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Use of remote sensing to monitor and measure the the health and productivity of agricultural practices.

Precision agriculture

Remote sensing and its applications in agriculture

Precision crop protection

Precision agriculture for measuring the crop productivity

Precision Agriculture

Agricultural production systems are facing difficulties due to the variation in the topography and the climate of the different regions. For the purpose of sustainable management of agricultural all the factors are needed to be analyzed and on a spatiotemporal basis. Advanced techniques like the geographical information system, global positioning system (GPS) and the remote sensing are used for their effective management and assessment. These technologies have the multifaceted benefits and utilities like yield prediction, crop acreage estimation, precise agriculture/site-specific management, computation crop evapotranspiration, soil moisture estimation, crop inventory, stress detection, crop growth detection, and crop discrimination (Hunt et al., 2014). These data provide the reliable information and timely information that are beneficial both for the policymakers and the farmers. Such information on a regional basis is provided through the GIS techniques and remote sensing. Both the GIS and the remote sensing are used effectively for the analysis of land cover and its use. Remote sensing can be described as a cheap alternative that provides a large amount of data over a large geographical area. In remote sensing, the basic concept that is used for the data acquisition is the through the remote sensing and this includes the measuring the characteristics of spectral reflectance from the various surface areas. The invention of both the hyperspectral and the multispectral remote sensing technology has broadened its application in the different fields and areas (Kingra, Majumder & Singh 2016). This study is based on the usage of the remote sensing for measuring the productivity and the health of the agricultural practices which is also called precision agriculture.  

Precision agriculture was developed during the middle of 1980s. The application of the remote sensing in the field of precision agriculture initially started with the sensors for the soil organic matter. This, however, later diversified into tractor mounted sensors or handheld sensors, aerial sensors and satellite sensors. Initially, the wavelengths of the electromagnetic radiation focused on the near or visible infrared regions. However, nowadays the electronic magnetic radiation include the wavelengths that range from the microwave to the ultraviolet spectrum. Thus, enabling the usage of the advanced applications of the thermal spectroscopy, fluorescence spectroscopy and light detection and ranging (LiDAR), and this also includes the traditional applications of the near infrared and the visible spectrum (Wei et al., 2012). With the advent of the hyperspectral spectroscopy, the spectral bandwidth has decreased dramatically and this allows the improved analysis of the crop biochemical and the biophysical characteristics, crop stress, molecular interactions and the improved analysis of some of the compounds. Currently, rather than the normalized difference vegetation indices, the spectral indices exist for the different types of applications in the precision agriculture. The satellite remote sensing and its spatial resolution along with the aerial imagery have now improved from 100 of meters to just a sub-meter accuracy. Thus, this allows the evaluation of the crop and the soil properties at the finest spatial resolution that only utilizes an extra amount of storage and the other processing essentials. Temporal frequency has also developed to great extent presently. Presently there is a significant amount of interest in collecting the data of remote sensing at various intervals for conducting pest management, crop and real-time soil management (Barnhart & Crosby, 2013).

Remote Sensing and its Applications in Agriculture

Precision agriculture involves the collection of data, analysis of the data and the information management. This also includes the sensor design, remote sensing, yield monitoring, and field positioning and technological advances in the field of computer processing. It has been found that the more than the 30 percent of the agribusiness in Agriculture came from the precision agriculture adoption by the farmers (Santesteban et al., 2013).

Remote sensing application in agriculture is entirely dependent on the interaction between electromagnetic radiation with the plant and soil material. This includes measurement of the reflected radiation instead of absorbed and transmitted radiation. It utilizes the non-contact measurements of the emitted and the reflected from the agricultural fields. In addition, it is important to mention that apart from absorption, transmittance and reflectance that the plant leaves emit the energy via the thermal emission or fluorescence. Thermal remote sensing is used to measuring the water stress of the plants is entirely based on radiation of the emission with respect to the temperature of the canopy and the leaf that varies with the rate of evapotranspiration and air temperature (Peng & Gitelson 2012). The amount of that is absorbed by the plant's pigments is inversely related to the radiation that is absorbed by the plants and this radiation varies with the incident radiation wavelength. Chlorophyll is a plant pigment and it absorbs the radiation strongly in the range of the visible spectrum of 400- 700 nm. For the chlorophyll a, the wavelength is 430 nm (blue) and for chlorophyll b is 660 nm. The other plant pigments like the carotenoids and the anthocyanin are also vital for measurement (Chen et al., 2013).

In the region of near infrared (700 to 1300 nm), the plant reflectance is high and due to this the canopy structure and the leaf density effects of the data and the measurements. The sharp differences in the near infrared and the reflectance in the red spectrum has led to the development of the spectral lines that are based on the reflectance value ratios in the near infrared and the visible regions. These specific spectral lines are used for the purpose of assessing the various attributes of the N content, chlorophyll content, biomass, leaf area index, plant canopies (Schlemmer et al., 2013).

In the field of agriculture, the applications of remote sensing are classified on the platform type used for the sensor. The sensor is used on the ground-based platforms, aerial and satellite platform. The imaging systems and their platforms are differentiated based on the minimum return frequency in sequential imaging, the image spatial resolution and the altitude of the platform. The spatial resolution of an image influences the smallest pixel area and the area for analysis (Zhang, Walters & Kovacs, 2014). The decrease in area of the smallest pixel when the spatial resolution increases and this result in the homogeneity of the crop or the soil characteristics within specific pixel increases. When the spatial resolution is poor, the spatial resolution accommodates larger pixels and this results in heterogeneity within the plant characteristics and the soil characteristics. Return frequencies are vital for the assessment of the temporal patterns in the plant and the soil characteristics. However, one of the major barriers is the remote sensing data from the satellite and the aerial platforms are delimited by the cloud cover. Whereas, the remote sensing that is ground-based are not affected by these limitations (Maes & Steppe, 2012).

Precision Crop Protection

The applications of the remote sensing within the purview of agriculture, includes the measuring of the soil properties like the pH, clay content, moisture, organic matter, plant disease, infestations of weeds, water stress and crop nutrient, biomass and crop yield. The conventional agriculture also utilizes remote sensing that has led to the utilization of the same in the precision agriculture. As the spectral resolution and satellite imagery advanced, the reflectance data became increasingly suitable to be used (Atzberger, 2013).

Thermography

Thermography is one of the techniques that allow the surface imaging temperatures of the crop canopies, plants and leaves. The infrared radiation emitted by the thermal infrared region from the 8 to 12 micrometre can be detected via the thermographic cameras and it is illustrated as the false colour images. The image pixel is related to the value of the temperature that the objects measured. The performance of the thermographic cameras can be measured by the scan speed, image resolution and thermal sensitivity. Thermography has application in both the near and remote sensing. The non-destructive nature of the infrared thermal imaging is beneficial, non-contact and the non-invasive having short period of time within which the data of the surface temperature of an object can be collected (Mutka & Bart, 2015). The technique is suitable for detection of modification due to diseases in a plant by measuring the water and the transpiration status. The temperature of a plant leaf can be determined through the plant respiration rate and environment. The leaf temperature increases when the rate of transpiration decreases. Similarly, some abiotic factors like the pathogens can also affect the stomatal opening which regulates the loss of water from the plant. The thermographic method that is used in the detection of the disease can be described as the passive measurement and is measured by tracking the amount of water transpired by the plant. The technique does not include any influence of external temperature. The temporal and the spatial scales can be measured within the tissue and of the tissue by measuring the transpiration data. Thus, further disease development can also be measured on the different scales (Fang & Ramasamy, 2015).

It has been found that local temperature changes have been noticed either due to the plant pathogens or due to the defence mechanisms of the plant. Hypersensitive responses have been observed within the tobacco leaves when it gets infected by the Tobacco Mosaic Virus (TMV). This results in the initial increase in the tissue size due to the salicylic acid accumulation. Whereas in the leaves of sugar beet the same kind of infection leads to the development of cold spots. It is important to note that the thermography utility is highly sensitive to the environmental conditions and thus its usage is a bit limited (Schaefer et al., 2012).

Fluorescence measurements

The fluorescence technology is used for the purpose of assessing the nitrogen demand for the crops in the field. A chlorophyll fluorescence is affected when a pathogen affects the enzymes of the Calvin cycle, electron transport chain, pigments and the photosynthetic apparatus of a plant. These methods are sensitive in detecting the abnormalities in the photosynthesis. The sensors needed to be put into the field and however is limited by the response time (Porcar-Castell et al., 2014).

Hyperspectral techniques

In measuring the plant vigour, the hyperspectral techniques have been found to be useful. The reflectance changes are noticed due to the changes in the biochemical and the biophysical characteristics of the plant tissues. Diseases in plants result in changes in the crop canopy density and its interaction with the solar radiation is plants, crop canopy morphology, transpiration rate, leaf shape and tissue colour. Due to this, the optical properties of the leaves gets modified. The reflectance of the leaves is dependent on the health of the plant and is thus sensitive to the changes in the cell wall degradation, hypersensitive reaction, and pigmentation (Bioucas-Dias et al., 2013).

Achieving the maximum yield at the lowest investment is one of the major and the ultimate goal of the farmers. There are techniques like the Global positioning systems (GPS) and Remote Sensing   (RS) that are used explicitly for assessing the temporal variations in crop yield and crop dynamics. The near-infrared regions and the visible portion of the electromagnetic spectrum plays a major role in assessing the crop yield, nitrogen stress, soil moisture, crop health and crop type. The advancement if the field of remote sensing has led to the usage of the multispectral images as a tool for monitoring the vegetation conditions, crop yield conditions and crop stress (Tittonell & Giller, 2013). The prediction of the crop yield is a major part here due to the several agronomic variables like the disease, maturity, vigour and density and these are used as yield indicators. It is thus important to mention that remote sensing has a crucial role to play in closely assessing the health of the plant. The reflectance is however dependent on the crop health, stage type and phenology. Several studies have shown that the in order to enhance the precision agriculture, the Normalized Difference Vegetation Index (NDVI) is used. The NDVI method is used for measuring the primary productivity due to its straight line relation with the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) (Al-Gaadi et al., 2016).

Advancement in the remote sensing area has led to the advances in agriculture as well. The satellite imagery has improved with respect to the spectral resolution, return visit frequency and spatial resolution. Certain aspects of precision farming still require further research which is as follows:

  • Without the requirement of the reference strips, the sensors must be able to estimate the nutrient deficiency directly.
  • The spectral indices must be developed continuously so that it can assess the multiple crop characteristics and stresses.
  • Emphasis must be put on to the spectral decomposition and chemometric decomposition (Mulla, 2013).

Conclusion

Thus, from the above study, it can be concluded that the precision agriculture has been made possible due to the advancement in the remote sensing arena. With the advent of the hypersensitive sensors, the capability of the measuring the crop health, crop disease and the crop yield is done remotely. A large number of data collected through the satellite provides the scope for spectral analysis. The spectral resolution also plays a pivotal role in identifying the several high resolution and detailed information. The Precision agriculture is both carried out regionally and remotely and the technique of remote sensing provides the option of assessing large areas remotely.

References

Al-Gaadi, K. A., Hassaballa, A. A., Tola, E., Kayad, A. G., Madugundu, R., Alblewi, B., & Assiri, F. (2016). Prediction of potato crop yield using precision agriculture techniques. PloS one, 11(9), e0162219.

Atzberger, C. (2013). Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sensing, 5(2), 949-981.

Barnhart, T. B., & Crosby, B. T. (2013). Comparing two methods of surface change detection on an evolving thermokarst using high-temporal-frequency terrestrial laser scanning, Selawik River, Alaska. Remote Sensing, 5(6), 2813-2837.

Bioucas-Dias, J. M., Plaza, A., Camps-Valls, G., Scheunders, P., Nasrabadi, N., & Chanussot, J. (2013). Hyperspectral remote sensing data analysis and future challenges. IEEE Geoscience and remote sensing magazine, 1(2), 6-36.

Chen, J., Zhang, M., Cui, T., & Wen, Z. (2013). A review of some important technical problems in respect of satellite remote sensing of chlorophyll-a concentration in coastal waters. IEEE journal of selected topics in applied earth observations and remote sensing, 6(5), 2275-2289.

Fang, Y., & Ramasamy, R. P. (2015). Current and prospective methods for plant disease detection. Biosensors, 5(3), 537-561.

Hunt, E. R., Daughtry, C. S., Mirsky, S. B., & Hively, W. D. (2014). Remote sensing with simulated unmanned aircraft imagery for precision agriculture applications. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(11), 4566-4571.

Kingra, P. K., Majumder, D., & Singh, S. P. (2016). Application of Remote Sensing and Gis in Agriculture and Natural Resource Management Under Changing Climatic Conditions. Agricultural Research Journal, 53(3), 295-302.

Maes, W. H., & Steppe, K. (2012). Estimating evapotranspiration and drought stress with ground-based thermal remote sensing in agriculture: a review. Journal of Experimental Botany, 63(13), 4671-4712.

Mulla, D. J. (2013). Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps. Biosystems engineering, 114(4), 358-371.

Mutka, A. M., & Bart, R. S. (2015). Image-based phenotyping of plant disease symptoms. Frontiers in plant science, 5, 734.

Peng, Y., & Gitelson, A. A. (2012). Remote estimation of gross primary productivity in soybean and maize based on total crop chlorophyll content. Remote Sensing of Environment, 117, 440-448.

Porcar-Castell, A., Tyystjärvi, E., Atherton, J., van der Tol, C., Flexas, J., Pfündel, E. E., ... & Berry, J. A. (2014). Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. Journal of experimental botany, 65(15), 4065-4095.

Santesteban, L. G., Guillaume, S., Royo, J. B., & Tisseyre, B. (2013). Are precision agriculture tools and methods relevant at the whole-vineyard scale?. Precision Agriculture, 14(1), 2-17.

Schaefer, A. L., Cook, N. J., Bench, C., Chabot, J. B., Colyn, J., Liu, T., ... & Webster, J. R. (2012). The non-invasive and automated detection of bovine respiratory disease onset in receiver calves using infrared thermography. Research in veterinary science, 93(2), 928-935.

Schlemmer, M., Gitelson, A., Schepers, J., Ferguson, R., Peng, Y., Shanahan, J., & Rundquist, D. (2013). Remote estimation of nitrogen and chlorophyll contents in maize at leaf and canopy levels. International Journal of Applied Earth Observation and Geoinformation, 25, 47-54.

Tittonell, P., & Giller, K. E. (2013). When yield gaps are poverty traps: The paradigm of ecological intensification in African smallholder agriculture. Field Crops Research, 143, 76-90.

Wei, G., Shalei, S., Bo, Z., Shuo, S., Faquan, L., & Xuewu, C. (2012). Multi-wavelength canopy LiDAR for remote sensing of vegetation: Design and system performance. ISPRS Journal of Photogrammetry and Remote Sensing, 69, 1-9.

Zhang, C., Walters, D., & Kovacs, J. M. (2014). Applications of low altitude remote sensing in agriculture upon farmers' requests–a case study in northeastern Ontario, Canada. PloS one, 9(11), e112894.

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