IT IN AGRICULTURAL SYSTEMS (ITA)UNIT 1V ECE

Plant modeling is the process of creating a digital
replica of every process, piece of equipment, and
workflow within a plant.

TYPES OF MODELS
• Simple Models. ...
• Statistical Models. ...
• Mechanistic Models. ...
• Innovation Research Partnerships. ...
• Validation with USDA Data. ...
• University Research Studies.

SIMPLE MODEL
• The SIMPLE model simulates crop growth,
development, and yield using a daily time
step, with a few functions or equations that
account for the effect of daily temperature,
heat stress, rainfall, and atmospheric
CO2 concentration.

STATISTICAL MODEL
• statistical crop models developed based on
current. climatic conditions to project impacts
of changes in. mean and variability of
temperature and precipitation with different
levels of predictor-variable aggregation and
for different sample sizes of simulated

MECHANISTIC MODEL
• Conversely, the mechanistic model is an
aggregation of equations independently
estimated from small and diverse data sets
relating intermediate environmental and pest
variables (e.g. soil water potential and weed
seed germination).

INNOVATION AND RESEARCH
• Marker-assisted selection (MAS): This approach uses
molecular markers linked to specific traits to select
those traits in the breeding process. MAS can
accelerate the breeding process by reducing the time
and resources required for phenotypic selection.
• Genomic selection: This method involves using high-
throughput DNA sequencing to identify specific genes
and alleles associated with desired traits. These
genetic markers are used to predict the performance
of offspring and select desirable traits.

• Establishments using corporate data to
validate plant specific processes.

HORICULTURE
• Horticultural Building Systems are defined as
the instance where vegetation and an
architectural/architectonic system exist in a
mutually defined and intentionally designed
relationship that supports plant growth and an
architectonic concept.

• Propagation. Propagation, the controlled
perpetuation of plants, is the most basic of
horticultural practices. Its two objectives are
to achieve an increase in numbers and to
preserve the essential characteristics of the
plant.

• The use of vertical farming (growing low crops
in multiple layers, mostly inside buildings) and
urban farming (the growing of plants within
and around cities), combined with
technologies such as hydroponics, allows us to
make efficient use of space and reduce the
distance our food travels to get to consumers

EXPERT SYSTEMS
• In agriculture Expert System are capable of
integrating the perspectives of individual
desciplines such as plant pathology,
entomology, horticulture and agricultural
meteorology into a framework that best
address the type of ad hoc decision making
required of modern farmers.

• Now-a-days, expert system is widely used in
agriculture exclusively for diagnosing and
managing pests. These pest problems are
mainly dependent upon human experts for
their diagnosis and getting recovery.

UNIT IV
• Agricultural production system has been evolving
into a complex business system requiring the
accumulation and integration of knowledge and
information from many diverse sources.
• In order to remain competitive, the modern farmer
often relies on agricultural specialists and advisors
to get information for decision making.
• Unfortunately assistance of the agricultural expert
is not always available when the farmer needs it. In
order to alleviate this problem, expert systems
were identified as a powerful tool with extensive
potential in agriculture.

• An Expert System (ES), also called a Knowledge Based System
(KBS), is a computer program designed to simulate the problem-
solving behavior of an expert in a narrow do main or discipline.
• The expert system could be developed for decision-making and
location specific technology dissemination process. An expert
system is software that attempts to reproduce the performance
of one or more human experts
• Most commonly in a specific problem domain, and is a
traditional application and/or subfield of artificial intelligence.
• Expert systems helps in selection of crop or variety, diagnosis or
identification of pests, diseases and disorders and taking
valuable decisions on its management. The expert system which
developed earlier were more of text based and could be utilized
only by the extension officials and scientists

• IMPORTANCE OF EXPERT SYSTEM
The complexity of problems faced by the farmers
are yield loses, soil erosion, selection of crop,
increasing chemical pesticides cost, pest resistance,
diminishing market prices from international
competition and economic barriers hindering adoption
of farming strategies. Expert System are computer
program that are different from conventional
computer programs as they solve problems by
mimicking human reasoning process, relying on logic,
belief, rules of thumb opinion and experience

Decision Support Systems
• Decision Support Systems to Manage Irrigation
in Agriculture
The main advantages of using a DSS include
examination of multiple alternatives, better
understanding of the processes, identification
of unpredicted situations, enhanced
communication, cost effectiveness, and better
use of data and resources.

• Data-driven DSS. ...
• Model-driven DSS. ...
• Knowledge-driven DSS. ...
• Document-driven DSS. ...
• Communication-driven and group DSS. ...
• DSS database. ...
• DSS software system. ...
• DSS user interface.

• A typical Decision support systems has four
components: data management, model
management, knowledge management and
user interface management. The data
management component performs the
function of storing and maintaining the
information that you want your Decision
Support System to use.

• Decision support systems (DSSs) are used in agriculture
to collect and analyze data from a variety of sources
with the ultimate goal of providing end users with
insight into their critical decision-making process.
• In particular, in the agriculture domain, these systems
help farmers to solve complex issues related to crop
production. In this sense, DSSs are key elements of
modern agriculture. However, as these tools scale into
data-extensive, real-time monitoring systems, the goals
of these systems become more challenging
(information overload, system design, data collection).

AGRICULTURAL AND BIOLOGICAL DATABASE
• The World Crops Database is an agricultural database.
The word “agriculture” originates from the Latin word
agricultura, which is a combination of ager (the field)
and cultura (cultivation).
• The data that are collected from biological world are
called biological data. For example, DNA sequence data,
population data, genetical data, ecological data etc.
However, bioinformatics deals with biomolecule's
related data collected from scientific experiments,
published literatures and computational analyses.

DATA TYPES IN AGRICULTURE
• It includes data related to crop yields, weather
patterns, soil conditions, market prices, and
other relevant factors. This data is used to
make informed decisions, improve farming
practices, optimize resource allocation, and
enhance overall agricultural productivity

EXAMPLE OF A BIOLOGICAL DATABASE IN BIOINFORMATICS
• A few popular databases are GenBank from
NCBI (National Center for Biotechnology
Information), SwissProt from the Swiss
Institute of Bioinformatics and PIR from the
Protein Information Resource. GenBank:
GenBank (Genetic Sequence Databank) is one
of the fastest growing repositories of known
genetic sequences.

DISADVANTAGES OF BIOLOGICAL DATABASES
• In addition to the mentioned data errors and
integrity problems, there are other types of
obstacles that may face the biological databases
developers such as, redundant data, a steady
flux, spreading data from several databases,
insufficient information, incorrect links, and
unclear naming terms and annotations

PRIMARY BIOLOGICAL DATABASE
• Primary databases are populated with
experimentally derived data such as
nucleotide sequence, protein sequence or
macromolecular structure. Experimental
results are submitted directly into the
database by researchers

• A farmer database, also known as an
agricultural database, is a structured collection
of information that contains details about
farmers and their farming activities.

How do you collect data in agriculture?
• Sensors. In precision agriculture, many sensors
are used. For example, weather stations can
measure a whole range of features: air
temperature and humidity, wind direction and
strength, precipitation rate, soil density and
acidity. Sometimes we ask farmers for this
data, sometimes we measure these indicators
ourselves.

• Data farming is the process of using designed
computational experiments to “grow” data,
which can then be analyzed using statistical
and visualization techniques to obtain insight
into complex systems. These methods can be
applied to any computational model.

AGRICULTURAL DATABASE IN INDIA
• Departments of Agriculture/Land Records or
Directorate of Economics & Statistics as State
Agricultural Statistics Authority (SASA). SASAs
finalize State level estimates on the basis of
district-wise data on area, production and
yield.

IT IN AGRICULTURAL SYSTEMS (ITA)UNIT 1V ECE

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