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Chapter - 4
Precision Agriculture: Technology and
Implementation
Authors
Satyam Anand
P.G. Scholar, Soil Science and Agricultural Chemistry, School
of Natural Resource Management- College of Post Graduate
Studies in Agricultural Sciences, CAU (Imphal), Umaim,
Meghalaya, India
Pushpam Kumar
P.G. Scholar, Soil Science and Agricultural Chemistry, School
of Natural Resource Management- College of Post Graduate
Studies in Agricultural Sciences, CAU (Imphal), Umaim,
Meghalaya, India
Ankit Alok
Ph.D. Scholar, Plant Pathology, School of Crop Protection-
College of Post Graduate Studies in Agricultural Sciences,
CAU (Imphal), Umaim, Meghalaya, India
Rishikesh Kumar
P.G. Scholar, Agricultural Extension, School of Social Science-
College of Post Graduate Studies in Agricultural Sciences,
CAU (Imphal), Umaim, Meghalaya, India
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Chapter - 4
Precision Agriculture: Technology and Implementation
Satyam Anand, Pushpam Kumar, Ankit Alok and Rishikesh Kumar
Abstract
Agriculture production highly depends on water and soil factors which
increasingly need to be utilized efficiently. Precision agriculture technology
has emerged as a transformative force in modern agriculture, revolutionizing
how farmers manage their crops and livestock. This innovative approach
leverages advanced technologies, data analytics and automation to optimize
farming practices, increase crop yields, and minimize environmental impact.
Precision agriculture has become indispensable in addressing the challenges
posed by a growing global population, climate change and resource
constraints. The implementation of precision agriculture begins with the
collection of real-time data from various sources, such as satellite imagery,
remote sensing, global positioning systems, geographic information systems,
drones, soil sensors and weather stations. These data streams are then
processed and analyzed using sophisticated algorithms to provide farmers
with valuable insights into their field conditions. Armed with this
information, farmers can make precise decisions regarding irrigation,
fertilization, pest control, and harvesting, thereby reducing waste and
enhancing resource efficiency. The adoption of precision agriculture
technology has demonstrated remarkable results, including improved crop
yields, reduced chemical usage and enhanced sustainability. It also enables
farmers to respond rapidly to changing conditions, optimizing resource
allocation while minimizing environmental impact. However, its successful
implementation requires investments in infrastructure, education and data
management capabilities. In this era of global food security and sustainable
agriculture, precision agriculture technology represents a crucial tool for
ensuring efficient and responsible food production. As technology continues
to advance, its integration into farming practices promises to revolutionize
the agricultural industry and help meet the growing demands of a changing
world.
Keywords: Precision agriculture, remote sensing, global positioning system,
geographic information system, sustainable agriculture.
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Introduction
The agricultural production system is a product of the intricate interplay
among seeds, soil, water and agrochemicals, notably fertilizers. Therefore,
prudent management of all these elements is paramount for the long-term
viability of such a complex system. The single-minded pursuit of increased
productivity during the green revolution, while neglecting the responsible
handling of inputs and disregarding ecological consequences, has resulted in
environmental deterioration. Precision agriculture emerged in the late 1980’s
with the matching of grid-based sampling of soil chemical properties with
the newly developed variable-rate application equipment for fertilizers. Since
then, it has become the main farming management practice worldwide.
Precision agriculture, also known as precision farming or smart farming, is a
revolutionary approach that leverages cutting-edge technology and data-
driven strategies to transform traditional agricultural practices. This
innovative methodology acknowledges that not all areas within a field are
the same and it seeks to optimize every aspect of farming operations, from
planting to harvesting, with a focus on maximizing efficiency, productivity,
and sustainability. Historically, agriculture relied on one-size-fits-all
methods, treating entire fields uniformly with fertilizers, pesticides and
water, regardless of the varying conditions within them.
However, the use of advanced technologies, including Geographic
Information Systems (GIS), Global Positioning Systems (GPS), Remote
Sensing (RS) and the Internet of Things (IoT), has ushered in a new era of
precision agriculture. Precision agriculture aims to customize farming
practices by taking into account the unique characteristics of different areas
within a field. This involves collecting real-time data from a variety of
sources, such as soil sensors, drones, satellite imagery and weather stations,
to gain insights into soil health, moisture levels, pest infestations and crop
performance. By analyzing this data, farmers can make informed decisions
on precise actions like targeted irrigation, tailored fertilizer application and
timely pest management.
The implementation of precision agriculture requires a fundamental shift
in the way farmers approach their operations. It demands a combination of
technological infrastructure, data analytics capabilities and a deep
understanding of agronomy. Farmers must adapt to this data-driven
paradigm to maximize crop yields, minimize resource waste and reduce
environmental impact. In this era of increasing global food demand, resource
scarcity and environmental concerns, precision agriculture technology offers
a promising solution to sustainably meet the world's agricultural needs. This
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comprehensive exploration will delve into the various facets of precision
agriculture, including its key technologies, benefits, challenges and the
transformative impact it has on modern farming practices.
Precision agriculture
The word ‘precision’ means exactness or accuracy. Precision agriculture
is a management strategy that gathers, processes and analyses temporal,
spatial and individual data and combines it with other information to support
management decisions according to estimated variability for improved
resource use efficiency, productivity, quality, profitability and sustainability
of agricultural production (International Society of Precision Agriculture).
Why precision farming?
1. Increasing agricultural productivity for economic gains.
2. Combating soil erosion and degradation on arable land.
3. Lowering the reliance on chemicals in crop cultivation.
4. Optimizing the utilization of water resources.
Advantages
Agronomical perspective
Implement agronomic techniques tailored to the
specific needs of each crop
Technical perspective
Enables effective time management.
Environmental perspective
Environmentally conscious approaches in crop
cultivation
Economical perspective
Enhances crop yield and quality while minimizing
production costs through the efficient utilization of
farm inputs, labour, water and other resources.
The need for precision agriculture
Traditional farming practices have resulted in extensive use of
agricultural inputs like machinery, pesticides, water and other resources,
leading to adverse environmental effects, including greenhouse gas
emissions. Precision agriculture, also known as precision farming, seeks to
optimize profitability while safeguarding the environment through the
efficient allocation of inputs based on the temporal and spatial variations in
soils and crops. Developed countries have embraced sensor and satellite
image-based technologies to promote precision agriculture. Economic
assessments of precision farming adoption have shown slight profitability
gains compared to existing best management practices (BMPs) and higher
productivity levels. To bridge the substantial yield gap between potential and
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actual levels in the developing world, there is a need to promote precision
farming to realize its intended benefits. Precision agriculture employs a
combination of technologies to effectively manage resources. In this
approach, inputs are meticulously applied to achieve increased average
yields compared to traditional farming methods. The distinctions between
traditional and precision farming are outlined below:
Traditional farming
Precision farming
The field is considered a uniform area in
terms of treatment and organization.
An arable location is seen as varying
from one point to another.
Nutrient management determined by the
field's overall average.
Nutrient management relies on GPS and
specific points.
Plant protection is determined by
averaging samples that have been affected
or damaged
Plant protection relies on sampling at
specific GPS points
Achieving low crop yields despite using a
significant amount of inputs.
Obtaining a high crop yield while using
minimal inputs.
Objectives
• Encouraging new initiatives in the 'Agriculture and its related
sectors' by integrating diverse agricultural elements to harness their
variability
• Lowering cultivation expenses as a result of site-specific crop
management techniques.
• Improvement in the efficiency of input utilization through site-
specific input management.
• Decrease in soil and environmental contamination.
• Decreasing nutrient application, particularly nitrogen fertilizer,
which results in reduced nitrate levels in groundwater and
diminished nitrous oxide emissions into the atmosphere.
• The decrease in chemical usage is achieved via variable rate
application technology.
• Decreasing the usage of irrigation water, resulting in reduced
nutrient runoff and deep percolation.
• Minimizing erosion, runoff and sedimentation in water bodies.
Concept of precision agriculture or precision farming:
Precision agriculture is the practice of applying precise and accurate
amounts of inputs like water, fertilizers, pesticides, etc., to crops at the right
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time to enhance productivity and maximize yields. It emphasizes using
inputs, such as chemical fertilizers and pesticides, in the correct quantity, at
the appropriate time and in the right locations. The goal of precision farming
is to align agricultural inputs and methods with specific crops and agro-
climatic conditions to improve the precision of their application.
The foundation of precision farming lies in the Global Positioning
System (GPS), originally developed by U.S. defense scientists for exclusive
military use. Precision farming primarily aims to reduce health hazards and
enhance safety for soil, the environment and human health through the
implementation of various technologies and machinery. It relies on the
identification, assessment, and management of variability in agricultural
practices. When people think of precision agriculture, they often picture
farmers utilizing computerized machinery controlled precisely through
satellites and local sensors, guided by planning software that accurately
predicts crop growth. This vision is often referred to as the future of
agriculture.
In the Indian context, precision farming can be defined as the precise
application of agricultural inputs for crop cultivation, considering factors
such as soil quality, weather conditions and crop management practices. It is
essentially an information and technology-based farming system where
inputs are managed and distributed on a location-specific basis to achieve
long-term benefits.
Precision Farming systems (PFS) are built upon recognizing spatial and
temporal variations in crop production. These systems account for variability
in farm management to increase productivity while reducing environmental
risks. In developed countries with large land holdings (1,000 hectares or
more), spatial variability is evident, encompassing both within-field and
between-field variations. Components, often referred to as enabling
technologies, of precision farming include:
• Remote sensing (RS)
• Geographical information system (GIS)
• Global positioning system (GPS)
• Soil testing
• Yield monitors
• Variable rate technology (VRT)
Remote sensing, global information systems, global positioning systems
and variable rate application are employed to accurately manage input
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utilization in production. The distinguishing feature of GPS and GIS is their
precision in both time and space. This approach is often referred to as "Site-
Specific Management" as it involves precise management of site-specific
nutrients and encompasses all aspects of field and production management,
from seed sowing to crop harvesting. The goal is to efficiently manage crop
production inputs, including water, seeds and fertilizers, with the aim of
boosting yield, quality, profitability, minimizing waste and promoting
environmentally friendly practices.
This type of farming system which comprises site specific management
system within a field is also referred as site-specific crop management
(SSCM). According to Second International Conference on Site Specific
Management for Agricultural systems, held in Minneapolis, Minnesota, In
March 1994, SSCM refers to a developing agricultural management system
that promotes variable management practices within a field according to site
or soil conditions (National Research Council, 1997).
However, according to Batte and Van Buren (1999), SSCM is not a
single technology but an interaction of several technologies that functions as
a whole system from collection of data to implementation and management
of resources as:
1. Gathering data at an appropriate scale and timing.
2. Incorporating and examining data to assist and guide various
decision-making processes.
3. Putting into action the responses generated and managing resources
accordingly, done on a suitable scale and at the right time.
Precision farming leverages modern technology to gather field data,
enabling precise resource allocation through sustainable practices, with a
primary focus on conserving natural resources as a key function. This
approach is widely integrated into agricultural systems. It is well known in
agricultural system as:
1. Land preparation
2. Seeding
3. Chemical application
4. Fertiliser application
5. Crop monitoring
6. Nutrient auditing
7. Soil and leaf testing
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8. Disease and pest management
9. Conservation agricultural practices
10. Yield and gross margin analysis
Component/tools of precision agriculture technology and their
implementation
Precision farming is the result of integrating various technologies, with
each combination being interconnected and contributing to advancements.
These interconnected technologies are detailed below:
1. Global Positioning System (GPS): A network of 24 satellites
orbits the Earth, emitting radio signals that can be received and
processed by ground receivers to pinpoint precise geographical
locations on our planet. This system boasts a 95% likelihood that
the indicated position on Earth will be accurate within a range of
10-15 meters. GPS technology enables the meticulous mapping of
farms and, when combined with suitable software, provides farmers
with real-time information about their crop's status. It also advises
them on the specific needs of different parts of the farm, whether it
be water, fertilizer, pesticides and so forth.
2. Geographic Information System (GIS): The use of GIS
commenced in the 1960s, representing a computerized mapping
system designed to acquire, store, analyze, and present information
closely tied to Earth's geographic references. GIS functions as
software capable of importing, exporting, and processing data
distributed in a spatial and temporal manner. This system facilitates
the layering of different datasets, which find applications in tasks
like land use planning, irrigation management, crop analysis, soil
assessment, and environmental studies.
A comprehensive GIS system comprises hardware, software and
established procedures aimed at supporting the compilation, storage,
retrieval and analysis of data attributes and their geographic locations to
generate maps. Computerized GIS maps differ from traditional maps in that
they incorporate multiple layers of information. GIS can work with various
forms of location data, such as latitude and longitude, addresses, or ZIP
codes. It can encompass diverse types of information for comparison and
contrast, including demographic data like population, income, or education,
as well as geographical data like stream locations, vegetation types, and soil
characteristics. Furthermore, it can encompass data related to the locations of
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factories, farms, schools, infrastructure like storm drains and roads, and
utilities like electric power lines.
As an example, if a rare plant species is observed in three different
locations, GIS analysis could reveal that these occurrences share common
traits, such as being on north-facing slopes at elevations above 1,000 feet,
receiving more than ten inches of rainfall annually. GIS maps can then be
used to identify other areas in the vicinity with similar conditions, aiding
researchers in their search for additional occurrences of the rare plant.
3. Grid sampling: This approach involves dividing a field into
smaller grids, typically ranging from 0.5 to 5 hectares in size. Soil
sampling is conducted within these grids to ascertain the correct
fertilizer application rates. Multiple soil samples are collected from
each grid, blended together, and then sent to a laboratory for
analysis.
4. Variable Rate Technology (VRT): Variable-rate technology
(VRT) enables the application of fertilizers, chemicals, lime,
gypsum, irrigation water and other agricultural inputs to be adjusted
at varying rates across a field. Variable-rate application (VRA) can
encompass a range of adjustments, from simple control of flow rate
to the more intricate management of rate, chemical composition and
application pattern. VRA allows for aligning changes in crop yield
potential with specific input rates, resulting in a more efficient
system and minimizing potential environmental impacts. VRT can
address the spatial variations within different management zones.
There are two primary types of VRT:
1) Map-based control: In this approach, a map detailing
application rates for the entire field is generated before the
operation begins.
2) Real-time control: Decisions regarding application rates in
different locations are made in real time using data collected
during the operation. This typically requires sensors to gather
necessary information on the go and is often designed for
specific tasks like herbicide application.
A variable-rate applicator consists of three key components: a control
computer, a locator, and an actuator. The application map is loaded into a
computer that is mounted on the variable-rate applicator.
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Requirements of VRA systems
• Prescription maps are used to specify input rates tailored to specific
sites or zones.
• Global Navigation Satellite Systems (GNSS), like GPS, aid the
applicator in understanding and utilizing the prescription map.
• Variable-rate capable machinery (sprayer, spreader, etc.).
• A control system that adjusts input rates based on application maps.
5. Nutrient expert system: This computer-based decision tool offers
customized nutrient recommendations for rice, wheat and maize to
individual farmers, regardless of whether they have soil testing data
available or not. Developed by the 'International Plant Nutrition
Institute,' this tool calculates potential yields based on specific
growing conditions and covers all macronutrients. Its primary
benefits include reducing nutrient wastage and providing location-
specific nutrient management guidance.
6. Site-specific nutrient management (SSNM): The SSNM (Site-
Specific Nutrient Management) approach is centered on providing
crops with nutrients precisely at the appropriate times. SSNM
adheres to the principles of the 5R's, which include delivering the
correct dose, at the right time, in the correct location, using the
proper method, and sourcing nutrients appropriately. The SSNM
process involves three key steps: first, setting a realistic and
attainable yield target; second, optimizing the use of available local
nutrient RESOURCES; and finally, applying fertilizers to address
the shortfall between the crop's requirements and the nutrients
naturally present in the area.
7. Yield maps: Yield maps are generated through the analysis of data
collected by a modified combine harvester equipped with an
integrated GPS system and a yield recording mechanism. The
process of yield mapping entails tracking the flow of grain as it
passes through the combine harvester while simultaneously
recording the precise location within the field.
8. Remote sensors: These typically fall into the groups of aerial or
satellite sensors that can detect alterations in field colors,
corresponding to shifts in factors like soil type, crop growth, field
borders, roads, water and more. Aerial and satellite images can
undergo analysis to generate vegetative indices, offering insights
into the overall health of the plants.
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9. Proximate sensors: These sensors can be employed to assess soil
characteristics like nitrogen levels and soil pH, as well as crop
attributes, as the sensor-equipped tractor traverses the field.
10. DRIS approach: This method proves valuable for analyzing
nutrient content within plants. DRIS, which stands for Diagnosis
and Recommendation Integrated System, was introduced by
Beaufils in 1973. Unlike conventional approaches, this technique
takes into account the ratio of nutrient concentrations within the
plant rather than focusing solely on individual nutrient elements.
11. Precision irrigation systems: The latest advancements in sprinkler
irrigation are being introduced for commercial applications, which
involve the use of GPS-based controllers to regulate the motion of
irrigation machinery. Additionally, there are ongoing developments
in wireless communication and sensor technologies aimed at
monitoring soil and environmental conditions, as well as the
operational parameters of irrigation machinery, including factors
like flow and pressure. These innovations are geared towards
enhancing water utilization efficiency in the irrigation process.
12. Precision farming on arable land: Precision agriculture methods
are most extensively and innovatively applied by farmers in arable
land. Controlled Traffic Farming (CTF) stands out as a holistic farm
strategy aimed at mitigating unnecessary crop damage and soil
compaction caused by heavy machinery, thereby curbing expenses
associated with conventional techniques. Controlled traffic practices
revolve around restricting all field vehicles to the narrow,
permanent traffic lanes, aided by decision support systems. Another
pivotal application of precision agriculture in arable land involves
the optimization of fertilizer usage, particularly for essential
nutrients like Nitrogen, Phosphorus and Potassium.
13. Bio-intensive farming: This is an organic farming approach with a
primary goal of achieving the highest possible crop yield from
minimal land while also enhancing and preserving soil fertility. The
central objective is to generate maximum biomass per unit area. The
concept and techniques of bio-intensive farming were introduced by
Alan Chadwick in the United States. Key elements of bio-intensive
farming encompass raised bed cultivation, biologically balanced
fertilization (BBF), French intensive raised bed (FIRB) design,
intensive planting methods, intercropping, companion planting and
a comprehensive focus on overall system energy management.
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14. Decision-support systems: Decision-support systems (DSS)
belong to a specific category of computerized information systems
designed to aid in business and organizational decision-making
processes. When it comes to generating control maps for field
operations guided by maps, some form of decision-making system
is typically employed. Even when decisions are made manually, the
abundance of data and intricacies involved in crop production make
a compelling case for utilizing a decision-support system. For
instance, determining phosphorus application rates for different
areas to address deficiencies and provide adequate nutrients for
wheat crops involved a computer program. This program
considered input data such as soil type, soil test results and yield
potential based on historical yields.
The decision-making computer program can be deterministic, relying on
predefined rules or formulas. It computes the appropriate control action for
each specific section of the field or orchard using geographic information
system data layers and the guidelines programmed into the decision-making
software. Alternatively, it can be stochastic, relying on computer
simulations. Validated crop-growth models are executed with various field-
operation control strategies under representative weather scenarios for each
field segment. The strategy that offers the highest economic return with an
acceptable level of risk is then employed to establish the field operation
control map. In real-time systems, control algorithms are essential for
promptly adjusting actuators based on sensor data.
Steps of implementation of precision farming
In the execution of precision farming, there are fundamental phases:
assessing variability, effectively managing variability, and conducting a
comprehensive evaluation of variability. The existing technologies empower
us to comprehend the variations within agricultural settings, and by offering
location-specific agronomic advice, we can effectively address and leverage
this variability, thus rendering precision agriculture practical. Additionally,
an assessment component should be an inherent part of any precision
farming system. These processes entail a comprehensive breakdown of
detailed steps, which are illustrated accordingly.
1. Assessing the variability: The initial and pivotal step in precision
farming involves the assessment of variability. This step is deemed
critical because one cannot effectively manage something without
first comprehending it. Factors and processes that influence crop
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performance and yield exhibit spatial and temporal variations. The
challenge in precision agriculture lies in quantifying this variability,
understanding when and where different combinations of factors
and processes contribute to spatial and temporal variations in crop
yield. Techniques for evaluating spatial variability are readily
accessible and have been widely applied in precision agriculture,
representing a significant component of the practice. While methods
for assessing temporal variability do exist, it's relatively uncommon
to simultaneously analyze both spatial and temporal variations. To
fully grasp the reasons behind yield variability, we require
observations spanning the crop's growth and development
throughout the growing season, which pertains to temporal
variation. Consequently, the application of precision farming
techniques necessitates the use of both spatial and temporal
statistics. However, this principle doesn't apply universally to all
factors influencing crop yield, as certain variables exhibit greater
spatial variations rather than temporal ones, rendering them more
compatible with current precision management methods.
2. Managing the variability: After a comprehensive assessment of
variability, farmers must align agronomic inputs with the known
conditions using site-specific and precise management
recommendations. These recommendations are tailored to specific
locations and are executed with the aid of accurate application
control equipment. To achieve the utmost effectiveness in site-
specific variability management, GPS technology can be employed
to enhance precision, making management more efficient and cost-
effective. When collecting soil or plant samples, it's important to
record the coordinates of the sampling sites, which can then be used
for management purposes. This approach ensures the efficient
utilization of inputs, minimizes wastage and aligns with the primary
objectives of precision farming.
The potential for enhanced precision in soil fertility management,
coupled with increased accuracy in application control, makes precise soil
fertility management an attractive prospect, albeit one that requires further
validation compared to uniform field management. Successful
implementation of precision soil fertility management hinges on the presence
of within-field variability that is accurately identified and reliably
interpreted, with demonstrated impacts on crop yield, crop quality and
environmental considerations. This enables the precise application of inputs.
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The greater the spatial dependency of a manageable soil property, the higher
the potential for precision management and its associated benefits. However,
the challenge intensifies as the temporal aspect of spatial variability becomes
more pronounced. When applied to soil fertility, this hypothesis supports the
idea that precision management is highly suitable for nutrients like
Phosphorus and Potassium, where temporal variability is minimal.
Conversely, for Nitrogen, where temporal variability can exceed spatial
variability in some instances, achieving precision in management can be
considerably more challenging.
3. Evaluating the variability: There are three crucial aspects to
consider when evaluating precision agriculture: economics,
environment and technology transfer.
a) Economics: It's essential to recognize that the profitability of
precision agriculture is primarily derived from effectively
applying the data, rather than merely utilizing the technology
itself.
b) Environment: Precision agriculture is often touted as a means
to potentially enhance environmental quality. This can involve
reducing the use of agrochemicals, increasing nutrient
utilization efficiency, improving the management of inputs, and
preventing soil degradation, all of which are seen as potential
environmental benefits.
c) Technology transfer: The term "technology transfer" might
suggest that precision agriculture is achieved when individuals
or organizations simply acquire and use enabling technologies.
However, precision agriculture involves more than just
technology adoption; it entails the effective management of
spatial and temporal variability. While enabling technologies
and agronomic principles are crucial, the key emphasis is on
the management aspect. Much of the focus in what's referred to
as technology transfer has been on communication with
farmers. As precision agriculture continues to evolve, factors
such as the operator's managerial skills, the spatial distribution
of infrastructure and technology compatibility with individual
farms will undergo significant changes.
Implementation of precision agriculture in agriculture system
Precision agriculture has become increasingly prevalent in modern
farming practices, encompassing activities from land preparation and seed
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sowing to agronomic management and final harvesting. This approach offers
a precise system that not only provides accurate data for site-specific
functions but also aids in the optimization of natural resources for future use.
One notable implementation of precision agriculture is the utilization of laser
land levelers. Uneven soil surfaces can significantly impact crop
germination, stand establishment, and yield due to uneven water distribution
and soil moisture levels. Therefore, land leveling is a critical prerequisite for
effective agronomic, soil and crop management practices. Laser-guided
leveling equipment represents an advanced method for achieving precise
field leveling. Laser land leveling entails the use of a laser-guided system to
level or grade the field with a specified degree of slope. This system consists
of a laser-transmitting unit that emits an infrared beam of light, capable of
traveling up to 700 meters in a straight line. A receiver component senses the
infrared beam and converts it into an electrical signal. A control box then
directs this signal to activate an electric hydraulic valve. The hydraulic valve
adjusts the blade of a grader, ensuring it follows the infrared beam by raising
and lowering it several times per second. This process is accomplished using
a dual slope laser that automatically controls the land leveler's blade to
precisely grade the surface, eliminating any undulations that may retain
water. Laser transmitters create a reference plane over the work area by
rotating the laser beam 360 degrees. The receiving system detects the beam
and guides the machine to maintain the desired grade. The laser can be
oriented horizontally or with slopes in two directions. Importantly, these
adjustments occur automatically without the operator needing to manually
manipulate hydraulic controls.
Benefits of precise land leveling
• Saves irrigation water >35%
• Reduced weed in the field
• Increase in field areas about 3.5%
• Reduce farm operating time by 10%
• Assist top soil management
• Saves labor costs
• Saves fuel/electricity used in irrigation
• Increase productivity up to 50%
Mechanized direct seed sowing: The uniformity of plant development
is strongly influenced by the uniformity of seedlings, which should exhibit
consistent growth in terms of height, thickness, fresh crop weight, floral
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primordial evolution, and overall health, whether for a specific hybrid
variety or a general qualitative characteristic. Sowing technologies play a
crucial role in ensuring this uniformity, especially when dealing with treated
vegetable and flower seeds. These treatments aim to eliminate disease or pest
germs (seed disinfection) or prevent contamination after seeding. Seed
treatments can involve chemical methods (such as wetting or dusting with
various chemicals) or physical methods (including heat, cold, UV rays, X-
rays, etc.), depending on the species. Therefore, the use of automatic sowing
equipment is advisable during the seeding process, as it ensures safety and
reduces the risk of intoxication with potentially harmful substances.
Soil mapping: Traditionally, soil mapping has been carried out by
experienced soil surveyors who are intimately familiar with the area, spend
significant time in the field, take soil samples at regular intervals, and create
a field soil map that is later digitized and printed. A soil map represents the
geographical distribution of different soil types and soil properties (such as
soil pH, textures, organic matter content, horizon depths, etc.) within a
specific area. Typically, it is the outcome of a comprehensive soil survey
inventory. Soil maps are commonly used for purposes such as land
assessment, spatial planning, agricultural guidance, environmental
conservation and similar projects. Traditional soil maps generally provide a
general overview of soil distribution and are accompanied by a soil survey
report.
In recent times, many soil maps have been generated using digital soil
mapping techniques. These maps offer richer context and higher spatial
detail compared to traditional soil maps. Soil maps created through
geostatistical methods also include an estimate of model uncertainty.
It's important to note that soil maps are essentially visual representations
of soil resource inventories, typically stored in a Soil Information System
(SIS). The major component of an SIS is a Soil Geographical Database,
which contains complete and consistent gridded or vector soil property and
class maps for the entire area of interest. An SIS also includes accompanying
reports, user manuals and metadata. Typically, an SIS combines polygon and
point maps linked with attribute tables for profile observations, soil mapping
units and soil classes. Various elements of an SIS can be manipulated and
visualized in relation to spatial reference (grids or polygons). For instance,
soil profiles can be used to predict different chemical and physical soil
properties spatially. In the case of pedometric mapping, both predictions and
simulations (2D or 3D, including geographic location and soil depth) of
values are visualized and employed for GIS modeling.
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Digital Soil Mapping (DSM) in soil science, also known as predictive
soil mapping or pedometric mapping, represents the computer-assisted
generation of digital maps that depict soil types and soil properties. Soil
mapping, as a broader concept, entails the creation and population of spatial
soil information by utilizing field and laboratory observational methods, in
conjunction with spatial and non-spatial soil inference systems.
The International Working Group on Digital Soil Mapping (WG-DSM)
defines digital soil mapping as "the process of generating geographically
referenced soil databases at a specific resolution. This is achieved by
employing field and laboratory observation methods in combination with
environmental data, all through quantitative relationships."
DSM can draw upon traditional soil mapping methods, but it is
recognized as distinct from them. Traditional soil mapping involves the
manual delineation of soil boundaries by field soil scientists. While the
resulting soil maps (typically in paper format) may be digitized or
boundaries may be drawn using field computers, both traditional,
knowledge-based approaches and technology-driven soil mapping
frameworks are essentially digital. However, Digital Soil Mapping is
distinguished by its extensive reliance on the following elements:
1. Technological advancements, encompassing GPS receivers, field
scanners, and remote sensing technologies.
2. Computational progress, involving geostatistical interpolation and
inference algorithms, Geographic Information Systems (GIS),
digital elevation models and data mining techniques.
In the realm of digital soil mapping, semi-automated methods and
technologies are employed to gather, process, and present data on soils and
related information, ultimately resulting in cost-effective outcomes. Outputs
from data-driven or statistical soil mapping methods are typically evaluated
for accuracy and uncertainty and can be readily updated with the arrival of
new information. Digital Soil Mapping tries to overcome some of the
drawbacks of the traditional soil maps that are often only focused on
delineating soil-classes i.e. soil types. Such traditional soil maps:
• Do not furnish data for modeling soil condition dynamics.
• Lack of adaptability for quantitative investigations into soil
functionality.
Crop scouting: Precision technology has revolutionized the practice of
crop scouting, which involves closely monitoring the development of crops
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throughout the growing season to detect and address issues that could impact
yield. Traditional methods of crop scouting included manual observations
while walking through fields, maintaining field notes, collecting plant
samples using tools like pocket knives and bags, and using hand
magnification lenses for close-up assessments.
Crop scouting is essential at various stages of a crop's life cycle. Prior to
planting, it helps identify weed populations, their types, and growth stages.
During planting, scouting provides data to determine the appropriate seed
depth and rate, as well as early signs of seed treatment effectiveness. After
planting, frequent scouting detects damaged seeds, early pest infestations,
and other potential problems. As crops grow and establish, ongoing scouting
prevents weed and pest damage and assesses the performance of post-spray
pesticides and fertilizers. Regular scouting also reveals issues like pest
outbreaks and soil moisture problems, allowing farmers to take corrective
measures and optimize crop efficiency.
Precision agriculture technologies have significantly enhanced crop
scouting. Mobile apps designed for tablets and smartphones have replaced
traditional field notebooks, enabling farmers to maintain accurate field logs
and compare data across years and different field areas. Global Positioning
Systems (GPS) and unmanned aerial vehicles (UAVs) have eliminated the
need for manual field walks. These technologies provide farmers with
valuable information that is often invisible to the naked eye and allow for
precise pinpointing of areas requiring attention.
GPS use in crop scouting
Global positioning systems (GPS) play a pivotal role in advancing
precision agriculture, particularly in the context of crop scouting. In
traditional crop scouting methods, farmers had to rely on memory and
manual note-taking to track their scouting locations. However, the
integration of GPS technology has revolutionized this process, offering
farmers the ability to record their precise locations within a one-foot margin.
This level of accuracy empowers farmers to make detailed observations and
pinpoint specific areas where issues such as pest infestations or unfavorable
soil conditions are encountered. Leveraging the precision of GPS, farmers
can effectively and accurately address threats identified within their fields.
Precision maps
Precision maps are increasingly invaluable tools in the realm of
precision agriculture, gaining widespread adoption in the agricultural sector.
These maps serve as indispensable aids for farmers, offering them detailed
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insights into specific field locations. A precision map is essentially a
representation of georeferenced data that furnishes information pertaining to
precise positions within a field. It encompasses essential data on factors such
as crop moisture levels, soil nutrient content, crop yields, and more. The
underlying mechanism of precision maps relies on the integration of diverse
physical sensors in conjunction with GPS data to analyze variables like crop
or soil moisture and crop yield.
The utility of precision maps lies in their capacity to enable farmers to
accurately pinpoint areas requiring attention, whether due to low crop yields
or inadequate moisture levels. This information empowers farmers to take
targeted actions accordingly. Importantly, precision maps also yield cost-
saving benefits for farmers by minimizing overspray. By harnessing
precision maps, farmers can optimize their resource allocation, ensuring that
pesticides, fertilizers, or seed replanting are applied only where necessary.
This not only leads to financial savings but also contributes to environmental
sustainability.
Precision maps can assume various forms. They encompass any map
that presents data in a georeferenced manner. For instance, while Unmanned
Aerial Vehicles (UAVs) can capture field imagery and provide a range of
information, they do not qualify as precision maps due to their inherent lack
of precision. In contrast, some combine harvesters equipped with appropriate
technology can gather highly specific data pertinent to individual points
within a field, as opposed to the field as a whole.
Soil maps
Geo-referenced soil maps are collected through various methods, with
the goal of obtaining accurate information about soil characteristics. One
approach involves dividing the field into a grid pattern and collecting soil
samples from each individual grid block. The level of accuracy in the
resulting soil map increases with the number of grid blocks and samples
taken. Another method involves sampling the field in zones determined by
previous data sources, such as yield maps, topography, or other precision
maps. In both cases, the soil samples are geo-referenced, meaning their
locations are recorded. This ensures that specific samples represent the soil
fertility levels in specific zones within the field.
The geo-referenced map created from these soil samples serves as a
valuable resource for cross-referencing with other precision maps, enabling
the assessment of nutrient levels, potential crop yields and various other
types of information. These maps can also be integrated with other precision
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agriculture technologies like Variable Rate Technologies (VRT), which are
utilized in equipment such as planters, sprayers, or spreaders. These
technologies use GPS coordinates from soil maps and other precision maps
to accurately dispense the right amount of agricultural products. This
approach prevents excessive seeding, fertilization, or pesticide application by
adjusting product rates based on the specific needs of different areas within
the field.
Yield maps
A yield map primarily focuses on crop productivity, specifically
highlighting variations in yield across different areas of a field. It serves as a
visual tool that assists farmers in understanding the relationships between
crop yield and various field conditions. To maximize its effectiveness, a
yield map is best utilized when integrated with a variety of other maps,
providing farmers with a comprehensive view of multiple types of
information that is corroborated by diverse data sources.
Much like soil maps and various other precision maps, yield maps can
be paired with advanced technologies like Variable Rate Technology (VRT)
to further support farmers during tasks such as harvesting, seed planting,
fertilizer application, or pesticide spraying. These two map types, although
prominent, represent just a portion of the broader category of precision
maps. Precision maps encompass a range of tools that collectively empower
farmers with essential decision-making information.
Tools and equipment needed for precision mapping
To create precise precision maps, farmers require an array of tools and
equipment, including sensors that are integrated into their farming
machinery. These pieces of equipment play a crucial role in assessing
various aspects of crop and soil conditions, offering valuable insights to
farmers. Here are some common tools used for precision mapping:
Grain moisture sensor: This sensor is responsible for detecting
moisture levels in grains, helping farmers determine the irrigation needs of
specific crop areas.
GPS antenna: The GPS antenna receives signals from global
positioning satellites, allowing for the recording of precise locations in the
field.
Grain flow sensor: This sensor aids in measuring the volume of
harvested grain.
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GPS receiver and yield monitor: Working in tandem, the Yield
Monitor and GPS receiver gather data collected by the sensors, centralizing
and geo-referencing the information.
Grain elevator speed sensor: Similar to the grain flow sensor, this
device collects data related to grain flow measurements. Using both sensors
can enhance measurement accuracy.
Site-specific input application
Site-specific management involves identifying and quantifying
variations within fields, documenting these discrepancies at specific
positions, and using this data to inform adjustments in agricultural practices
or resource inputs. This approach shifts away from applying uniform
management techniques across an entire field and instead tailors
management practices to specific areas within it.
To implement site-specific farming effectively, a producer must possess
the following capabilities:
Accurately determine location: Utilizing GPS technology, particularly
Differential GPS, ensures precise location data for the application of inputs.
Collect Data at Specific Locations: Information pertaining to various
locations within fields can be acquired either through sensor technology or
by sampling. While using sensors is generally the more straightforward
method, in some cases, data on specific inputs like crop nutrient
requirements may be better assessed through sampling. Commercially
available sensors include:
In summary, site-specific management involves pinpointing variations
within fields, documenting them accurately, and tailoring agricultural
practices accordingly. This approach deploys technology like GPS and
sensors to optimize resource allocation and improve overall crop
management.
• Yield monitors
• Soil electrical conductivity or electro-magnetic sensors
• Remote imagery, including satellite images, aerial photography and
hand-held active sensors
• Soil compaction sensors
• On-the-go soil pH (alkalinity or acidity) sensors
Site specific application: Variable-rate controllers are versatile tools
capable of managing various inputs that require site-specific adjustments.
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These inputs encompass liquid materials like fertilizers and manure, dry
materials such as fertilizers and manure, anhydrous ammonia, seeds,
agricultural chemicals, and starter fertilizers applied by planters. Existing
flow-monitoring consoles can also be adapted to regulate the application of
materials at specific sites. The data-input device can range from a compact
personal digital assistant (PDA) to a laptop computer. Variable-rate
application equipment can vary in size, ranging from large commercial
fertilizer applicators to smaller, more personalized variable-rate seeders or
anhydrous ammonia applicators. Many controller consoles today are
designed to be compatible with multiple application devices. Consulting
equipment manufacturers to determine the most suitable console for specific
application requirements is advisable. Additionally, many companies have
on-staff site-specific experts who can assist growers in selecting the
appropriate tools for successful site-specific farming.
Weed management
Site-specific weed control technologies consist of three essential
components:
A weed sensing system: This system identifies, locates, and measures
parameters of both crops and weeds. An automated weed sensing system is
critical for optimizing herbicide use in site-specific weed management. A
sensing platform can be employed to map weed infestations and create
treatment maps using a weed management model before implementing weed
control measures.
A weed management model: This model utilizes knowledge and data
related to crop-weed competition, population dynamics of weeds, the
biological effectiveness of control methods, decision-making algorithms, and
treatment optimization based on weed species density and composition,
economic objectives, and environmental constraints.
A precision weed control implement: This implement could be a
sprayer with individually controllable boom sections or a series of adjustable
nozzles that enable the spatially variable application of herbicides.
Insect-pest and disease management
In the realm of crop protection, insect pests and diseases pose substantial
challenges, prompting ongoing advancements in precision farming
technology. One such modern innovation involves pest detection sensors
designed to identify the presence of diseases and insect pests in crops. These
sensors effectively provide real-time data directly from the field. When it
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comes to accurate insect pest detection, farmers have access to a range of
sensors, each varying in complexity and operational principles. Some of the
most common sensor types are:
1. Low-power image sensors: The low-power image sensor
represents a wireless, autonomous monitoring system that relies on
an affordable image sensor. Positioned within an individual trap,
this wireless sensor periodically captures images of the trap's
contents and transmits them remotely to a central control station.
These transmitted images are subsequently utilized to calculate the
pest count within each trap. By assessing the insect population
numbers, farmers can make informed decisions about when to
initiate crop protection measures and where within their fields these
measures should be applied. This sensor proves invaluable for
monitoring extensive agricultural areas while consuming minimal
energy. The utilization of low-power image sensors offers
numerous advantages in farm production Some of them are:
• Significant reduction of pest monitoring costs
• No human intervention in the field required
• Applicable for small and big areas
• Low maintenance cost
• Real-time insect pest monitoring
2. Acoustic sensors: An acoustic sensor is a device designed for
detecting insect pests by monitoring the sounds they produce.
Here's how it operates: Wireless sensor nodes are strategically
placed throughout the field, all connected to a central base station.
When the noise level generated by the insect pests surpasses a
predefined threshold, one of the sensors sends this information to a
computer in the control room. The computer then precisely
identifies the area affected by the infestation. These sensors are
highly effective in identifying infestations at an early stage, thereby
significantly reducing potential crop damage. They are particularly
valuable for monitoring expansive field areas while maintaining
minimal energy consumption.
Another approach to monitoring insect pest presence involves sensors
designed for measuring the Leaf Area Index (LAI). Insects often damage
leaves, leading to the loss of chlorophyll and a reduction in total leaf area.
This diminishes the plant's ability to photosynthesize. By measuring the leaf
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area index, these sensors can detect insect attacks in their early stages and
alert farmers to take appropriate actions promptly. These sensors utilize
radiation measurements and other parameters to calculate the leaf area index
accurately in real-time, directly in the field. Additionally, this type of sensor
finds application in the detection of crop diseases.
3. Sensors for early crop disease detection: Crop diseases pose a
significant threat to global food security, as they can lead to
substantial yield losses if not promptly and effectively managed.
Therefore, disease protection stands as a paramount responsibility
for farmers. To mitigate these risks, modern farming practices
employ a combination of methods for safeguarding crops against
diseases. These methods encompass both direct and indirect
approaches to disease identification.
Direct detection methods are primarily laboratory-based techniques for
disease identification. These include well-known methods such as
polymerase chain reaction (PCR), immunofluorescence (IF), fluorescence in-
situ hybridization (FISH), enzyme-linked immunosorbent assay (ELISA),
flow cytometry (FCM) and gas chromatography-mass spectrometry (GC-
MS). While these techniques provide precise data, they are not suitable for
on-field disease detection.
In contrast, indirect methods are employed directly in the field. These
methods rely on assessing plant stress levels and plant volatility to identify
both biotic and abiotic stresses, including pathogenic diseases in crops.
These indirect methods involve the use of optical sensors that leverage
thermography, fluorescence imaging and hyperspectral techniques to predict
the occurrence of plant diseases.
In essence, the combination of direct and indirect methods empowers
farmers to proactively protect their crops against diseases by enabling early
detection and targeted interventions.
The future of pest detection sensors
In addition to the methods mentioned earlier, there exists a wide array of
sensors that can be employed for the detection of crop diseases and insect
pests, utilizing various signals such as electrical, chemical, electrochemical,
optical, magnetic, or vibrational cues. However, the field of agricultural
technology is rapidly evolving, with ongoing development of novel sensors
aimed at supporting early pest identification. These advanced sensors rely on
bio-recognition elements like DNA/RNA, antibodies and enzymes to
enhance their precision and effectiveness.
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As the world strives to produce sufficient food to feed a growing
population and ensure a sustainable future, farmers require every available
tool to optimize their farmland's productivity. Utilizing sensors in crop
production plays a pivotal role in achieving this goal. Access to real-time
field data simplifies farming practices, safeguards harvests, and enhances
yields, all of which contribute to environmental protection.
Conclusion
Precision agriculture involves the incorporation of modern technologies
such as Geographic Information Systems (GIS), Global Positioning Systems
(GPS) and Remote Sensing (RS) into farming practices. This shift allows
farmers to manage variations within their fields, optimizing cost-
effectiveness rather than applying traditional blanket treatments. Variable
Rate Technology (VRT), now integrated into farm equipment like fertilizer
and pesticide applicators and yield monitors, has seen rapid development,
driving the growth of precision agriculture. Tailoring management to
specific locations enables input reduction while maximizing yields, a
compelling prospect for farmers. Simultaneously, reduced input application
decreases fertilizer and pesticide runoff, positively impacting the
environment within the agricultural ecosystem. Remote sensing plays a
crucial role in various precision agriculture applications, including assessing
soil fertility and moisture levels before planting, monitoring crop growth and
stress (crop scouting) and predicting yields. This data aids farmers in making
informed decisions. Although precision agriculture has gained rapid
acceptance and expansion, several essential prerequisites are required for its
full development and adoption. These include ongoing research and
development of algorithms for correcting remote sensing data, extracting
valuable information and ensuring access to timely and cost-effective remote
sensing data or value-added products. Additionally, user-friendly decision
support systems that seamlessly integrate GIS, GPS and RS technologies are
necessary. A subsequent training and technology transfer program is also
vital to expedite the acceptance and implementation of this technology in the
agricultural sector.
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