Format: Live instructor-led online training via Zoom / Microsoft Teams

Pest and Disease Mapping Systems Training Course

The Pest and Disease Mapping Systems Training Course is designed to equip agricultural professionals, plant health specialists, agronomists, GIS analysts, extension officers, researchers, environmental scientists, and development practitioners with advanced knowledge and practical skills in monitoring, mapping, analyzing, and managing agricultural pests and diseases using modern geospatial technologies. Pest and disease outbreaks continue to pose significant threats to agricultural productivity, food security, biodiversity conservation, and rural livelihoods. Effective surveillance and spatial monitoring systems are essential for early detection, rapid response, risk assessment, and sustainable crop protection. This course provides comprehensive training in Geographic Information Systems (GIS), Remote Sensing, GPS technologies, mobile data collection, spatial epidemiology, and digital agriculture solutions for pest and disease management.

The training covers the complete pest and disease surveillance workflow, including field data collection, pest and disease identification, geospatial database development, outbreak mapping, risk assessment, disease spread modeling, environmental suitability analysis, crop health monitoring, and decision-support systems. Participants will gain practical experience in collecting, managing, analyzing, and visualizing agricultural health data using GIS software, satellite imagery, drones, GPS devices, and cloud-based monitoring platforms. Through practical exercises and real-world case studies, participants will learn how to transform spatial data into actionable information for crop protection and agricultural planning.

Participants will explore advanced applications such as early warning systems, pest distribution modeling, disease hotspot analysis, crop stress monitoring, climate-driven pest forecasting, integrated pest management (IPM), precision agriculture, and digital surveillance systems. The course also introduces emerging technologies including artificial intelligence, machine learning, Internet of Things (IoT), drone-based monitoring, predictive analytics, cloud computing, and smart agriculture platforms. Emphasis is placed on improving surveillance efficiency, strengthening outbreak preparedness, reducing crop losses, enhancing food security, and supporting sustainable agricultural production systems.

Upon completion of the course, participants will be able to design and implement pest and disease mapping systems, conduct risk assessments, develop geospatial surveillance frameworks, generate outbreak intelligence reports, and support evidence-based agricultural decision-making. They will acquire practical competencies that enhance crop protection strategies, improve agricultural resilience, strengthen food security, and contribute to sustainable agricultural development.

Course Objectives

1.     Understand the principles of pest and disease mapping and surveillance systems.

2.     Apply GIS and Remote Sensing technologies in agricultural health monitoring.

3.     Conduct pest and disease data collection and spatial analysis.

4.     Develop geospatial databases for agricultural surveillance.

5.     Perform outbreak mapping and hotspot identification.

6.     Analyze environmental factors influencing pest and disease occurrence.

7.     Develop pest and disease risk assessment models.

8.     Support integrated pest management through spatial intelligence.

9.     Utilize emerging technologies for agricultural surveillance and forecasting.

10.  Develop decision-support systems for pest and disease management.

Organization Benefits

1.     Improved pest and disease surveillance capabilities.

2.     Enhanced early warning and outbreak response systems.

3.     Reduced crop losses through timely interventions.

4.     Improved agricultural productivity and food security.

5.     Better allocation of pest management resources.

6.     Enhanced risk assessment and forecasting capabilities.

7.     Strengthened agricultural extension and advisory services.

8.     Improved decision-making through geospatial intelligence.

9.     Increased efficiency in monitoring and reporting systems.

10.  Enhanced institutional capacity in crop protection and agricultural resilience.

Target Participants
Agronomists, Plant Pathologists, Agricultural Officers, GIS Specialists, Extension Officers, Crop Protection Experts, Agricultural Researchers, Remote Sensing Analysts, Environmental Scientists, Development Practitioners, Government Agricultural Officers, NGO Professionals, Monitoring and Evaluation Specialists, Data Analysts, Project Managers, Precision Agriculture Specialists, Academics, Food Security Experts, Natural Resource Managers, and professionals involved in agricultural health and crop management.

Course Outline

Module 1: Introduction to Pest and Disease Mapping Systems

·       Principles of agricultural pest and disease surveillance

·       Concepts of spatial epidemiology

·       GIS applications in crop protection

·       Pest and disease management frameworks

·       Components of surveillance systems

·       Introduction to digital agriculture monitoring

Case Study: Development of national pest and disease monitoring systems.

Module 2: Pest and Disease Data Collection and Management

·       Field survey methodologies

·       Mobile and GPS-enabled data collection

·       Pest and disease identification techniques

·       Spatial database design and development

·       Data quality assurance and validation

·       Agricultural surveillance information systems

Case Study: Establishing field-based pest surveillance programs.

Module 3: GIS Mapping and Spatial Analysis

·       Pest distribution mapping techniques

·       Disease occurrence mapping

·       Spatial clustering and hotspot analysis

·       Risk zone identification

·       Geographic pattern analysis

·       Visualization and reporting techniques

Case Study: Mapping crop disease outbreaks across agricultural regions.

Module 4: Remote Sensing Applications for Crop Health Monitoring

·       Satellite imagery acquisition and processing

·       Vegetation health assessment

·       Crop stress detection methodologies

·       Spectral analysis for disease identification

·       Change detection techniques

·       Remote sensing-based crop monitoring

Case Study: Early detection of crop stress using satellite imagery.

Module 5: Environmental Risk Assessment and Modeling

·       Climate and environmental drivers of outbreaks

·       Environmental suitability analysis

·       Habitat suitability modeling

·       Risk factor identification

·       Spatial vulnerability assessments

·       Predictive mapping techniques

Case Study: Environmental modeling of pest infestation risks.

Module 6: Pest and Disease Forecasting Systems

·       Forecasting principles and methodologies

·       Climate-based outbreak prediction

·       Seasonal forecasting models

·       Statistical and predictive analytics

·       Early warning system development

·       Forecast validation techniques

Case Study: Developing climate-driven pest forecasting systems.

Module 7: Integrated Pest Management and Spatial Decision Support

·       Principles of integrated pest management

·       Spatial targeting of interventions

·       Resource allocation optimization

·       Monitoring intervention effectiveness

·       Decision-support system design

·       Agricultural advisory services

Case Study: GIS-supported integrated pest management programs.

Module 8: Drone Technologies for Pest and Disease Surveillance

·       Drone-based agricultural monitoring

·       High-resolution imagery acquisition

·       Crop scouting and inspection techniques

·       Automated pest detection systems

·       Precision agriculture applications

·       Drone data processing and analysis

Case Study: Drone-assisted pest surveillance in commercial farming systems.

Module 9: Artificial Intelligence and Machine Learning Applications

·       AI applications in agricultural surveillance

·       Machine learning for pest detection

·       Automated image classification techniques

·       Predictive outbreak analytics

·       Big data integration and analysis

·       Smart agriculture innovations

Case Study: AI-powered disease detection and forecasting systems.

Module 10: Food Security and Agricultural Risk Analysis

·       Impact assessment of pest outbreaks

·       Food security monitoring systems

·       Agricultural resilience analysis

·       Economic loss estimation techniques

·       Disaster risk reduction strategies

·       Emergency response planning

Case Study: Assessing food security impacts of agricultural pest invasions.

Module 11: Reporting, Dashboards, and Information Systems

·       Agricultural surveillance dashboards

·       Data visualization and reporting

·       Web GIS applications

·       Stakeholder communication frameworks

·       Information dissemination systems

·       Monitoring and evaluation tools

Case Study: Developing interactive pest surveillance dashboards.

Module 12: Capstone Pest and Disease Mapping Project

·       Project planning and implementation

·       Data collection and integration

·       Spatial analysis and risk modeling

·       Forecasting and decision-support development

·       Report preparation and presentation

·       Final project evaluation

Case Study: End-to-end pest and disease mapping project supporting surveillance, forecasting, outbreak response, crop protection planning, and sustainable agricultural development.

General Information

1.     Customized Training: All our courses can be tailored to meet the specific needs of participants.

2.     Language Proficiency: Participants should have a good command of the English language.

3.     Comprehensive Learning: Our training includes well-structured presentations, practical exercises, web-based tutorials, and collaborative group work. Our facilitators are seasoned experts with over a decade of experience.

4.     Certification: Upon successful completion of training, participants will receive a certificate from Foscore Development Center (FDC-K).

5.     Training Locations: Training sessions are conducted at Foscore Development Center (FDC-K) centers. We also offer options for in-house and online training, customized to the client's schedule.

6.     Flexible Duration: Course durations are adaptable, and content can be adjusted to fit the required number of days.

7.     Onsite Training Inclusions: The course fee for onsite training covers facilitation, training materials, two coffee breaks, a buffet lunch, and a Certificate of Successful Completion. Participants are responsible for their travel expenses, airport transfers, visa applications, dinners, health/accident insurance, and personal expenses.

8.     Additional Services: Accommodation, pickup services, freight booking, and visa processing arrangements are available upon request at discounted rates.

9.     Equipment: Tablets and laptops can be provided to participants at an additional cost.

10.  Post-Training Support: We offer one year of free consultation and coaching after the course.

11.  Group Discounts: Register as a group of more than two and enjoy a discount ranging from 10% to 50%.

12.  Payment Terms: Payment should be made before the commencement of the training or as mutually agreed upon, to the Foscore Development Center account. This ensures better preparation for your training.

13.  Contact Us: For any inquiries, please reach out to us at training@fdc-k.org or call us at +254712260031.

14.  Website: Visit our website at www.fdc-k.org for more information.