate agent under the presence of a specific heavy metal. In general, all these techniques involves the interaction of an incident beam over an analyte or indicator element yielding transmitted, reflected or fluorescent signal. A schematic representation of the spectroscopic principle, the working mechanism of an optical sensor is shown in Figure 1.
Among the optical chemical techniques, the simplest to implement is that based on the measurement of light absorbed by a sensitive heavy metal layer. Absorption in a gas or liquid, where it is assumed that each single molecules equally contributes to the total light absorbed, may be characterized by a Beer-Lambert law, or simply the Beer law,
where IT and II represents the intensity of the transmitted and incident beam, ε is the molar absorptivity (Lmol-1 cm), and C is the concentration (mol L-1) of the absorbing species and d is the absorption path length (cm). In the case of a solid, absorbing and homogeneous medium, the transmitted signal is calculated using the Lambert Bouguer law, expressed as , where x is the thickness of the medium and α is the extinction coefficient. The Beer law can also be expressed in terms of the absorbance (or optical density) A:
There exists a linear relation between the absorbance and the concentration of the element to be measured. However, in order to observe the linear dependence of absorbance on concentration, the incident beam should be ideally monochromatic. In the case that a wide broadband light source is used, the contribution of all wavelengths must be considered, in such cases the equation (2) becomes:
Also the presence of highly absorbing or highly scattering media should produce a deviation from perfect Beer law behavior. In the case that more than one absorbing material is present the absorbance contribution of each species must be considered. In most of the absorbance-based heavy metals sensors an intermediate agent, an optical film that changes its absorbance according to the concentration of a specific heavy metal, is used (Antico et al, 1999;Guo et al, 2006).
A special case of the absorption-based sensors are those schemes where materials that change their color under the presence of a specific heavy metal are used (Balaji et al, 2006; Prabhakaran et al, 2007). The reaction of the sensitive components to the concentration of a specific ion produces a photochromic reaction that can be observed with a naked eye. Such materials are often in solution, but for sensing the most attractive are those that can be deposited as thin films over a substrate. The instrumentation of absorption-based sensors is the simpler of the optical heavy metal techniques, since it can be implemented with a monochromatic light source and a photodetector. This also makes this technique very susceptible to be implemented in microscopic opto-fluidic configurations that could diversify the technique.
It is well-known that chemical reactions could lead to changes in the complex refractive index of a substance; this fact has been impulse researchers to design and fabricate materials that react with heavy metals that can be used as transducers. When these materials, commonly in the form of a thin layer, are illuminated with an appropriated light the signal will be partially or totally reflected. However, this reflectance will change when the layer is in contact with a specific metal that it reacts with. If the refractive index of the layer is purely real, the changes in the reflected signal can be estimated by using the Fresnel formulae.
However, in most cases the optical response of these materials under the presence of heavy metals are more complex and involve a change in the real and imaginary parts of the refractive index, that produce changes in reflectivity and absorbance. Also, there is a contribution of scattered light. So, the reflected signal is composed of light from different sources, however, also in this complex response the signal reflected is used to deduce, directly or indirectly, the concentration C of the heavy metals.
The reflected-based techniques are specially used in optical fiber schemes since the set-up is very simple to implement (Yusofand Ahmad, 2003, Guillemain et al, 2009). The material sensitive to the heavy metals are directly deposited over the fiber tip or in a substrate that will be illuminated by an optical fiber. The reflected signal is usually collected by the same fiber, but frequently another fiber or fibers are used to collect it. The reflected signal propagates along the fiber to the detector, where it is analyzed in order to determine the heavy metal present and their concentration (Figure 2).
Some materials have the property of being fluorescent when they are illuminated with a light source of appropriated wavelength. The fluorescence is the optical radiation generated when electrons of an atom or molecule return from the excited to the ground state after absorption of a photon from an excitation light source. In general the energy of the excited photon is lower than the absorbed one so the wavelength of the fluorescence signal is longer than that of the excitation.
The intensity of the fluorescent signal (IF) is proportional to the intensity of light absorbed by the sample (II-IT), therefore it is possible to establish a direct relation between the intensity of the fluorescent signal and the concentration of an absorbing material. This feature is very important for sensing since intensity of the fluorescence increases as the concentration of the absorbing species augments. Although, we have just made reference to the fluorescence intensity, for sensing, the decay time of the fluorescence signal is more frequently used because this parameter is less sensitive to source fluctuations, interference from ambient light or drift due to aging of detector. It is possible to design and fabricate a fluorescent material sensitive to a specific heavy metal. Thus, the intensity, wavelength and life time of the fluorescent signal will change under the presence of this metal. Fluorescence-based techniques are the most used to detect the presence of heavy metals due to its extraordinary sensibility (Mayra et al., 2008;Achatz et al, 2011, Aksuner, 2011).
The most popular label-free refractometric technique is the Surface Plasmon Resonance (SPR), since it allows the direct observation of chemical reactions in real time without the use of markers or labels. SPR is a quantum optical-electrical phenomenon produced by the interaction of light with a metal surface. Actually, the surface plasmon is a charge density oscillation that exists at a metal-dielectric interface. The plasmon propagates in a direction parallel to the metal-dielectric interface in the boundary of the metal and the external medium (Figure 3).
These oscillations are very sensitive to any change in the optical refractive index of the material at the boundary. The optical excitation of plasmon can be achieved in a three-layer system consisting of a thin metal film sandwiched between two isolators of different dielectric constant (Maier, 2007), where the phase-matching condition between the optical and plasmon wave vector is fulfilled. In the optical domain, the surface plasmon excitation will be observed as an intensity transmission loss at a specific wavelength. The wavelength of the dip depends on the refractive index of the two dielectrics and the thin metal film, and the propagation constant of the optical waveguide. There are three common method to excite surface plasmon, using a prism coupler and the attenuated total reflection, a periodic grating, and an optical waveguide planar (Figure 3A) or cylindrical. The prism coupler technique is the most popular since exhibits a good sensitivity, stability, and reproducibility for the measurement of heavy metals (Forzani et al, 2007; Lin et al, 2009; Abdi et al, 2011; Fen et al, 2012 and 2013; Fen and Yunus, 2013). For heavy metal detection a sensitive thin film layer is deposited over the thin metal film, so when the target heavy metal interacts with the layer a refractive index change is produced. The surface plasmon conditions changes and the peak wavelength shifts as can be seen in Figure 3B. SPR is the most sensitive refractometric method, with a theoretical resolution of 1x10-7, so it is possible to detect very small traces of heavy metals.
Figure 4, 5 and 6 show the stimulated Brillouin scattering based BOTDA photonics configuration and the principle of measurement used at Lehigh University Geo-sensing laboratory, respectively (Texier et al, 2005; Pamukcu et al, 2006; Turel and Pamukcu, 2006; Anastasio et al, 2007). Brillouin is a nonlinear effect, in which light is scattered at well-defined points along the fiber where the acoustic properties of the fiber are locally modified by the environment. The stimulated Brillouin scattering (SBS) it is an acoustic – optical process which is useful for distributed measurements of a probe beam by the SBS interaction with a counter-propagating nanosecond pump pulse. In the SBS technique, as in a null detector, the pump and probe are initially de-tuned by a (frequency) that is slightly greater than the Brillouin frequency. Therefore, in unstressed fiber, the base line remains flat resulting in a self-referenced sensor eliminating the need for duplication with another reference fiber.
The Brillouin line being intrinsically narrow (~20 - 50 MHz), the initial de-tuning can be quite small so that the amount of strain required to generate a signal is also quite small (0.001 %), allowing for higher resolution and sensitivity of the sensor compared to other fiber-based measurement techniques. When SBS based sensors are used for environmental sensing the fiber is hitched or bonded with selective polymer transducers that are mass detectors in direct contact with the surrounding medium. The polymer reacts to the surrounding (i.e. moisture, pH, target chemical) by selectively absorbing the target compound and it swells. Localized swelling of the bonded or hitched polymer produces tangential, axial or radial stresses on the fiber depending on the physical coupling. These stresses result in axial straining of the fiber and a measurable change in its local acoustic properties, hence a Brillouin scatter of the transmitted light. The location of the generated signal is determined by time domain reflectometry.
In the case of LPFG`s the environmental changes produce a spectral shifts, that required a sophisticate or a complicate equipment. These devices are very sensitive to temperature changes so to measure another different parameter it is necessary to make the corresponding compensation. One alternative to avoid these difficulties are hetero-core fibers. These devices are constructed by changing the diameter of the core in a small length (mm) in a transmission line (Figure 5 and 6), which causes the optical wave to expand within the cladding in the single-mode region of the hetero-core, thus the evanescent field can easily interact with the external medium.
Owing to the core diameter mismatch, some of the light is guided by the cladding of the SM fiber (Figure 7). This makes the transmission of the device dependent on the refractive index of the external medium. The sensor exhibits maximum transmission changes when the index of the sample medium approaches that of the SM fiber cladding. The device can operate at different wavelengths as well as when coated with thick films made of variable index materials. Moreover, standard emitters, fibers, detectors, etc., are needed to fabricate the sensor, which makes it attractive for diverse applications (Villatoro and Monzon-Hernandez, 2006).
The usefulness of SBS for sensing is general: any change in external conditions, that affects the acoustic properties of the optical fiber, can in principle be detected. This is true of direct temperature and pressure changes, but can also be true of changes in chemical environment that can be made to result in temperature and pressure changes. An SBS based BOTDR sensing system was used to detect water content changes in soil. Water transducers (hydrophilic polymers) were tested to correlate Brillouin strain response to the water content of the surrounding soil environment. In these experiments, the optical fiber was wound and secured about discretely placed discs (2 cm length x 5 cm diameter) of AEP60 hydrophilic polymer (Figure 8A), stringed along 100-m fiber continuous optical fiber. The diameter of the polymer disc was selected to accommodate the minimum curvature of bending of the fiber, as shown in Figure 8B.
The optical fibre at the inlet and outlet of the string of four transducers were spliced to spools of fibre on each end, and connected to the photonic set-up. Each water transducer was then embedded in a wet clay sample of predetermined water content (5, 10, 20 and 30 % by dry weight of clay), as shown in the inset sketch of Figure 9B.
The clay samples were packed in equal volume, watertight, cylindrical cells of 14-cm diameter and 28-cm height. The experiments were conducted in a temperature-controlled environment, at 25°C so that Brillouin scattering measurements were not influenced by thermal expansion or contraction of the fibre. The Brillouin shift was measured with 5 to 20 minute intervals up to the maximum observable swelling. When no significant change in Brillouin shift was recorded for three consecutive measurements, the transducers were removed from the soil chambers and left for open air-drying.
The Brillouin shift measurements were recorded with 5 to 20 minute intervals until the drying phase was completed. Since Brillouin readings could be recorded for strains as low as 10, very small volume change due to water absorption could be detected in a few minutes. Figure 9B shows the time rate of Brillouin signal changes during the hydrogel swelling and drying cycles of the 4-different water content clay hosts.
In the experiments described above, the AEP60 polymer used would typically expand from 38 % to 400 % over dry volume when exposed to water. They are non-toxic and are manufactured in medical grades, approved for use in human wound care applications. This group of polymers does not swell in hydrocarbons and chlorinated solvents and has high thermal resistance. They are cross-linked to give them mechanical stability and accurate expansion characteristics. The water absorption and expansion factor can be accurately pre-defined at the formulation stage. Full expansion is reproducible over many wetting and drying cycles and is consistent over a wide range of pH and dissolved solid concentrations. A different integration of optical fiber and polymer transducer was used to improve the polymer response kinetics as shown in Figure 10. In this design, the polymer was reduced to smaller size discrete sleeves (1cm length x 0.3 cm diameter) that were bonded over the optical fiber. The bonding adhesive used was Locktite 414, a super bonding, quick drying adhesive containing cyanoacrylate and is intended for plastics and vinyl. The Locktite 414 was applied to each end of the polymer sleeve also.
The reduced size was anticipated to improve the swelling kinetics and alleviate the hysteresis affects observed in the previous configuration. The working principle of the integrated sensor is also depicted in Figure 10, where first the influx of the target substance (e.g. water) into the polymer transducer causes swelling of the bonded polymer. The swelling causes the bonding interface to strain and cause the fiber elongate in tangential pull. The fiber strain can then be recorded with location and amplitude, as shown in Figure 11, indicating where along the fiber line the influx of the target substance had occurred, and also the calibrated quantity of the substance based on the degree of swelling of the polymer, respectively.
Repeated swelling and shrinkage tests of the polymer sleeve component of the integrated water sensor showed hysteresis of length and diameter change. Both the length and diameter of the of the polymer sleeve expanded by 35 % (~ 0.35 cm and ~0.1 cm, respectively) after being soaked in water for three days. The majority of this expansion occurred during the first 12 hours of soaking. Increasing with every cycle, the final dry length of the polymer was greater than the original by ~ 0.025 cm (~2.5 %). As more cycles were completed, the diameter increased to its maximum faster, but the value of this maximum decreased. The final diameter of the polymer sleeve was fairly constant for each cycle, slightly less than the original by ~ 0.0005 cm (~0.2 %).
The magnitude and rate of swelling correlated directly with the initial water content when the polymer sleeves were embedded in test clay specimens of different water contents. Once again the full swelling occurred between 8 to 12 h when the sleeves were embedded in wet clay. Figure 12A shows that the polymer linear extension and clay water content relation was fairly linear. The linear relation is desirable for robust calibration. Figure 12B shows the dimensionless frequency shift response of the integrated water sensor to clay water content increase. The figure plots two spectrums, the shift spectrum at the location of the expanded polymer and a spectrum near the polymer location that does not undergo the swelling stress.
The actual Brilloin frequency shift was measured 0.0432 GHz corresponding to axial strain of 0.098 % for the fiber. The actual elongation of the polymer rod was 3 mm, which fully transferred to the fiber would have corresponded to an average strain of 0.14 % for the fiber over the 12.6 ns pulse. This meant that about 70 percent of the axial tug generated by the swelling of polymer was transferred to the fiber. The other 30 percent can be attributed to slippage across the length of the bond between the fiber and the polymer sleeve or other process related inefficiencies.
These test results demonstrated the viability of integration of optical fiber with reactive polymer as a BOTDR based sensor. Yet, the slow kinetics of the response (~12 hr for full swelling in case of water) rendered the prototype assembly limited for quick detection and measurement purposes. Hence a new polymer and fiber-optic cable configuration is proposed where a thin layer (e.g. on the order of few hundred μm) of the reactive polymer is brushed and bonded onto the fiber-optic cable. Figure 13 presents variations of the conceptual sensor where the reactive polymer coat is continuous. The working principle of this new configuration is similar to the previous ones described, all based on BOTDR, with the exception that the continuous coating of a thin layer reactive polymer is anticipated to provide a truly distributed and fast detection mechanism.
Point detection fiber optic sensors have been developed for measurement of liquid levels, chemical species (inorganic and organic), drugs, environmental agents (such as pollutants and pesticides), biochemical reactions, and to monitor a wide variety of various chemical processes (Wolfbeis, 2000). One of the major components of a sensor system is the sensing or recognition element. Polymers have often been utilized as a chemical sensing material. The interaction of an analyte with the polymer coating is of prime importance. A signal needs to be generated once this interaction occurs, and much work has been carried out to understand the interaction of the polymer coating with the analyte molecules, especially in terms of the diffusion behavior of the analyte through the polymer coating to the actual sensor itself and its subsequent enrichment within the polymeric coating layer (Philips, et al, 2003).
The integrated chemical sensor based on BOTDR discussed in here is a single SBS (Stimulated Brillouin Scattering) sensing optical fiber bonded with such a polymer, which swells selectively in the presence of a target chemical. The polymer coat is cross-linked to swell in a preferential direction. During swelling, the bonded polymer coat exerts a radial or tangential force at the polymer-fiber interface, hence an axial strain on the fiber. The SBS signals are generated along the fiber at the discrete points of chemical contact triggered by polymer swelling, hence the local axial strains in the fiber. Consequently, the location of the target chemical can be detected simply by linear positioning of the SBS signals over the entire length of the cable. Because the detection is based on local physical changes in the fiber and not on loss of transmitted light, widely distributed sensing is possible without high power requirements. The specific detection and measurement components of the integrated sensor described above include, a core/shell type (multi-layer) polymer coating, and an inner fiber optic cable core, or a rigid woven jacket conceptualized in Figure 13.
In this conceptual design, the outer cross-linked polymer coating (shell) serves as a rigid, high permeability filter and confinement to the inner polymer (core). The core is a flexible, chemically selective polymer, preferably with rapid mass sorption kinetics. While the highly networked rigid polymer shell confines and directs the cores welling toward the fiber optic cable, the selective detection of chemicals is based on the thermodynamics and kinetics of chemical sorption and swelling of the core polymer layer. A volume change or “swelling” occurs in the inner flexible polymer layer as a result of mass sorption of the surrounding analyte. Solute/solvent sorption interrupts the intermolecular forces between individual chains of a lightly cross-linked or a linear polymer adjacent to the glass fiber, resulting in swelling forces. The outer, permeable but rigid polymer layer comprised of highly networked cross-linked chains help confine the volume change tendency and direct part of the swelling forces inward (radially and/or longitudinally) thus creating a hoop or a tangential stress on the fiber at the polymer interface. This “pinch” or tug” of the fiber cause changes in the elastic and refractive properties of the fiber locally, generating a shift in its original Brillouin frequency at that local. Comparing the new frequency to the original, it is then possible to quantify the change in terms of the swelling forces and the quantity of the absorbed substance.
A multi-chemical sensor can be developed by bundling polymer-coated fibers of different functions and sensitivities. The entire sensor assembly can be several tens of kilometers of optical fiber hosting several 100 measurement locations on the same line, each at a minimum spatial resolution of 1-meter. The photonics assembly connected to one end of the fiber allows fast detection of discrete sensing locations rendering the entire assembly a multiplexed network of many point sensors on the same transmittal line. Hence, the fiber line coupled with the stringed transducers can be embedded linearly or laced into a host medium (i.e. water pipeline, paved surfaces, porous media such as soil or concrete) to detect target chemical(s) online over large distances, areas or spaces by linear positioning of the fiber.
The molecularly imprinted polymers are often used to improve selectivity (Wolfbeis, 2000; Philips et al, 2003). The incorporation of desired functional monomers into the polymer structure further enhances the selectivity to a given analyte. These polymers are cross-linked and prepared by free radical polymerization processes such as solution or dispersion polymerization with acrylic or vinyl monomers. Polyelectrolyte gels are charged cross-linked three-dimensional networks of monomers that possess high swelling capability due to solvent sorption. The amount of swelling is known to be a string function of pressure, temperature, ion concentrations and pH changes (Siegel, 1993; Siegel et al, 1998; Matsuo and Tanaka, 1988). Their swelling and kinetics depend on parameters such as the degree of cross-linking (Skouri et al, 1995), external salt molarity (Yin et. al, 1992), and the degree of gel ionization rule (Katchalsky and Michaeli, 1995; Yin et al, 1992).
An emulsion or miniemulsion polymerization approach can be utilized to prepare film-forming polymer latexes with desired functional moieties to be used to coat optical fibers. The base latex polymer may be based on acrylic (e.g., n-butyl acrylate, n-butyl methacrylate) or styrene/acrylic film-forming compositions (i.e., with glass transition temperatures (Tg) of room temperature or lower). These latexes are prepared by conventional emulsion polymerization or by a miniemulsion polymerization process in the case where monomers with very low water solubility are used. In the miniemulsion polymerization process, the monomer would be emulsified in the presence of an aqueous surfactant (such as sodium lauryl sulfate) / costabilizer (e.g., hexadecane dissolved in the (co)monomer mixture) combination using a high shear device to form miniemulsion droplets which could then be polymerized in the presence of a free radical such as potassium persulfate.
The polymer would also be crosslinked to varying extents using crosslinking monomers such as ethylene glycol dimethacrylate, divinylbenzene, or bisacrylamide. In addition, functional monomers can be copolymerized along with the base acrylic or styrene/acrylic monomers. One type of monomer is a carboxylic acid such as methacrylic acid (MAA), which copolymerizes well with the base monomers. At high concentrations, this latex could function as an alkali-swellable latex whereby the latex particle size, and coating swellability, would increase dramatically upon neutralization in aqueous solutions of high pH (e.g. > 10) which would trigger a sensor response. N-methylol acrylamide (NMA) may also be incorporated into the base copolymer composition to obtain a crosslinked polymer, which can also act as a hydrogel, which could also swell when exposed to water. In addition, the monomer, N-(isopropylacrylamide) (NIPAM) will also be utilized for forming hydrogel particles which can swell when exposed to water. Incorporating NIPAM into a polymer composition would also lead to the formation of a thermosensitive polymer coating since poly(NIPAM) exhibits a strong phase transition above 32°C. It is also possible to copolymerize a alkoxysilane-containing monomer with the acrylic or styrene-acrylic monomers via miniemulsion polymerization to enhance the compatibility of the polymer coating with the glass optical fiber.
The extent of bonding of the polymer coating to the glass optical fibers is critical. The optical fibers can either be coated with the manufacturer’s cladding removed or in place. Preliminary experiments have shown that it is difficult to coat the uncoated glass fibers. These fibers are brittle without the manufacturer’s cladding in place; the composition of which is unknown. The fiber can be passed through a coagulant bath prior to its immersion in the latex bath. Similar to dip coating, which has been utilized in preliminary coating experiments, the latex will coagulate onto the glass fiber. The surface of the fiber needs to be made hydrophilic for this process. This can be achieved by either physical adsorption of nonionic water-soluble polymer such as poly(vinyl alcohol) (PVOH) or by corona treatment of the fiber surface. In addition, the polymer processing techniques used in wire coating applications can also be applied to the case of the optical fibers. Important coating parameters would include the solids content of the latex (a high solids content is needed to control the rheology of the dispersion to be coated; a reasonable viscosity is needed for effective coating). Latexes can also be made self-thickening by the incorporation of carboxyl groups into the latex particles. A thickener can also be added to a latex composition to adjust the coating viscosity.
In addition, the surface tension of the latex would need to be controlled to give good wetting onto the glass fiber. Contact angle measurements on glass substrates can be used to determine the optimum wetting behavior before moving on to the glass fiber itself. The thickness of the polymer coating would also need to be varied to determine the necessary thickness needed to give a good, measurable response when exposed to solvent or water containing the heavy metal ions. If the coating is not thick enough, the response to the analyte may be too weak. If the coating is not uniform on the fiber, there would be unexposed regions of the fiber which would affect the detection limit and sensitivity. In addition, there needs to be good adhesion of the coating to the fiber, otherwise delamination could occur. Silane adhesion promoters can be explored to enhance adhesion of the polymer coating onto the glass fiber substrate. The drying temperature and drying conditions (e.g., time and temperature that the latex-coated fibers are dried in an oven to ensure good film integrity or the use of forced heated air flow over the fibers) are also critical coating variables to be investigated.
The kinetics of swelling of the polymer coatings when exposed to aqueous or organic media needs to be evaluated by monitoring the changes in the dimensions of the coating or the gravimetric uptake of the media by the polymer. The time-dependent changes can be analyzed to give an idea of the best polymer architecture to obtain an optimum sensor response when exposed to a given chemical. The time constant for the sensor response needs to be determined and correlated with the swelling kinetics of the polymer coating to achieve the best sensor performance.
The development of an optical fiber pH sensor based on hetero-core fiber structure coated with an acrylic polymer doped with Prussian blue is discussed here. In this design, the pH changes of the surrounding medium affects the Prussian blue present in the layer and produce a change in the refractive index of the layer. The pH changes are then observed as an increment in the hetero-core transmission signal.
The hetero-core fibers were constructed using two different length and two different types of optical fiber. In this case two types of single-mode fibers (SMA and SMB) and two of multimode fibers (MMA and MMB) were used. First, two pieces of MM fiber, stripped of its coating polymer (3 cm section) were spliced to a stripped SM fiber on each side. The hetero-core fibers were treated with Prussian Blue 0.1 mM (PB), polyvinyl alcohol(PVOH) at 4 %,acrylic polymer emulsion (APE) at 50 % plus and their combinations, like PVOH + PB and APE + PB to develop a reactive coat over the stripped surfaces. A small U-shape container made of a glass capillary was fixed to a mechanical mount and was filled with the mixture of polymer support and Prussian blue sensitive material. Then the single-mode section of the hetero-core fiber was immersed for 5 minutes into the solution after which the fiber was removed and dried at room temperature. In this manner the sensitive material was adhered to the single-mode section of the hetero-core fiber.
One end of the hetero-core fiber was connected to a white light source Yokogawa AQ4305 and the other to the spectrum analyzer Ando AQ6315A (Figure 14A). The set-up was used to measure the transmission light during the modification process of the fiber and later to measure the response of the modified fiber to pH changes. In order to test the sensitivity of the device to changes in pH, a test was designed which consisted of immersing the optical fiber section modified with PVOH/PB or APE/PB in a Petri dish where the pH was varied by adding 0.1 M NaOH or 0.1 M HCl, recording each transmission spectrum changes in the wavelength range from 350 nm to 1700 nm (Figure 14B).
The transmission spectra of two hetero-core fibers with PVOH/A (5 mm and 10 mm sections) were measured in different pH solutions are shown in Figure 14A and 14B, respectively. As seen in figure 15 the device has good sensitivity (-1.5 dB and -2 dB approximately), however the signal is erratic and not repeatable for different pH changes. This was attributed to solubility of PVOH in acidic conditions, and checked visually and with the transmission spectrum analysis. A new polymer, acrylic polymers emulsion (APE) was selected to replace PVOH. This polymer has similar characteristics as PVOH. It is water soluble, inexpensive, and colorless when dried, and has been reported as a good support in manufacturing of modified electrodes for pH determination.
The transmission spectra of two hetero-core fibers with APE/A (5 mm and 10 mm sections) were measured in different pH solutions are shown in Figure 16A and 16B, respectively. As seen in Figure 16A, the device showed very obvious changes when subjected to acidic and basic conditions. The presence of three peaks in visible region, 400, 500 and 700 nm wavelengths were noted when optical fiber was in air. These signals were attributed to light absorption and loss of light by index refraction changes by the composite material. In acidic pH values the light losses were in the range of 1.5 to 3 dB while for basic pH levels transmission near to 0.5 dB.
In order to identify the origin of signals found, the concentration of Prussian blue was increased to 2 mM. As we can see in Figure 16B, the intensity of transmission peaks at 400, 500 and 700 nm was increased, suggesting that they are due to increased concentration of PB in the composite. It also shows that the device sensitivity increased from 1.5 to 3 dB with 1mM concentration of PB until 4 to 6 dB with 2 mM concentration of PB for acidic pH solutions, but behaved same as previous in basic solutions. Finally there was a good return to initial conditions after each change of interface (Figure 17).
Subsequently we performed a sensitivity analysis for pH changes by taking the APE/PB modified fiber signal in air to use as a normalizing reference. As shown in Figure 18, there is good sensitivity to pH values lower than 7 with gains up to 6 dB at 400 nm (absorption or loss peak), whereas above pH 7 the peak is inverted, turning in a gains peak which may be due to the hydration process of the polymer and breaking of complex of PB by hydration. To identify the changes in transmission spectrums, the most characteristic signals (400, 700 and 800 nm) and the response to 1500 nm (common wavelength in telecommunication systems) were plotted independently.
Analyzing the charts of Figure 18 shows that the transmission intervals are decreasing with increasing wavelength, which demonstrates good sensitivity of the device. As previously mentioned, the pH changes were more evident at pH values less than 7, due to that pH values higher than 7, it promotes the process of hydration of Prussian blue complex (Equation 4) and the signal grows weak on each pH change (García – Jareño et al, 1996).
Based on the results obtained so far, the 10 mm rather than the 5 mm length hetero-core device is recommended since their sensitivity and the evanescent wave field is bigger than 5 mm length devices. Also the 10 mm length device provides a gain of about 2 dB at throughout the analysis spectrum.
Actually, all devices showed in this paper are preparing to their application in real scenarios, with the intention to quantify physicochemical properties directly to polluted soil without extraction from the field and pre-treatment of sample, which could reduce time and costs of analytical determination, increasing the sensibility, detection and quantification limits in comparison with spectroscopic and spectrometric techniques, to take the best professional decision to remediate in the better technical conditions the polluted soil.
For pollution detection and soil remediation purposes it is essential to have relevant and reliable information on the soil structure, the hydrogeological circumstances and accumulation zones of the detected pollutants. Combined application of geological, hydrogeological and geophysical investigations prior the placement of the optical fiber in the field may increase the efficiency of the monitoring technique.
Spatially resolved mapping of chemical constituents is an important need in a variety of environmental and geo-environmental applications. For example, spatially resolved analyte monitoring can simultaneously indicate and locate when an accepted level of exposure to toxic or explosive species has been exceeded, and can track its source.
The capability of long-range distributed sensing is unique to optical – fiber technology. A distributed fiber optic sensor returns a value of a target measurement as a function of the linear position along the fiber length. The only contact between the point to be measured and the observation area is the optical fiber.
The authors would like to thank the Consejo Nacional de Ciencia y Tecnología de los Estados Unidos Mexicanos (CONACyT), L’Oreal, Academia Mexicana de Ciencias (AMC) and Fundación México – Estados Unidos para la Ciencia (FUMEC).J. A. García is grateful to CONACyT for his scholarship.
An Unmanned Aerial Vehicle (UAV) is a type of aircraft that operates without a human pilot on-board. There are different types of UAVs employed for different purposes. Originally, the technology was employed by the military for anti-aircraft target practice, intelligence gathering and surveillance of some enemy territories. The technology has however grown beyond its initial purpose and in recent years has gained prominence in different spheres of human endeavor. Advancements in technology has allowed for the increased adaptation of unmanned aerial vehicles for various purposes. Without an on-board pilot, UAVs are controlled either remotely by a pilot at a ground station or autonomously, steered by a pre-programmed flight plan.\n
There is a huge potential for the application of UAVs in Agriculture. One such application is in accurate and evidence-based forecasting of farm produce using spatial data collected by the UAV. UAVs also allow farmers to observe their fields from the sky. This sky-view can reveal many issues on the farm, common among which is irrigation related problems, soil variations, fungal and pest infestations. Further information relating to water access, changing climate, wind, soil quality, the presence of weeds and insects, variable growing seasons, and more can all be monitored with UAVs. From a livestock perspective, UAVs are being used to perform head counts, monitoring animals and also studying eating habits and health related patterns. Utilizing the information gathered, farmers can provide fast and efficient solutions to detected problems and issues, make better management decisions, improve farm productivity, and ultimately generate higher profit. In this chapter, various applications of UAVs in Agriculture are discussed both in commercial livestock farming and crop farming. This chapter also presents some of the open challenges to the application of UAVs in Agriculture.\n
Immediately following this introduction is a discussion of the various types of UAVs which is done in Section 2. This is followed by the applications of UAVs in crop farming and in livestock in sections 3 and 4 respectively. Advantages of UAVs and corresponding challenges are discussed in Section 5, the chapter ends with the 6th and concluding section.\n
UAVs can be classified based on usage, with some being used for photography, aerial mapping, surveillance, cinematography etc. However, a better classification can be made based on their feature sets. Vroegindeweij, et al. in their paper , presented an overview of the different types of UAVs applied in Agriculture and categorized them into three main groups – fixed-wing, Vertical Take Off and Landing (VTOL) and bird/insect. The authors identified the VTOL with its agility, great maneuverability and hovering ability as best suited for Agricultural application. In , the authors however argued in favor of the fixed-wing UAVs, stating that their long flight time and speed makes them better suited in comparison to the VTOL, which have comparatively shorter flight time and slow speed. In other works, authors have argued in favor of unmanned helicopters such as the monocopter or single-rotor UAV [3, 4]. These types of UAVs have long flight time, can fly at different altitudes and have good hovering abilities. However, they are much more complex to fly. A comprehensive survey of various UAVs was also done in . From these literatures, four major types of UAVs are identified, which are:
Fixed-wing-multi-rotor Hybrid UAVs
These are the most common type of UAV, evident by their wide popularity among professionals and hobbyists alike. They find applications in photography, aerial video surveillance, recreational sports and games etc. They are the easiest to manufacture and also the cheapest type of UAV. Multi-rotor UAVs are further classified based on the number of rotors on the platform. There are those with three rotors called tricopter, with four rotors called quadcopter, with six rotors called hexacopters and those with eight rotors called octocopter. Flying a multi-rotor UAV does not require exceptional skill unlike the other types of UAVs.\n
Multi-rotor UAVs though cheap and easy to manufacture have a few drawbacks which include: limited flying time, endurance and speed. They can only sustain an average flying time of between 20 and 30 minutes. This is because a large percentage of their energy is expended fighting gravity and wind to remain stable in the air. Figure 1 shows an octocopter used for precision spraying of liquid pesticides and herbicides.\n\n
These types of UAVs have wings similar to normal aircrafts. Unlike the Multi-Rotor UAVs, they do not exert a lot of energy to stay afloat in the air, hence able to fly longer; having average flight times of over an hour. Longer flight time makes them most ideal for long distance operations. However, they cannot hover on a spot and are thus not suitable for aerial photography. Furthermore, they are more expensive and require exceptional flying skill to operate. Figure 2 shows a sample fixed wing UAV used for capturing images across large acres of farmland.\n\n
Single rotor UAVs are also called monocopters and look very much like helicopters in design and structure. Though they are called single rotor UAVs, they actually have two rotors - a large on top and a smaller one at the tail. The bigger rotor is for lift while the smaller is used for control. They have significantly longer flying time than their multi-rotor counterpart, as they are often powered by gas engines. These UAVs are also highly maneuverable and much more efficient than the multi-rotor types. Similar to the multi-rotor, they are also able to hover, hence useful for aerial photography and precision spraying. Despite these beneficial attributes, they come with higher operational risks as the large sized rotor blades usually pose a risk which is mostly fatal in nature. Like the fixed wing UAVs, these also require special flying training. Figure 3 shows a sample single-rotor UAV.\n\n
These types of UAV combine features of the fixed-wing and the multi-rotor UAVs, with the hybridization gives these UAVs a best-of-both-worlds feature set. They are able to perform vertical take-off and land (VTOL) as well as hovering in place like the multi-rotor and single-rotor. Similar to the fixed-wing and single-rotor UAVs, these also benefits from long flight-time, but can stay in flight for much longer. Figure 4 shows an image of one such UAV that is versatile enough to be used for image capturing, surveillance as well as precision spraying.\n\n
Though these are the four common types of UAVs, there is a unique type of UAV called the Flexible Membrane Wing (FMW) UAV . The FMW has wings made from flexible membrane material, with the advantages of this being easy of storage (as the wings can simply be folded up) and better control and maneuverability in windy conditions (as the flexible wings dynamically adjust to cater for wind preventing “adaptive washout”). The FMW is a niche UAV, targeted flying in harsh and windy conditions. Flexible membrane also implies lighter weight and by extension the possibility of carrying larger payloads.\n
\nFigure 5 shows a comparison of the four different types of UAVs based on their average weights, payload size and flight time; Table 1 on the other hand summarizes their in-flight specifications. For each category, a model UAV was selected. The values shown were obtained from the respective manufacturer documentation and/or operator’s manual of each product. On Table 1, the advantage of the hybridization can clearly be seen, as it resulted in higher flying altitude, wider control range, increased speed and longer flight time compared to the other UAV types.\n\n
|UAV type\n||Altitude (km)\n||Avg. control range (km)\n||Avg. airspeed (m/s)\n|
|Multi-rotor UAVs (DJI Agras MG-1P )\n||2\n||3–5\n||7\n|
|Fixed-wing UAVs (AgEagle RX60 )\n||0.125\n||2\n||18.8\n|
|Single-rotor (Alpha 800 )\n||3\n||30\n||15.2\n|
|Fixed-wing-multi-rotor hybrid UAVs (Jump 20 )\n||4\n||500–1000\n||30\n|
According to Massachusetts Institute of Technology (MIT), UAV technology will give the Agriculture industry a high-technology makeover, with planning and strategy based on real-time data gathering and processing. PwC put a $32.4 billion valuation on the UAV-powered Agriculture solutions market . The application of UAV technology in Agriculture has become increasingly necessary with the increase in global population and the resultant pressure on agricultural consumption. The ever growing international population is not proportionately matched with crop growth; hence, there is a growing concern about food sustainability. In a bid to tackle this challenge, farmers around the globe have had to adapt modern and automated solutions in order to keep up with the agricultural needs of the world population that is in constant flux. UAVs are one such technology that could help improve crop yield. A number of UAV application areas are presented in the following subsections.\n
The use of UAVs for soil information sourcing is helpful at the early start of a crop cycle. The data collected helps in early soil analysis, and is also useful in planning seed planting patterns. These data can also assists the farmer in making irrigation plans as well as determining the quantity of fertilizer needed on the soil or field after planting. Using a data-driven approach, the farmers can improve the overall yield quantity of agricultural produce, while significantly saving on fertilizers and pesticides. All these are made possible through the analysis of remote images captured with UAV. UAV imagery also has a huge potential in designing site-specific weed control treatments. With the high resolution images, farmers can quickly and precisely spot weeds almost immediately they spring up and apply minimal pesticide to contain them. The authors in  developed an Object-Based Image Analysis (OBIA) on a series of UAV images using six-band multi-spectral cameras on a maize field in Spain. While in  the technical specifications and configuration of a UAV which could be used to capture remote images for Early Season Site-Specific Weed Management (ESSWM) were given. The study also evaluated the image spatial and spectral properties necessary for weed seedling discrimination. They deployed an UAV equipped with multi spectral cameras and analyzed the technical specifications and configuration of the UAV to generate images at different attitudes; with the high spectral resolution required for the detection and location of weed seedlings in a sunflower field. The result of the study can be of help in the selection of an adequate sensor and configuration of the flight mission for ESSWM.\n
Planting crops is a costly and cumbersome endeavor that has traditionally requires a lot of manpower. UAVs have simplified crop planting for farmers, with their abilities to cover large acres of land within a short period with utmost precision and accuracy. Today’s high-end UAV farming technology offers UAV-powered planting techniques that reduce planting costs by up to 85%. The reduction in planting costs is a result of the UAV’s capability of performing multiple tasks at the same time.\n
UAVs have become increasingly popular in recent years in agricultural research applications. They have been found to have capabilities of acquiring images with high spatial and temporal resolutions in Agriculture. Reference  evaluated the performance of a UAV-based remote sensing system for quantification of crop growth parameters of six sorghum hybrids. Factors such as Leaf Area Index (LAI), fractional vegetation (fc) and yields were considered. The evaluation was carried out using a fixed-wing UAV, equipped with a multi spectral sensor to collect images during the 2016 growing season with flight missions carried out 50 days after planting. The flight missions provided data covering the different growth periods of the sorghum hybrids. The authors inferred that high resolution images acquired using UAV can be effectively utilized for in-season data collection from the field. The results obtained proved the relationship between Normalized Difference Vegetation Index (NDVI) and LAI, and between NDVI and fc. It was thus possible to determine/estimate LAI and fc from UAV derived NDVI values. It was shown also that imagery taken at flowing stage could be better indicator of yield, rather than NDVI obtained at earlier growth stage of sorghum crop. Furthermore, it was also established that early season NDVI measurement is useful index for estimating plant population density of sorghum.\n
The authors in  sought to develop a novel method to quantify the distance between maize plants at field scale using an UAV. The distance between roots and plants are essential in determining the final grain yield in row cops. An UAV-based image algorithm was developed to calculate maize plant distances. Knowledge of the exact number of plants per square meter is essential and helps to improve yields by deducing the fertilizer and pesticide application to match plant demand. Determining plant population is essential for several other processes such as soil-to-plant balance, nutrient recycling and resource use efficiency. The study demonstrated the possibility of quantifying the distance between maize plants and provided an innovative approach to quantify plant-to-plant variability and by extension crop yield estimates.\n
Crop spraying is usually a tough and onerous task for farmers and agricultural production companies. It involves covering extremely large expanses of land comprehensively to ensure proper growth of crops. Agricultural UAVs have simplified crop spraying for farmers; as they can cover large expanse of land within a very short time interval. Using sensors, UAVs can automatically adjust their height when spraying across uneven fields. This improves the spraying accuracy and conserves resources. The advantages of using UAVs for crop spraying include: time and cost savings for the farmer, efficient spraying as both the plants and the soil below can be reached, and protecting farmers from prolonged exposure to potentially harmful chemicals that are hitherto associated with manual spraying. Agricultural UAVs utilize state-of-the-art topographical scanning techniques to dispense the optimal amount of fluid required for proper crop growth. This ensures even coverage with limited wastage. Lv et al.  demonstrated the practicability of infrared thermal imaging in evaluating the droplet deposition in the field of aerial spraying. In the study, the effect of UAV flight speed on the spray droplets was investigated and the variable spray test was conducted by a UAV simulation platform, with airborne spray system under controllable environment. Several conclusions were drawn from the study among which were that deposition density decreases with the flight speed and droplet diameter (i.e. the distribution uniformity of particle size) decreases with an increased flight speed resulting in the worse uniformity of the sprayed droplets. The authors therefore provided a theoretical support for optimizing the spraying parameters of plant protection UAV, aimed at improving plant yield.\n
Spot spraying is similar to crop spraying but targets weeds. With the use of high resolution cameras, the UAV can identify weeds and precisely spray a jet of herbicide. Spot spraying can save up to 90% on chemical herbicides. Numerous research works [17, 18, 19] have been done in determining the efficacy of UAVs for spot spraying. Some factors considered were balancing UAV altitude and speed with spraying height and accuracy as well as droplet sizes, spray pressure and the possible effects of the UAVs’ propeller(s) airflow direction.\n
A combination of large farm fields and low efficiency in crop monitoring system are some of the greatest farming challenges. The challenge of monitoring is further aggravated by unpredictable weather conditions, which drive up risk and field maintenance costs. An agricultural UAV helps the farmer overcome some of these challenges. UAVs with thermal imaging cameras enable the farmer to monitor his farm. The farmer can check the state of crops in the farm, as well as areas that need urgent attention. The result is improved yield and greater profit.  demonstrated the possibility of generating quantitative mapping products such as crop stress maps from UAV images and highlighted the value of UAV remote sensory when applied in precision Agriculture. The study applied a single-rotor UAV (monocopter), equipped with multiple spectral cameras, and then developed a framework to process the UAV images and generate mosaic images which can be aligned with maps for GIS integration at a later stage.\n
Agricultural UAVs fitted with thermal imaging cameras have the capability to providing tremendous insights into specific troubled areas in the farm. Using the thermal cameras, the farmers are able to determine areas with low soil moisture, pinpoint crops that are dehydrated, locate areas that are water-logged and in general have a sense of the overall health status of crops in the field. Such precise and specific monitoring were either not possible with traditional farming, inefficiently done or extremely expensive as experts have to be contracted to carry out the task and proffer adequate solutions. UAVs now give the farmers that ability to do these themselves. In , the authors carried out a study on vineyard water status variability by thermal and multispectral imagery using an UAV. Assessment of the water status variability of a commercial rain-fed Tempranillo vineyard was done, and concluded that an UAV can be used to assess vine water status, and to map within vineyard variability which could be useful for irrigation practices. The work done in  focused on the application of thermal remote sensory in precision Agriculture, and some of the concerns relating to its application. Gonzalez-Dugo et al.  further dealt with the assessment of heterogeneity in water status in a commercial orchard as a prerequisite for precision irrigation margent. High resolution airborne thermal imagery was employed. A UAV with thermal camera on board was flown three times during the day over a commercial orchard; and the indicators derived from the thermal imagery described the spatial variability in crop water status and thus allows the mapping of an orchard on a tree by tree basis. It therefore becomes a valuable tool for water management in precision Agriculture.\n
Farm health assessment is crucial for detecting fungal and bacterial diseases on the farm. By scanning a crop using both visible and near-infrared light, UAV-carried devices can detect temporal and spatial reflectance variations and associate it to the farms health for early interventions, which ultimately saves the entire farm. These two possibilities increase a plant’s ability to overcome disease. And in the case of crop failure, the farmer will be able to document losses more efficiently for insurance claims. UAVs offer new and modern methods of accurately monitoring and assessing pest damage needs to be investigated. The authors in  explored the combination of UAVs, remote sensory and machine learning techniques as a promising technology to address the problem of agricultural pests in farmlands. UAV platform was deployed over a sorghum crop in South-East Queensland, Australia, to collect high resolution RGB images of certain areas which were severely damaged by white grub pest. An image processing pipeline was implemented prior to image analysis. The study demonstrates how UAV-based remote sensitivity and machine learning could be used to achieve biosecurity surveillance and pest management. The work presented in  also corroborated the use of UAV in crop health assessment, and outlined the benefits of deploying UAV remote sensing over the traditional methods. They developed a method that can quickly monitor crop pest, based on UAV remote sensing, which was deployed for inspection pests in Baiyangdian agricultural zone during the growth season. An improved SIFT Algorithm was adapted for image matching and mosaic with good result. The method adopted by  was used to check the status information of crop pest. Similarly, in the work done by Yinka-Banjo et al. , the authors proposed the use of UAVs for bird control in farmlands. Their solution combined the use of autonomous vehicles with bird scare tactics. The combination was reported to be more efficient than the traditional human-based manual approaches.\n
Livestock farming is the act of rearing animals for food and/or other uses such as medicine, leather, fur and fertilizer. The authors in [27, 28] showed that traditionally Livestock Production Systems (LPS) were grouped into three major classes, namely: livestock production integrated with crop, land based and agro-ecological. They further sub-divided LPS into 11 groups – solely livestock production, temperate and tropical highlands grassland-based, arid and subtropics grassland, humid and subtropical mixed-farming based, temperate and tropical highlands rain-fed mixed farming, humid and subtropics rain-fed mixed, temperate and highlands irrigated mixed farming, humid and subtropics irrigated mixed farming, arid and subtropics irrigated mixed farming, landless monogastrics and landless ruminant farming. Similarly, in , the authors reviewed five (5) types of livestock production systems in tropical areas based on factors such as agro-ecological zones, animal type, function and management. The identified classes were Pastoral Range, Crop-livestock (low and highlands), Ranching and landless.\n
|Animal type and/or produce\n||Number/quantity (10^6)\n|
|Sheep and goats\n||1777\n|
|Cattle and buffaloes\n||1526\n|
Livestock farming as with other aspects of Agriculture can be monotonous and laborious. Humans are however not well suited to such task over a prolonged period of time. Machines therefore can find practically applications in this arm of Agriculture, as they are designed to perform repeatable tasks, faster and possibly more efficiently (over a long period of time) than humans can. UAVs are therefore no exceptions and have found practical applications in livestock farming. Applications of UAVs in livestock farming are discussed as follows:\n
To further put Table 2 in perspective, according to the National Development Agency of South Africa, there were over 13 million units of cattle, 30 million sheep and 6.6 million goats and 1.6 million pigs bred in each province annually between up on to 2003. The figures are even significantly higher in European countries according to Eurostat. These are staggering numbers, hence monitoring and daily head counts of these large number of animals can be challenging. UAVs can thus find application here and be used to perform headcounts of livestock across these large grazing areas [31, 32, 33]. Animal counting can be done either by using image recognition  or using heat detecting infra-red cameras . For image processing, Convolutional Neural Network (CNN) has emerged in recent times as the most widely used . In large grazing areas, the UAVs can also be used to detect and count the number of animals present. In most of these works, the UAVs fly across the field, and counting the number of animals present. In the work done by  however, the authors proposed an approach, wherein the number of goats are counted and tracked using fewer numbers of pictures, sometimes only one. The authors reported 73% count accuracy and 78% tracking accuracy.\n
In contrast, in their book , the authors reviewed numerous methods of performing thermal imaging for monitoring animals in the wild. Among many other factors, the authors argued that thermal imagining is not dependent on time of the day unlike image processing. This therefore provides a unique opportunity to observe animals in their natural habitats without causing disturbances – which can lead to dispersion and possibly double or inaccurate counts.\n
Beyond counting, research work is underway at the Texas A&M University, to investigate the use of infra-red cameras mounted on UAVs to monitor the health of animals. The research is based on the premise that, animals with fever tend to have heightened temperature. This can easily be detected by the UAV and appropriate medication can be administered . Similar research targeted at monitoring health has also been carried out in . Figure 6 shows a sample heat map of a herd of cattle captured by a UAV.\n\n
On an individual levels, animals can be tagged with RFIDs or similar sensors and can then be monitored using UAVs. With this, farmers can effectively monitor the movements and feeding behavior of a specific animal . This has also been extensively used in monitoring endangered animals, raised in captivity and released into the wild. Similar to the two application areas discussed above, the identification can be carried out using UAVs fitted with normal cameras or IR cameras (which detect heat emissions from the animal) or RFID readers.\n
A major challenge to the application of RFID is that passive RFID tags have very short range, hence might be difficult to use. Potential solutions might include:
Painting QR codes on cattle, which the cameras fitted on the drone, can simply scan in order to identify the animal.
The use of a relay drone, such as the RFly being researched at MIT . The RFly acts as a relay between the RFID tags and the reader. Using RFly, the authors recorded up to 50 meter range extension for passive RFID tags.
These technologies can be borrowed and used for animal identification and monitoring in Agriculture.\n
\nFigure 7 shows a potential use case of UAVs and RFID tags in animal identification.\n\n
Mustering is the process of using aircraft to locate and gather animals across a large span of land. Dogs (sheep dogs) and human on horses (cow boys) or motorcycles have traditionally been used to direct livestock along specific paths. For larger expanse of land, small sized helicopters are used. These helicopters are often piloted by one person and have highly maneuverable and agile. The challenges with the use of helicopters are the need for extensive training, the cost of licenses and certifications, the cost of fuel and most especially the high level of risk exposure and casualties associated with it.\n
UAVs provide a unique opportunity for aerial mustering as they are comparatively risk free, cheaper to fly and require shorter training period, yet able to achieve similar results. UAVs have successfully been used in Australia and New Zealand to muster sheep and cattle . According to , aerial mustering UAVs are fitted with sirens to herd sheep, deer and cattle. The UAVs can also be used to guide the animals to feeding, drinking and milking areas. Numerous case studies of the application of the DJI Phantom in Agriculture are given in . Figure 8 shows a use case of UAVs for sheep mustering.\n\n
Geo-fencing, virtual perimeter or geo-zoning simply means creating a virtual barrier or perimeter around a geographical area of interest [42, 43]. It has also been defined as an enclosure, or a boundary without the use of physical barriers. It can be accomplished by using a combination of RFID, LoRaWAN and GPS based location sensors for instance. Sensors obtain the location of the subject of interest relative to a map. Geo-fencing has been used in numerous fields such as fleet management and logistics – to monitor movement of vehicles, proximity marketing – which prompt users of products when they are close enough, asset management – which send alerts when an asset is moved without authorization, people monitoring – such as in monitoring movement of children and employees and in law enforcement – to restrict and/or monitor persons of interest.\n
Geo-fencing has also seen immense application in Agriculture, more specifically in free-range livestock farming. Sensors are placed on collars of cattle, goats etc. and these send location data to the farmer. There are two major forms of application of geo-fencing in agriculture: in the first, the sensors simply notify the farm owner when animals have grazed outside a pre-defined perimeter . In this system, the farmer has to actively go muster the animals back into the perimeter. In the second approach, the animals are given subtle stimulations when they wonder outside set perimeter. Such simulations might include high frequency sounds or low voltage jolts – this approach depends on associative learning . An illustration of a geo (virtual)-fence is shown in Figure 9, with the red boundary showing the grazing area and the blue circle showing an animal grazing outside the boundary.\n\n
Recent research work has focused on improving the efficiency of geo-fencing technologies. Low cost GPS being the most commonly used geo-fencing sensors have an error range of between 5 and 10 m and sometimes take long to locate and lock on to the required number of satellites. In a bid to improve on them, Assisted-GPS and WiFi have been used to respectively improve accuracies and reduce the time-to-first-fix . LoRaWAN has recently been introduced as an alternative protocol for accurate location of animals [44, 46].\n
Though some arguments have been raised with respect to the effectiveness of geo-fencing, such as in the work of , rather than purely depending on stimuli, UAVs can be used to steer the animals back into range when they roam out of grazing perimeters. UAVs can therefore provide a cheap and effective way of getting animals back “inline” and are particularly useful when a number of animals stray outside different ends of the perimeter.\n
Limited Constraints: Being air borne they are not hindered by physical constraints such as road/soil terrain, uneven paths and obstacles. They can simply fly over them all.
Shorter travel path: It is well known that the shortest distance between two points is a straight path. UAVs are best suited for this, as they can fly directly in straight paths. This is not always the case with land based vehicles.
Flying dark: In the case of autonomous UAVs, the UAVs can be programmed to fly in pitch darkness or at times with near zero visibility when it would be difficult for humans to manually control them.
Time and labor savings: Activities such as head count, monitoring and mustering often require the employment of more hands to help out. These can be both labor intensive and time consuming. With the use of UAVs, the number of extra laborers required is significantly cut down, while simultaneously saving time. Similarly in crop farming, UAVs can spray crops about 40–60 times faster than human laborers can.
Cost: Beyond savings in time, cutting down on laborers directly translates to cost savings. Though, capable UAVs are not cheap and there is also the added cost incurred in form of electricity to recharge the batteries; the cost savings and advantages of UAVs still significantly outweighs the manual and labor intensive processes of traditional/crude agriculture.
Aerial photography and imaging: With the use of UAVs, farmers can quickly obtain aerial images of their entire farm or select areas of interest. This can be useful in determining when fruits start to sprout or when pests and weeds are choking out crops.
UAVs have seen a wide range of applications in a smart city, all of which contributes greatly to the development of any smart city. In  the authors pointed out some of the challenges associated with the use of UAVs. Though these works focused on smart city applications, a number of these challenges are also applicable to the Agricultural space. The challenges were broadly classified into business and technical, and include:
Cost: The technology is perceived as expensive as a result of the technical nature of UAVs. Deployment, integration and training can be very expensive . Similarly, in , the authors took a project management perspective to deployment of UAV related projects and highlighted cost as a key element that needs to be considered. It was also noted that proper estimations need to be using various technique prior to undertaking any such project.
Licensing and regulation issues: This is still a gray area with respect to UAVs. Regulations are either none-existent or a loose adaptation of aviation laws, which do not perfectly fit in with UAVs. There is therefore the need to draw up legislation to regulate the new possibilities and application areas of UAVs. Countries such as the USA, the UK, Germany and Spain  are leading the way in this direction by drafting guidelines for the use of UAVs and areas over which they can be flown. Other countries of the world are however still some way behind.
Business Adoption: From a business perspective, it might not be out rightly easy to justify the adaptation of UAVs into Agriculture. Though one might argue that there might be cost savings in the long run, counter arguments can be put forward regarding the actual acquisition cost of the UAVs, insurance / replacement of crashed UAVs, purchase of high resolution cameras for imagery as well as the accompanying software solutions and other running costs. When all these are added, it makes it a hard case to sell to farmers and Agriculture business owners.
Technical Challenges: These come in the form of system integration - integration of the middleware services with the UAV, high performance systems for data analytics, Net-centric infrastructure which enable any member of a team to control the UAV and retrieve imagery and sensor information in real time and application of machine learning / computation intelligence to identify and retrieve useful insights from the large pool of data.
Ethics and privacy: Some feel that the use of UAVs for monitoring and surveillance would lead to the invasion of their privacy. A lack of standard operational and technological procedures needed for safe performance of the UAVs is a great challenge. There could be GPS-jamming and hacking because of the vulnerabilities in the command and control of UAV operations.
Limited flight time: UAV flight time is largely dependent on battery capacity. In most UAVs, particularly the multi-rotor, batteries can often times only sustain a flight time of between 10 and 30 minutes, and can be less when flown during high wind speeds. For activities such as crop spraying UAVs are only effective on hills, small areas, and in areas where other equipment cannot easily reach, for longer distance/range they are less efficient and even more costly than larger ground-based crop spraying equipment. The same challenge can be seen in the area of NDVI imaging, where farmers obtain NDVI images to assess the plant health. Alternative solutions are airplanes and satellites. While UAVs are the most cost-effective for small areas, they are currently not competitive against planes and satellites for larger areas.
There is the need to improve battery technology and find a way of using batteries with bigger capacity yet small footprint in UAVs. The use of solar photo-voltaic cells to power UAVs, such as  or the hybrid fixed-wing might be promising direction to be explored.
Limited payload size: Due to the small size of most UAVs, they are unable to carry a lot at once. This therefore limits their applications to basic aerial photography and observation. Though there are large-size UAVs such as , these are still limited in terms of flight time which is even further shortened when they are fully loaded. This limitation is prominent in application of UAVs in crop dusting (spraying pest/weed controlling chemicals or fertilizer on crops). Large gas powered monocopters might be a potential solution to this challenge.
Autonomy of UAVs: The possibilities of UAVs in Agriculture are numerous. However, most are currently being manually operated by humans. This limits their applications to certain times of the day when there is clear visibility. Advancements in computational intelligence specifically in areas of navigation, obstacle avoidance automatic sensing and actuation (performing pre-programmed tasks) can further accelerate the acceptance and usage of UAVs in livestock Agriculture.
Data Processing: Recent research works have shown the importance of data and information in almost all areas of human endeavor. Agriculture is certainly no exception. The use of UAVs as data gathering tools is still very much in its infancy. There is the need to develop effective techniques for data acquisition, data muling and most importantly converting these data to useful information. For instance, by observing the movement and body temperature of cows, farmers might be able to detect possible health related issues early on before they become fatal.
Empowering Farmers: In an article titled “on Drone technology as a tool for improving agricultural productivity”, in  the authors identified empowering farmers as a vital process in improving agriculture. They concluded that it’s one thing to have the technology and have the ability to gather billions of data for analysis, however all these are of no use, if they cannot be properly integrated and applied into agricultural business processes; where it can bring the much needed improvement. This can only be done by empowering the farmers themselves – either through formal class room education or informal practical demonstrations.
Cost: The ideal UAV for agriculture applications is one that has a good balance of durability, long flight time, stability and optional ability to fly autonomously. Such a device would cost much more than an average farmer might be able to afford. Most especially for farmers in developing countries. For those in much developed countries, there might also be the challenge of justifying how the purchase of such expensive devices can directly translate to measureable profit. To this end Farmers are still largely depend on manual ways of carrying out their farm operations.
Safety: There are also safety concerns with the use of UAV in Agriculture. One such is the UAVs’ inability to recognize and avoid other airborne aircrafts and objects within the same airspace. This could result in collisions. Though obstacle avoidance is not too far-fetched, incorporating such features into basic UAVs would further drive up the already expensive cost of the UAVs.
Availability: There is also the problem of manufacturing, and meeting up with the demands for UAVs by farmers. This is largely expected since the industry is still exploring and testing Agricultural use cases. Manufacturing is being done on a small scale and the fixed costs remain high. In , it was pointed out that despite the numerous potential advantages of thermal remote sensing has over the optical RS in crop and soil monitory, there are a number of practical difficulties in its use. These include but not limited to atmospheric attenuation and absorption, calibration, climate conditions, crop growth stages as well as complex soil and plant interaction that have thus far limited its use in the agricultural sector.
Unmanned Aerial Vehicles or UAVs are essentially flying robots. Though initially designed for military use, they have are now widely used in various areas, from recreational sports, fire-fighting, flight simulations / trainings to toys for children. In this chapter we presented an application of UAVs to commercial Agriculture. We presented four major types of UAVs, and though the multi-rotor UAV with its ability to hover on spot and take-off and landing vertical may seem well suited for agriculture, its limited flight time is a major limitation. The hybrid-fixed-wing-motor-rotor might be a better fit. A detailed insight into the applications of UAVs in crop production and livestock farming was also presented. A prominent requirement for most UAV application in Agriculture is an integrated camera, as it allows images to be taken. Images are used in weed identification and control, soil analysis, animal monitoring, animal head counts, geo-fencing, mustering among others. Like most machines, UAVs have the advantage of doing repetitive and monotonous works better and more efficiently when compared to humans. Some advantages of applying UAVs in Agriculture were presented, some of which include limited path constraints, time saving and reduction in manual labor. However, there are a number of challenges limiting UAVs, most prominent among which is cost. UAVs that are well suited for Agriculture use are expensive. Operation and maintenance also come at a cost. It is therefore often difficult to convince farmers and Agriculture related stakeholders to integrate UAVs into their business. Beyond cost, battery limitations, safety and legal related issues are still major hurdles that need to be scaled before UAVs can find a strong foothold in agriculture.\n
convolutional neural network
Mearly season site-specific weed management
geographic information system
global positioning system
leaf area index
long range wide area network
livestock production systems
normalized difference vegetation index
object-based image analysis
codequick response code
radio frequency identification
scale-invariant feature transformation
unmanned aerial vehicle
vertical take off and landing