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122 views103 pagesIt in Agriculture
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/ 103
ITIN
AGRICULTURAL
SYSTEM
Dr. C.R.BALAMURUGAN
Professor
Department of Electrical and Electronics Engineering
ER. PERUMAL MANIMEKALAICOLLEGE OF ENGINEERING
Koneripall Hosur 635117.
Mrs. N.RAMADEVE
Assistant Professor
Department of Elettical and Electronics Engineering
GRTINSTITUTEOF ENGINEERING AND TECHNOLOGY
Tiuttani-631 208
r.S.SATHYA
Associate Professor
Department of Artificial intelligence and Data Science
(GRT INSTITUTE OF ENGINEERING AND TECHNOLOGY
Tiruttani-631 209
Macnus PuBLicaTIoNs UNITI
UNITIE
UNIT
SYLLABUS
PRECISION FARMING
Precision agriculture and agricultural management —
Ground based sensors, Remote sensing, GPS, GIS and
‘mapping software, Yield mapping systems, Crop
production modeling
ENVIRONMENT CONTROL SYSTEMS
‘Aiicial light systems, management of crop growth
in greenhouses, simulation of CO, consumption in
‘greenhouses, online measurement of plant growth in
the greenhouse, models of plant production and expert
systems in horticulture.
AGRICULTURALSYSTEMS MANAGEMENT
Agricultural systems - managerial overview,
Reliability of agricultural systems, Simulation of crop
growth and field operations, Optimizing the use of
resources, Linear programming, Project scheduling,
Arica intelligence and decision support systems.
WEATHER PREDICTION MODELS
Importance of climate variability and seasonal
forecasting, Understanding and predicting word's
climate system, Global climatic models nd their
potenial for seasonal climate forecasting, General
Systems approach to applying seasonal climate
freer.
E-GOVERNANCE INAGRICULTURAL
SYSTEMS
Expertsstems, decision suppor systems, Agricultural
and biological databases, e-commerce, ebusiness
systems & application, Technology enhance earning
systems and solutions, elearning, Rural development
tnd information society
CONTENTS
UNITI_ PRECISION FARMING
1. Introduction rr
1.1 Precision agriculture and agriculture management... 1.4
LLL Precision Agricultue se. 16
1.1.2 Agricultural Management . 18
1.2 Ground-based sensors 1
13 Remote sensing 123
14 GPS, GIS and mapping software 134
1.4.1 Global Positioning System (GPS) 134
14.2 Geographic Information System (GIS)... 1.37
1.43 Mapping Sofware, a 142
1.5 Yield mapping systems 1146
15.1 Benefits of Yield Maori Systems, 149
1.6 Crop production modeling... 1.50
‘Two marks Question and Answer. 158
PART B & C Questions 1.83
UNITH ENVIRONMENT CONTROL SYSTEMS
2.1 Aificial light systems... 24
2.1.1 Artificial light systems in maechse
agriculture ea,
2412 Need for Artificial light systems in
ABFICUITE oe 1-210
22 Greenhouse pug
22:1 Management of crop growth in greenhouses. 2.14
23. Simulation of CO, consumption in greenhouses.....2.16
24 On-line measurement of plant growth in the
sreenhouse : 2.29
25 Horticulture 231
25.1 Modern horticulture. 2.60
252 Model of pat prs a expert systems in
horticulture... 271 ~TTwo Mark Question and AnsWer8 vos 2.88
PART B & C Questions 2.103
UNITIT —_ AGRICULTURALSYSTEMS MANAGEMENT
3.1. Agricultural Systems 31
3.1.1 Farm Management Software 32
3.1.2 ToT and Sensor Technol0gy rcnnsnnnnnnan 32
3.13 Drones and UAVs 32
3.14 Blockchain in Supply Chin Management »...3.2
3.1.5 Mobile Applications 33
3.1.6 Machine Learning and AT... ccrosnnnnen 33
3.1.7 E-commerce Platforms. 33
3.1.8 Robotic Farming rn 33
3.19 Weather Forecasting and Climate
Data Analysis... 33
3.2 Managerial overview of Agricultural Systems... 34
3.2.1 Reliability of Agricultural Systems.
3.3 Simulation of Crop Growth and Field Operations... 3.10
3.3.1 Types of Crop Simulation Model i TT seu 3.13
3.4 Agricultural Systems Management
3.4.1 Key components of agricultural systems
management include... 3.17
3.5 Optimizing the use of Resources. 3.19
35.1 Precision Agriculture s 3.20
35.2 Crop Rotation and Diversification 3.20
3.53 Water Management
354° Energy Efficiency
3.5.5. Integrated Pest Management (IPM) .
35.6 Optimized Fertilizer Use..
35.7 Labor Management.
3.5.8 Financial Management
35.9 Waste Management.
3.5.10 Continuous Improvement
3.6 Linear Programming in agriculture system
‘management
vil
37
38
39
3.6.1 Linear programming is applied in agricultural
system management eo 23
Project Scheduling In Agricultural System
Management ee
37.1 Identify Project Objectives sce 3.26
3.7.2. Breakdown of Activities. 3.26
3.7.3 Sequence Activities 3.26
3.7.4 Estimate Activity Durations. 3.26
3.75. Resource Allocation 3.27
3.7.6 Develop the Schedule 27
37.7 Critical Path Analysis 3.21
3.7.8 Resource Leveling : 27
3.7.9 Contingency Planning .cnatcnnninsene3.2T
3.7.10 Monitor and Control... 3.28
‘Anificial intelligence in agricultural system
3.28
‘management
3.81 Applications of Alin agricultural system
‘management ..
Decionsepport yes i agscural ym
‘management
3.9.1 Crop Planning and Management
3.9.2 _Inigation Scheduling and Water Management
3.9.3 Pest and Disease Management.
3.94. Nutrient Management and Soil Health.
3.9.5 Weather and Climate Risk Management.
3.9.6 Market Analysis and Risk Assessment...
3.9.7 Resource Allocation and Financial Planning.3.35,
3.9.8 Advisory Services and Extension Support... 3.35,
3.10 Significance of Agriculture Management
Systems
3.10.1 Productive Farm Management ~ A Necessity for
Agribusiness Success be 3.36
3.102 Sinieat Opporuies in Understanding te
Farmland and Planning..
37 10.3. Eicient Farm Optimization tkrough Analysis 3.37
3.10.4 Better Resource Evaluation and Risk
‘Management... 3.38
3.10.5 Cost and Profit Analysis 3.38
3.10.6 Inventory and Logistics Management 3:39
3.10.7 Impact of Emerging Technology on Agriculture
‘Management Systems... 3.39
3.11 Types of decision support systems in saree
systems management 340
3.11.1 Crop Management DSS... 340
3.11.2 Itigation DSS 341
3.11.3 Livestock Management DSS. 3.41
3.114 Market Analysis DSS ... 342
3.11.5. Weather and Climate DSS. 3.42
3.11.6 Nutrient Management DSS .. 3.42
3.11.7 Precision Agriculture DSS.. 3.42
3.11.8 Risk Management DSS. 3.43
‘Two Marks Question and Answers... 3.4
PART B & C Questions . : 338
UNITIV WEATHER PREDICTION MODELS
4.1 Weather prediction models. Al
4.11 Numerical Weather Prediction (NWP) Models 4.1
4.1.2, Global Climate Models (GCMs) 42
4.1.3, Regional Climate Models (RCMS) wsrvnon 4.2
4.14 Statistical Forecasting Models. 43
4.15. Ensemble Forecasting Systems. 43
4.1.6. Hybrid Forecasting Approaches wcwnrwen4 3
4.2 Importance of climate variability. 44
42.1 Crop Planning and Management .vecnnion 4 S
4.22 Resource Allocation and Optimization v0.4.5
423. Risk Management eS
42.4 Precision Agriculture cininnennninnennd S
425 Market Dynamics. 46
43
44
45
46
4.2.6 Policy and Regulation Compliance. 46
42.7 Relationship between climate variability and the
agricultural system... 47
428 Climate Variability Influence: Around the
agricultural system, depict various elements of
climate variability, 47
429 Arrows Showing Inter2et0R scence 4B
4.2.10 Effects and Adaptations 48
42.11 Captions and Labels... 49
4.2.12 Color Coding on : 49
Importance of seasonal Forecasting. 49
43.1 Crop Planning and Management cme 49
43.2. Water Management 4.10
43.3 Pest and Disease Management... 4.10
4.3.4 Resource Allocation . 4.10
43.5 Market Planningand Risk Management ...4.10
4.3.6 Policy Compliance and Stakeholder
Engagement, Add
43.7 Long-Term Planning and Adaptation... 4.11
Understanding and predicting world’s climate system 4.11
44.1 Data Collection and Integration ven AAD
44.2 Climate Modeling and Prediction. A 13
443. Decision Support Systems... 413
4.44 Adaptive Management and Resilience Building .14
Global climatic models 0 414
45.1. Climate Projection and Scenario Analysis... 4.15
45.2 Crop Modeling and Yield Forecasting ....... 4.16
4.53. Risk Assessment and Management 4.16
4.5.4 Adaptation Planning and Resilience Building4.16
455 Policy Development and Decision Support. 4.16
4.5.6 Capacity Building and Knowledge Sharing... 4.17
Global climatic models and their potential for seasonal
climate forecasting 4.17
4.6.1 Understanding Climate Drivers 4.18 47
48
49
4.6.2. Generating Seasonal Forecasts. 418
4.63 Assessing Climate Risks 418
4.64 Optimizing Crop Management ay
4.65. Supporting Decision-Making s..unosnnn 419
4.66 Promoting Climate-Smart Agriculture un. 4.19
4.2.7 Improving Long-Term Planning 4.19
Seasonal climate forecasting 420
4.7.1 Data Collection and Processing 4.21
47.2. Climate Forecasting Models 421
4.73. Downscaling and Bias Correction enon 421
4.7.4. Forecast Verification and Evaluation 421
4.7.S Stakeholder Engagement and Communication. 4.22
4.7.6 Decision Support Systems (DSS) .....nson 4.22
4.7.7 Adaptive Management Strategies ou. 4.22
47.8 Capacity Building and Training swoon 4.22
4.7.9 Monitoring and Evaluation sn 23,
4.7.10 Feedback and lerative Improvement 0.04.23
General systems approach to applying seasonal climate
forecasts 423
48:1 Data Collection and Forecast Entegration «4.24
48.2 Stakeholder Engagement and Needs
‘Assessment. 424
48.3 Decision Support Tools and Platforms econ 4.25
48.4 Crop Planning and Management 425
4.85 Water Resource Management... 4.25
4.8.6 Risk Assessment and Mitigation ooowmeuun4 25
48.7 Market Analysis and Planning 426
488 Capacity Building and Knowledge Sharing... 4.26
Implications of Agricultural Systems... 421
49.1" Increased Efficiency 427
49.2. Improved Productivity adalat AZT
49.3 Environmental Sustainability 428
494 Enhanced Decision-Making... 428
4.10
4a
4.12
43
49.5 Risk Management wont 428
4.9.6 Labor Savings... 428
49.7 Data Management and Integration 429
49.8 Market Opportunities 429
‘The Management Factors in Precision Agriculture
system 8 x geste 29
4.10.1 Data Collection and Integration nanan 4.30
4.10.2. Data Analysis and Interpretation 430
4.10.3 Decision Support Systems c.ccmsninnnnin 430
4.10.4 Technology Adoption and Training. 431
4.10.5 Field Mapping and Zoning 431
4.10.6 Variable Rate Application (VRA).. 431
4.10.7 Equipment Calibration and Maintenance ....4.31
4.10.8 Continuous Monitoring and Evaluation on. 4.32
4.10.9 Integration with Farm Management Practices4.32
4.10.10Compliance and Regulatory Considerations .4.32
Compote Siegal ease -422
4.11.1. Crop Management. 433,
4.112 Livestock Management A 33
4.113 Soil and Water Conservation ronan 433
4.114 Pest and Disease Control. 433
4.1155 Technology and Machinery Management... 434
4.11.6 Financial Management. 434
4.11.7 Market Access and Marketing Strategies... 4.34
4.118 Environmental Sustainability 434
4.119 Regulatory Compliance and Risk Management 4.34
Advantages of seasonal forecasting 435
4.12.1 Improved Agricultural Panning ono 433
4.122 Water Resource Management 4.35
4.123 Energy Sector Planing. 436
4.124 Disaster Preparedness and Mitigation... 436
4.12.5 Beonomic Planning and Risk Management... 4.36
‘Various climatic factors that influence climate in IT
agriculture systems 437
‘a ‘Two Marks Question and Answers.
PART B & C Questions
UNITY »-cOVERAANCEINAGHICUFAURAL SYSTEMS
5.
32
53
34
4.13.1 Temperature ..
4.13.2 Precipitation
4.133 Humidity
4.13.4 Wind
4,135 Solar Radiation
4.13.6 Extreme Weather Events ..
4.13.7 Climate Change ..
aii
E-Governance A
SII Several ways e-governance is applied in
agricultural systems. ere
Expert systems 53
52.1. Crop Management aon
522 Disease and Pest entiation a
‘5.23 Soil Health Management. zu
52.4, Precision Agia. pass
5.25 Livestock Management =
52.6 Market Ansys and Deisow mang 55
5.2.7 Knowledge Transfer rc ae
Decision Support Systems (Ds
53.1 Crop Planning ad Management «nm 56
53.2. Precision Agriculture oa
5.3.3 Pest and Disease Management a
5.3.4 Water Resource Management. a
535 Market Anais and Risk Management 0 58
5.36 Policy Planning and Evaluation... ae
Agricultal and biologie databases 5
5.4.1 Genetic Resources Databases
9
5.4.2. Crop Databases 2
5.4.3 Livestock Databases
‘5.44 Plant Pathogen Databases
38
56
37
58
5.4.5 Entomological Databases.
3.10
54.6 Ecological Databases 5.10
5.4.7 Agricultural Statistics Databases 5.10
E-commerce , sal
5.5.1 Online Marketplaces 5.12
55.2. Agr-inpue Sales 5.12
55.3 Financial Services 5.12
5.54 Market Information Services 5.13
55.5 Logistics and Transportation. 5.13,
55.6 Value-added Services 5.13
5.5.7 Quality Assurance and Traceability, 5.4
E-Business Systems & Applications... 5.14
5.6.1 Supply Chain Management (SCM) Systems 5.15
5.6.2 Enterprise Resource Planning (ERP) Systems . 5.15
5.63. Farm Management Information Systems... 5.16
5.64 Precision Agriculture Technologies su... 5.16
5.65 E-Marketplaces and Trading Platforms... 5.16
5.6.6 E-Extension Serviee8 wuss 5.17
5.6.7 Traceability and Certification Systems... 5.17
5.68. E-Leaming and Training Platforms . 5.17
‘Technology Learning systems and solutions au... 5.18
S71 Data Analytics and AL...
5.7.2 Remote Sensing
5.7.3. Precision Farming.
5.74 Internet of Things (oT)
575. Blockchain, i"
5.7.6 Farm Management Software .
5.2.7. Mobile Apps
518. Agti-Tech Startups and Innovation Hubs... 5.19
Enhanced Learning Systems in IT Agricultural
Systems 5.20
5.8.1 Machine Leaming and Predictive Analytics 5.21
5.8.2 Decision Support Systems (DSS) 521 59
5.10
3.
5.83. Digital Twins... om 5:21
584 Smart Sensors and loT Devices 521
5.85 Robotic Automation 521
$86 Visual Realty (VR) and Avgmented
Reality (AR) i522
5.8.7 Collaborative Learning Platforms 5.22
588 Feedback Loops and Cotinons Improvement 5:22
What is E-Learning?. 523
59.1 Why is E-Learning important? 5.24
‘5.9.2 Advantages of e-learning 5.24
5.9.3 List of three pillars of cohort learning... 5.25
5.9.8 Disadvantages of e-leaming..u:sor-nnw 5:26
59.8 E-Learning ats 5.27
Raral Development and Infomation Seu... 530
5.10.1. Access to Information and Communication
‘Technologies (ICTs) eat
5.10.2. Digital Inclusion .. 531
5.10.3. E-Government Services: 531
5:10. Agricultural Technology Adoption -o-- 51
5.10.5 Financial Inclusion Hait.532
'5.10.6 Community Empowerment nnsssininnnee 5.32
5.32
533
5.34
334
5.10.7 Privacy Protection
Information security in aricohurl systems
S.1L.1 Risk Assessment :
5.11.2 Data Encryption.
5.11.3 Access Contol.. 5.34
5.11.4 Secure Communication $34
5.115 Regular Updates and Patch Management ...5.34
5.11.6 Physical Security Measures 35
5.11.7 Training and Awareness 535
5.11.8 Data Backup and Recovery. 5.35
5.11.9 Vendor Security Assessment 5.35
5.11,10Compliance with Regulations w.s.0.r-son 5.35
5.12 E-Governance Plan in IT agriculture systems
5.36
5.12.1 Assessment of Current Agricultural Systems 5.36
5.12.2 Stakeholder Engagement...
5.12.3 Infrastructure Development
5.124 Digital Platforms Development.
5.12.5 Data Management and Analytics
5.12.6 E-Government Services
5.12.7 Capacity Building and Training.
5.12.8 Interoperability and Integration
5.12.9 Regulatory Framework and Policy Support.
5.12.10Monitoring and Evaluation.
5.12.11 Sustainability and Scalability
5.12.12Public Awareness and Outreach.
‘Two Marks Question and Answers.
PART B & C Questions ..
‘Model Question Papers
5.38
5.36
5.36
5.36
337
337
537
537
337
38
5.38
5.39
538 Unit
Precision agriculture and agricultural management - Ground
based sensors, Remote sensing, GPS, GIS and mapping
software, Yield mapping systems, Crop production modeling,
1. INTRODUCTION 3
Inthe realm of agriculture, “IT” or Information Technology
plays a significant role in modernizing and optimizing various
aspects of the agricultural system. Here are som key areas where
IT is instrumental:
1. Precision Agriculture: IT tools such as GPS, GIS
(Geographic Information Systems), and remote
sensing technologies enable farmers to precisely
‘manage their fies, They can monitor soil conditions,
‘moisture levels, and crop health remotely, allowing
for targeted application of fertilizers, pesticides, and
water resources.
2, Farm Management Software: There is numerous
sofiware solutions tailored for farm management
‘These tools help farmers in planning, monitoring, and
‘analyzing various aspects of their operations including
inventory management, crop rotation, labor
scheduling, and financial tracking,
3. Market Information Systems: IT facilitates access
to market information, pricing trends, and commodity
exchanges. Farmers can make informed decisions ‘about when to sell their produce and where to find
the best markets, thereby maximizing their profits.
‘Weather Forecasting: Accurate weather forecasts are
‘crucial for agricultural planning and risk management.
IT allows farmers to access real-time weather data
‘and forecasts, helping them make decisions regarding
planting, harvesting, and irrigation scheduling,
Supply Chain Management: IT systems help
streamline the agricultural supply chain by facilitating
‘communication and coordination among farmers,
distributors, processors, and retailers. This improves
cfficiency, reduces waste, and ensures timely delivery
of agricultural products to consumers.
Drones and UAVs: Unmanned aerial vehicles (UAVS)
equipped with cameras and sensors are increasingly
used in agriculture for tasks such as crop monitoring,
pest detection, and mapping. IT enables the processing
‘and analysis of data collected by drones, providing
valuable insights for decision-making,
Internet of Things (IoT): IoT devices such as soil
moisture sensors, temperature gauges, and automated
inrigation systems are becoming more prevalent on
farms. These devices collect data in real-time,
allowing farmers to monitor and manage thei
‘operations remotely through connected platforms.
Blockchain Technology: Blockchain has the
potential to enhance transparency and traceability in
the agricultural supply chain. By recording
transactions and movements of agricultural products
‘ona decentralized ledger, blockchain technology can
help verify the authenticity and quality of produce,
as well as improve food safety standards.
‘Overall, IT solutions continue to revolutionize agriculture
by increasing productivity, optimizing resource utilization, and
improving decision-making processes throughout the
agricultural value chain. an
Fig: 1.2 Sonsors in Agreuture Fig: 4.4 (Artificial Inteligence) in Sol quality monltorn
1.1 PRECISION AGRICULTURE AND AGRICULTURE
‘MANAGEMENT
Precision agriculture and agricultural management are two
interconnected concepts that utilize technology and data-driven
approaches to optimize agricultural practices and improve
productivity, Precision agriculture refers to ereating potential
for substantial change in management and decision making in
agriculture, The word Potential in Precision agriculture tries to
censute various technologies and practices that will make up
tomorrow's precision agriculture are only emerging and
‘implemented and others are rejected today. The technically or
economically unfeasible agriculture can become feasible as result
of @ technological innovation occurring well outside the arena of
agricultural technology development or agricultural research.
The precise dimensions and characteristics ofthe precision
agriculture continue to evolve; the following features
characterize most precision agriculture applications in use are
under development
‘+ Data capture tends to be electronic, automated, and
relatively inexpensive
‘+ Data capture ean occur more frequently and in more
detail.
‘© Information, either captured as a part of field
‘operations or purchased externally, can be considered
separate input into the production operation. It is also
@ feature of integrated pest management and
sustainable agriculture concepts.
‘© Data interpretation and analysis can be more formal
and analytical
Scientific decision rules are applicable to actual
farming operations.
‘+ Implementation of the response can be more timely
and more site specific.
‘© Performance of alternative management systems can
bbe quantitatively evaluated. ‘The uncertainties associated with the rapid evolution of
information technologies and the dynamics of the process of
‘adopting precision agriculture acts as challenges to the success
of precision agriculture, The human decision making is more
likely to suffer bias and misinterpretation when (1) feedback
loops are long between the time the decision is made and the
‘outcome occurs and (2) cause/effect linkages are not simple.
‘These two characteristics apply to traditional erop production
settings.
+ Decroase input esses
‘Target uot 10
(refoeso uptake
‘tiles
Fig 45 Precision agriculture
1.1.1 Precision Agriculture
Precision agriculture (also known as precision farnting or
smart farming) involves the use of advanced technologies to
‘manage variability within fields and optimize erop production.
Key components of precision agriculture include:
Remote Sensing: Remote sensing technologies,
including satellite imagery, drones, and aerial
photography, provide detailed information about soil
conditions, crop health, moisture levels, and pest
infestations across large agricultural areas.
Global Positioning System (GPS): GPS technology
enables farmers to accurately map field boundaries,
tack machinery movements, and create geo
referenced data layers for analysis and decision-
making,
Geographic Information Systems (GIS): GIS
sofiware integrates spatial data from multiple sources
to create detailed maps and models of agricultural
landscapes. Farmers use GIS tools to identify areas
‘of high and low productivity, analyze soil variability,
‘and plan precision farming operations,
Variable Rate Technology (VRT): VRT allows
farmers to apply inputs such as fertilizers, pesticides,
‘and irigation water at variable rates based on spatial
‘variability within fields. By matching input application
rales to specific crop needs, farmers can. optimize
resource use and minimize environmental impact.
Precision Planting and Seeding: Precision planting
‘and seeding equipment use GPS and sensor
technology to precisely place seeds at optimal spacing
‘and depth, ensuring uniform crop emergence and
maximizing yield potential
Automated Machinery and Robotics: Advanced
machinery equipped with sensors, actuators, and
autonomous capabilities automate various farm tasks,
a ‘including planting, spraying, harvesting, and soil
cultivation. Roboties technologies, such as robotic
weeders and automated milking systems, improve
efficiency and reduce labor requirements.
‘+ Data Analytics and Decision Support Systems: Data
analytics tools and decision support systems analyze
large volumes of agronomic data to identify patterns,
trends, and insights that inform farm management
decisions, Machine learning algorithms can, predict
crop yields, detect pest outbreaks, and optimize
planting schedules based on historical and real:
data.
© Irrigation Management: Precision irrigation
systems, including drip irrigation and soil moisture
sensors, optimize water application by delivering the
right amount of water to crops precisely when and
where it is needed, This helps conserve water
resources and prevent water logging or drought stress.
‘Overall, precision agriculture enables farmers to make
informed decisions, optimize resource use, and increase
productivity while minimizing environmental impact. By
adopting precision agriculture technologies and practices,
farmers can achieve sustainable agricultural production and meet
the challenges of feeding a growing global population.
1.1.2 Agricultural Management
‘Agricultural management encompasses the planning,
organization, and control of farming operations to achieve
desired production goals whileinimizng risks and maximizing
profitably Key aspects of agricultural management include:
‘+ Crop Planning and Rotation: Agricultural managers
develop crop rotation schedules and planting plans
based on factors such as soil fertility, climate
conditions, market demand, and pest management
strategies. Crop rotation helps maintain soil health,
‘manage pests and diseases, and improve overall crop
yields.
Resource Management:
‘+ Land Management: Agricultural managers optimize
land use by selecting suitable crops, implementing
conservation practices, and minimizing soil erosion
‘and degradation,
+ Water Management: Efficient irrigation systems,
water conservation practices, and proper drainage
techniques are essential for managing water resources
‘effectively and sustaining crop production.
+ Nutrient Management; Agricultural managers
develop nutrient management plans to optimize
fertilizer use, prevent nutrient runoff, and maintain
soil fertility levels without causing environmental
harm,
Financial Management: Agricultural managers develop
budgets, track expenses, analyze financial performance,
‘and manage cash flow to ensure the economic viability of
farming operations. They may secure financing, manage
insurance policies, and invest in new technologies or
infrastructure to improve productivity and profitat
‘Risk Management: Agricultural managers identify and
mitigate risks associated with weather variability, market fluctuations, pest and disease outbreaks, and regulatory
changes. They may use insurance products, hedging
strategies, diversification of erops or markets, and
contingency plans to minimize financial losses and
‘operational disruptions.
‘Regulatory Compliance: Agricultural managers navigate
complex regulatory requirements related to food safety,
environmental protection, labor standards, and land use
‘regulations, They ensure compliance with local, state, and
federal laws while maintaining operational efficiency and
sustainability
«Market Analysis and Marketing Strategies: Agricultural
managers monitor market trends, analyze consumer
preferences, and develop marketing strategies to sell
‘agricultural products at competitive prices. They may
‘engage in direct marketing, contract farming, value-added
processing, or certification programs to differentiate their
products and capture additional value’along the supply
chai
+ Human Resource Management: Agricultural managers
oversee labor recruitment, training, scheduling, and
performance evaluation to ensure the efficient operation
‘of farming activities, They may also address labor welfare
issues, promote workplace safety, and foster a positive
‘work environment to attract and retain skilled workers.
«Technology Adoption and Innovation: Agricultural
managers explore new technologies, tools, and practices
to improve farm productivity, reduce costs, and enhance
sustainability, They may invest in precision agriculture
technologies, auton ation systems, genetic improvements,
and sustainable farming practices to stay competitive and
adapt to changing market and environmental conditions,
Effective agricultural management requires a holistic
approach that integrates agronomic knowledge, business
acumen, environmental stewardship, and social responsibilty
By implementing sound management practices, agricultural
‘managers can optimize resource use, enhance productivity, and
‘contribute to the long-term viability of farming operations.
1.2 GROUND-BASED SENSORS
Ground-based sensors are devices used in agriculture to
collect data related to soil, weather, erop health, and
environmental conditions. These sensors are deployed directly
in the field, providing real-time or periodic measurements that
help farmers make informed decisions about crop management,
itrigation, fertilization, and pest control. Development of
Ground-based sensing systems requires knowledge by research
‘on the soil and crop processes. Sensors offer opportunity to
automate the collection of soil, crop and pest data economically
than manually. VRT refers Variable-Rate Technology is a system
that allows machinery and equipment used in farming to work
at varying rates. That means the rate of application of an input
such as fertilizer, seed or pesticides changes across a field to
match the requirement of the crop at that specific location.
Improvements to VRT and crop modeling are expected to
advance rapidly with a higher spatial density of measured soil
and crop parameters. Sensors are needed that are fast, efficient,
‘and ean assess factors important to crop production,
‘Moran et al. (1996) concluded that the information from
ground-based sensors is needed for soil organic matter, soil
moisture, cation exchange capacity, nitrate nitrogen, compaction,
a
4 » sii te TR
soil texture, salinity level, weed detection, and érop residue
coverage, These parameters as well as soil pH, and availablity
Cf phosphorus and potassium cannot be ascertained by remote-
sensing technology. Moreover, the use of real-time ground-based
sensors provides the grower control over timing of data
acquisition not possible with satellite or aircraft sensing
techniques,
‘Sensors have been developed or are underway to measure
soil and crop conditions including soil organic matter, soil
‘moisture content, electrical conductivity, soil nutrient level, and
crop and weed reflectance. Continuous, real-time
electrochemical soil chemical constituent sensors are currently
Available for nitrate measurement and are dedicated to specific
application in corn side~dress applications. A real-time acoustic
soil texture sensor and a real-time soil compaction tester are
also under development. Some important real-time indexes may
be determined by their relationships to other variables rather
than by direct determination, Soil conductivity is appropriate
for concurrent real-time assays of salinity, soil moisture, organic
‘matter, cation exchange capacity, soil type’and soil texture.
Recently, this work was extended to non-saline soil methods in
‘combination with electrochemical constituent sensing which
Separates components of direct contact conductivity.
‘Conductivity component analysis is employed for georeferenced