Geospatial Science and Technology Utilization in Agriculture

International Journal of Trend in Scientific Research and Development (IJTSRD)
Volume 6 Issue 4, May-June 2022 Available Online: www.ijtsrd.com e-ISSN: 2456 – 6470
@ IJTSRD | Unique Paper ID – IJTSRD50330 | Volume – 6 | Issue – 4 | May-June 2022 Page 1448
Geospatial Science and Technology Utilization in Agriculture
Dr. Anil Kumar
Associate Professor, Department of Botany, Government PG College, Rishikesh, Dehradun, Uttarakhand, India
ABSTRACT
Since the agrarian revolution during the 18th century, the use of
technology to improve the effectiveness and efficiency of farming
practices has increased tremendously. Discoveries in the field of
science and technology have enabled farmers to effectively use their
input to maximize their yield. These advancements have been greatly
assisted by the use of sophisticated machineries, planting practices,
use of fertilizers, herbicides and pesticides and so on. At the present
moment however, the success of large-scale farming highly relies on
geographic information technology through what is known as
precision farming. Precision agriculture, or precision farming, is
therefore a farming concept that utilizes geographical information to
determine field variability to ensure optimal use of inputs and
maximize the output from a farm (Esri, 2008). Precision agriculture
gained popularity after the realization that diverse fields of land hold
different properties. Large tracts of land usually have spatial
variations of soils types, moisture content, nutrient availability and so
on. Therefore, with the use of remote sensing, geographical
information systems (GIS) and global positioning systems (GPS),
farmers can more precisely determine what inputs to put exactly
where and with what quantities. This information helps farmers to
effectively use expensive resources such as fertilizers, pesticides and
herbicides, and more efficiently use water resources. In the end,
farmers who use this method not only maximize on their yields but
also reduce their operating expenses, thus increasing their profits. On
these grounds therefore, this article shall focus on the use of
geospatial technologies in precision farming. To achieve this, the
paper shall focus on how geospatial data is collected, analyzed and
used in the decision making process to maximize on yields.
How to cite this paper: Dr. Anil Kumar
"Geospatial Science and Technology
Utilization in Agriculture" Published in
International Journal
of Trend in
Scientific Research
and Development
(ijtsrd), ISSN: 2456-
6470, Volume-6 |
Issue-4, June 2022,
pp.1448-1453, URL:
www.ijtsrd.com/papers/ijtsrd50330.pdf
Copyright © 2022 by author(s) and
International Journal of Trend in
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Keywords: geospatial, technology,
agriculture, GPS, GIS, farmers,
yield, information, scale
INTRODUCTION
Agriculture faces many challenges today, including
climate change, depleted land quality, water
shortages, poor water quality, and economic
pressures. Farmers, however, do now have greater
access to computational and geospatial tools that can
also at least help mitigate some of these challenges.
Geospatial technology cannot be successful if the
correct data is not collected and analyzed effectively.
To achieve this, several techniques have been
advanced most of which are based on remote sensing.
Remote sensing is essential in dividing a large farm
into management zones (Grisso, 2009). Each zone has
specific requirements that require the use of GIS and
GPS to satisfy its needs. Thus, the first step of
precision farming therefore is to divide the land into
management zone. The division of this land into
zones is mainly based on:
1) Soil types
2) pH rates
3) Pest infestation
4) Nutrient availability
5) Soil moisture content
6) Fertility requirements
7) Weather predictions
8) Crop characteristics
9) Hybrid responses
This information can be accessed by reviewing
available records. Most farms usually have records of
soil survey maps, historical characteristics of crops,
and records that show the cropping practices of the
regions. Additionally, aerial and satellite photographs
can be used in this process. For example, in the image
sample below taken on January 30, 2001, three
IJTSRD50330

International Journal of Trend in Scientific Research and Development @ www.ijtsrd.com eISSN: 2456-6470
parameters were analyzed from a Daedalus sensor
aboard a NASA aircraft. The individual fields are
numbered in each of the images. The top image
(mostly yellow) shows vegetation density. The color
differences indicate crop density with dark blues and
greens for lush vegetation and reds for areas with bare
soil (known as “Normalized Difference Vegetation
Index”, or NDVI). The middle image analyzed water
distribution with green and blue areas measuring wet
soil and red areas indicating dry soil. The middle
image was derived from reflectance and temperature
measures from the Daedalus sensor. The last image
on the bottom measures crop stress with red and
yellow pixels indicating areas of high stress. The data
collected from analyzing these different conditions
allows the farmer to micromanage the application of
water to best address differing soil conditions and
vegetation growth.[1]
Technologies used today include GPS tracked
monitors that help record information including
weather, soil quality, crop progress, or even livestock-
related data. In particular, Internet of Things (IoT)
devices provide not only real-time data but GPS
tracking enables geospatial approaches to assess
information, such as volumetric measurements or
creating heat maps to measure spatial intensity.
By monitoring closely crops using small devices
placed by plants or soils, then farmers are better able
to forecast crop health and output prior to harvest.
This enables farmers to also better plan in advance.
Fig. An agriculture field
Additionally, one can generate up-to-date aerial and
satellite photographs of the farm during different
periods of the year or seasons. With this information,
the farmer is able to determine the productivity of
different management zones. At the same time, the
growth and yield patterns of different zones within
the farm can also be identified.
Furthermore, such devices help save costs because
farmers can plan more exactly how much fertilizer,
water, and other measures are needed based on real-
time conditions and forecasting. This can help limit
waste, particularly from fertilizers, which can have
negative consequences for water quality as runoff
from farms can greatly increase nitrate and phosphate
levels in water.
Increasingly, farming is seen as a major contributor to
climate change, in particular carbon released from
soils and emissions from livestock has been seen as
having negative consequences for our climate. By
creating more efficient decisions on when to crop,
minimizing the number of livestock and resources
needed for agriculture, farmers can at least help to
mitigate their impact on climate change emissions
Various remote sensing techniques can be used to
increase the effectiveness of this process. The most
common remote sensing technique that has been
applied over the years is observation with the use of
the human eye. With the help of modern technology,
any observation that is made using this method is
usually geo-referenced into a GIS database.[2] Much
of precision agriculture relies on image-based data
from remote sensing such as determining the
greenness of the field using a technique to determine
the productivity/yield of different managemen zones
(Brisco et al, n.d.). This technique is based on the
relationship that arises from the comparison of the
reflection of red light and near infrared light. Data
from RADARSAT has also provided farmers with
reliable information regarding the parameters that
determine soil conditions and crop performance.[3]
GIS technology has become a vital tool for crop
management. Geographic data about soil condition
helps farmers to be more efficient in segmenting
arable land in order to apply differential rates of
fertilizer, and forecasting to determine when, where,
and what to plant in what is known as precision
agriculture. Satellite and aerial imagery is used to
analyze existing conditions of the land, soil samples
taken from the fields are used to create a more precise
understanding of the condition of a farm. By
understanding the condition of the land on a micro
scale, farmers and those in the agriculture field can
better manage fertilizer and water application,
resulting in reduced costs and better crop yields.
The variations in crops grown organically versus
conventionally are significant enough to be detected
by analysis on satellite imagery. The European Space
Agency (ESA) has been working with Ecocert, an
organic certification organization, as well as
consulting firms Keyobs and VISTA, and Belgium’s
University of Liège, to develop a methodology to
analyze satellite imagery to differentiate crop fields
based on whether the crops were grown via organic or
conventional methods.

ESA analyzed multi- and hyperspectral imagery from
five different satellites, SPOT-4, Kompsat-2, Landsat-
5, Proba, and WorldView-2 as part of the study.
Using indicators that included crop spectral
reflectance, yield forecasts and spatial heterogeneity,
ESA was able to predict with an average accuracy of
90% which crops were grown organically versus
conventionally. Dr Pierre Ott from Ecocert,
concluded, “Accuracy rates of 80% to 100% in
discriminating organic from conventional fields are a
performance in itself. It seems very promising as far
as the potential of future developments is concerned.”
Efforts are ongoing to further refine this methodology
so that it can be commercially utilized.
Cornfield classification determinations using a
WorldView-2 satellite image acquired on August 10,
2010. The fields in light green are classified as
organic (KMO) and the ones shaded dark green are
classified as conventional (KM). An accuracy of
+90% was obtained on the classification between
organic and conventional.
The images were acquired by the Daedalus sensor
aboard a NASA aircraft flying over the Maricopa
Agricultural Center in Arizona. The top image
(vegetation density) shows the color variations
determined by crop density (also referred to as
“Normalized Difference Vegetation Index”, or
NDVI), where dark blues and greens indicate lush
vegetation and reds show areas of bare soil. The
middle image (water deficit) is a map of water deficit,
derived from the Daedalus’ reflectance and
temperature measurements. Greens and blues indicate
wet soil and reds are dry soil. The bottom image (crop
stress) shows where crops are under serious stress, as
is particularly the case in Fields 120 and 119
(indicated by red and yellow pixels). These fields
were due to be irrigated the following day.
Discussion
Farmers can use geospatial technologies such as GPS,
GIS, and Landsat satellite imagery to assess
variations in soil quality for planting crops. This heat
map shows soil quality with areas numbered 31
having the highest quality soil in a field for corn
productivity.
The data that is collected from remote sensing acts as
a source of point data. From the trends and
frequencies that have been recorded, this dataset can
easily be converted into spatial data that reflects the
situation of all management zones within the farm
with the use various GIS techniques and tools.
Kriging is an example of a method that can be used to
convert point data from remote sensing into spatial
data (Brisco et al, n.d.). Spatial data can then be used
to determine the possible problems that might be
present in various management zones. This gives
farmers the chance to come up with informed and
effective decisions to alleviate the prevailing
problems in order to boost the overall production of
the farm.[4]
Once point data has been collected, it needs to be
stored and analyzed for it to be useful to the farmer. It

is at this point that GIS tools come into use. GIS
software can be used to develop digital maps that
transform spatial information that has been collected
on the ground into digital format. At the same time,
the point data that had been collected on the field can
now be transformed into spatial data to reflect the
entire farm. [1] To effectively differentiate points
with different values within the management zones,
the collected data is normally presented in either
raster or vector formats (Brisco et al, n.d.). In raster
format, imaginary grids within a map are developed.
Points within the map that have different values are
assigned different colours. Therefore, from a glance, a
user can be able to identify points that have similar
characteristics and differentiate them with points that
have different characteristics. This form of data
representation is useful in spatial modelling to show
the relationship that exists within grouped data.
Vector format on the other hand uses coordinates
from the x-axis and y-axis to assign a specific point
within a map. Points that have similar characteristics
are plotted and joined together to form a borderline.
This form of data presentation is effective in
computerized mapping and spatial database
management.[5]
Aerial technologies are also helping farmers make
better choices by using unmanned aerial vehicles
(UAVs) as well as satellite technologies. For instance,
the Soil Moisture Ocean Salinity (SMOS) satellite,
launched in 2009, is able to collect microwave data
from the Earth’s surface, which can allow planners
and farmers to forecast crop production and assess the
likelihood of drought or even flooding prior to events
occurring. This enables decisions to be made well in
advance, helping to mitigate stresses for crops.[9]
On the other hand, small, cheap drones are often used
to provide more fine-scale assessment, including data
on plant height, count and biomass estimates,
indication of disease, presence of weeds, plant health,
field nutrients, and volumetric data using simple
cameras that can create photogrammetric data
Powerful modeling tools, such as Decision Support
System for Agrotechnology Transfer (DSSAT) and
Soil-Water-Air-Plant (SWAP), have also become
incorporated with common and open source GIS tools
such as GRASS, enabling farmers and analysts to
forecast water availability and crop health without
great expense.
These tools are increasingly incorporated with high
performance computing (HPC) or cloud-based
computing, enabling large-scale analyses for large
areas in the tens of thousands of hectares to be
estimated[8]
For decades, Landsat and other more recently
developed multi-spectral satellites, such as Advanced
Spaceborne Thermal Emission and Reflection
Radiometer (ASTER), have been used to provide
Normalized Difference Vegetation Index (NDVI)
data. This allows farmers to monitor the health of
their crops and estimate harvest for wide areas.
Once spatial data has been mapped, comparison of
the results that are presented with the field notes is
essential. This process is conducted to determine any
trends and relationships that might be present on the
ground. At this point, an area that has high content of
nutrients in the soil or a region that is highly infested
with parasites might be identified. This distribution
can either be in the form of uniform or non-uniform
variability. With this information, favourable
management techniques can be put in place to
increase the efficiency of farming to ensure optimal
use of inputs and to maximize the output. Thus, the
information that has been provided with the use of
remote sensing and GIS can be used to make site-
specific decisions with regards to the use of fertilizer,
herbicides and pesticides, irrigation and so on. Most
importantly, the data that has been generated needs to
be stored in a systematic manner for future reference.
This is essential, as it will increase the effectiveness
and efficiencies of future surveys.[2]
The main reason of collecting this data is for a farmer
to have a clear understanding of the needs of different
points in the farm to maximize his production. As this
need increases, the use of automated farm machinery
is inevitable (Sohne et al, 1994). These machines are
expected to conduct their work precisely according to
the information that has been fed on them. With the
use of GIS and GPS, automated farm machineries are
now more accurate, safe, eliminate human effort
required to drive them and most importantly, increase
the productivity of farms.[3]
Results
Many of the key advancements powering the utility of
IoT, remote sensing, and simulation applications have
to do with improved capabilities in machine learning,
in particular deep learning techniques.Deep learning
using convolution neural networks (CNNs) has
enabled farmers to make better decisions from
collected data. For instance, using drone data, CNNs
can be used to count the number of livestock or make
measurements on crops using visual data.Data from
IoT devices can also be assessed, helping to find
emerging patterns of crop stress before it becomes too
serious. These advancements have helped to make
machine and deep learning techniques become
increasingly critical for decisions that help save
resources while responding to threats.[5]

We have seen many transformations to technologies
and techniques used that can benefit agricultural
decisions. Farmers have a greater variety of data to
choose from to help with decisions needed that not
only benefit them but also can have a positive impact
on the environment.[7]
Costs and technology access may limit some farmers
from benefiting changes occurring for modern
agriculture; however, many of these technologies are
declining in cost and, in fact, many of the tools, such
as GIS and some of the satellite data, are free to use.
Improving how agriculture is done will increasingly
be more critical as we try to be more effective in how
we use landscape resources to mitigate negative
impacts on the economy and climate.
Automated farm machineries are operated with the
help of Navigation Geographic Information Systems
(NGIS). This system is a combination of GPS and
GIS systems that enables the machine to:
1. Map Display
2. Path Planning
3. Navigation Control
4. Sensor System Analysis
5. Precision Positioning
6. Data Communication
The system also enhances the management of the
automated machines by enabling the user to control
its speed, direction, and to monitor the surrounding
conditions (Xiangjian and Gang, 2007). For
automated machines to conduct their roles effectively
and efficiently, they need to be fed with positioning
information. This information is usually sent via a
GPS receiver that contains precise time, latitudes and
longitudes. The machine also received information
with regards to the height above ground as well as the
height above sea level. With the help of its GPS
system, the machine is usually guided through an
optimal path. Factors such as the length, traffic
characteristics, corners and costs are usually
considered while generating the path that shall be
followed by the machine. Steering of the machine is
determined by the angle that exists between the target
points within the path. This ensures that the machines
cover all the target points that have been identified
from the spatial data from GIS. This therefore ensures
that the machine will traverse the farm and spray,
deposit or plant the exact amount or quantity of input
that is required to maximize the output of a given site
as per the findings in the farm.[6]
Trimble is one geospatial vendor for precision
agriculture technology. Tractor with Trimble based
GPS technology on board.[4]
Conclusions
With the use of remote sensing, GPS and GIS,
farmers can be able to understand site-specific needs
of their farms. With this information, they are capable
of formulating and implementing management
techniques that will ensure the optimal use of inputs
to maximize their output and profits. Geospatial
technologies therefore provide a farmer with an
information resource that he/she can use to make
informed decisions that guarantee effective and
efficient management of the farm to maximize its
productivity. Thus, farmers should understand and
implement these technologies in conjunction with
their experience and expertise to get maximum
benefits of their farms.[9]
References
[1] Brisco, B., Brown, R., Hirose, J., McNairn, H.
and Staenz, K. (n.d.). Precision Agriculture and
the Role of Remote Sensing: A Review.
Retrieved on 1st October 2012 from
ftp://ftp.geogratis.gc.ca/part6/ess_pubs/219/219
370/3520.pdf
[2] Esri (2008). GIS for Sustainable
Agriculture. GIS Best Practices. New York:
ESRI Publications
[3] Grisso, B. (2009). Precision Farming: A
Comprehensive Approach. Retrieved on
1st
October 2012
from http://pubs.ext.vt.edu/442/442-500/442-
500.html
[4] Sohne, W., Heinze, O. and Groten, E. (1994).
Integrated INS/GPS System for High Precision
Navigation Applications. Record-IEEE PLANS,
Position Location and Navigation
Symposium, 35(2): 310-313.
[5] Xiangjian, M. and Gang, L. (2007). Integrating
GIS and GPS to Realise
Autonomous Navigation of Farm
Machinery. New Zealand Journal of
Research, 50(1), 807-812

[6] For more on IoT and agriculture,
see: Jaiganesh, S., Gunaseelan, K., Ellappan,
V., 2017. IOT agriculture to improve food and
farming technology, in: 2017 Conference on
Emerging Devices and Smart Systems
(ICEDSS). Presented at the 2017 Conference on
Emerging Devices and Smart Systems
(ICEDSS), IEEE, Mallasamudram,
Tiruchengode, India, pp. 260–
266. https://doi.org/10.1109/ICEDSS.2017.807
3690.
[7] For more on a recent article discussing using
satellite sensors and UAV data for agriculture,
see: Mazzia, V., Comba, L., Khaliq, A.,
Chiaberge, M., Gay, P., 2020. UAV and
Machine Learning Based Refinement of a
Satellite-Driven Vegetation Index for Precision
Agriculture. Sensors 20,
2530. https://doi.org/10.3390/s20092530.
[8] For more on some models used for agriculture,
see: Wang, Xiaowen, Cai, H., Li, L., Wang,
Xiaoyun, 2020. Estimating Soil Water Content
and Evapotranspiration of Winter Wheat under
Deficit Irrigation Based on SWAP Model.
Sustainability 12, 9451.
https://doi.org/10.3390/su12229451
[9] For more on a recent deep learning tool in
agriculture, see: Zheng, Y.-Y.; Kong, J.-L.; Jin,
X.-B.; Wang, X.-Y.; Su, T.-L.; Zuo, M.
CropDeep: The Crop Vision Dataset for Deep-
Learning-Based Classification and Detection in
Precision Agriculture. Sensors 2019, 19, 1058.

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