AI in Agriculture and Precision Farming Training Course

Module 1: Introduction to AI and Precision Agriculture

• The evolution of farming: From traditional methods to Agriculture 4.0
• Overview of the AI stack in agriculture: Sensors, Connectivity, and Algorithms
• Identifying high-value use cases: Weed management, livestock monitoring, and logistics
• Understanding the "Digital Twin" concept for farm management
• Challenges of AI in the field: Connectivity, environment, and data labeling
• Practical session: Setting up a digital farm profile and identifying key data sources for a pilot project

Module 2: Data Acquisition: IoT Sensors and Farm Telemetry

• Understanding soil sensors: Moisture, pH, NPK levels, and temperature
• Weather station integration: Wind speed, humidity, and solar radiation data
• IoT communication protocols for rural areas: LoRaWAN, NB-IoT, and Satellite
• Data cleaning and normalization of irregular environmental telemetry
• Real-time monitoring architectures: Moving data from the soil to the cloud
• Practical session: Configuring a LoRaWAN gateway and visualizing real-time soil moisture data

Module 3: Computer Vision for Crop Health Monitoring

• Image processing fundamentals for plant pathology
• Automated pest and disease identification using Deep Learning (CNNs)
• Weed vs. Crop classification for targeted herbicide application
• Fruit ripeness detection and grading using visual spectral analysis
• Leaf Area Index (LAI) calculation through automated image segmentation
• Practical session: Training a model to identify specific leaf diseases from a curated image library

Module 4: Machine Learning for Yield Prediction and Forecasting

• Feature engineering for yield: Integrating historical data, weather, and soil health
• Supervised learning models for predicting harvest volumes and quality
• Time-series analysis for crop growth cycle monitoring
• Managing risk: Using AI to predict potential crop failure or market fluctuations
• Analyzing the impact of genetic varieties on yield performance
• Practical session: Implementing a regression model to forecast seasonal yield based on historical farm data

Module 5: AI-Driven Irrigation and Nutrient Management

• Variable Rate Application (VRA): Calculating precise fertilizer and water needs
• Evapotranspiration modeling for intelligent irrigation scheduling
• Detecting nutrient deficiencies through multispectral leaf analysis
• Automated fertigation systems: Integrating AI logic with hardware pumps
• Reducing environmental runoff through predictive nitrogen management
• Practical session: Designing an automated irrigation schedule based on predictive moisture and weather models

Module 6: Drone Technology and Aerial Image Analytics

• Drone hardware for agriculture: Multispectral, Thermal, and RGB cameras
• Flight planning for autonomous farm surveys and mapping
• Processing "Orthomosaics": Stitching aerial photos for farm-wide analysis
• Calculating Vegetation Indices: NDVI, NDRE, and SAVI for plant vigor
• Individual tree/plant counting and canopy volume estimation from the air
• Practical session: Processing raw drone multispectral imagery to generate an NDVI health map

Module 7: Satellite Remote Sensing and GIS Integration

• Accessing and utilizing open-source satellite data (Sentinel, Landsat)
• Large-scale crop mapping and classification using spatial-temporal data
• Monitoring soil moisture and land use changes from space
• Integrating GIS (Geographic Information Systems) with AI for terrain analysis
• Analyzing regional agricultural trends and drought monitoring
• Practical session: Building a Python script to extract and analyze historical NDVI data for a specific GPS coordinate

Module 8: Autonomous Farm Machinery and Robotics

• Foundations of GNSS and RTK for centimeter-level accuracy in the field
• Path planning for autonomous tractors and robotic weeders
• Obstacle detection and safety protocols for robotic farm workers
• AI-powered robotic harvesting: Challenges in soft-fruit handling
• Fleet management: Coordinating multiple autonomous units in one operation
• Practical session: Simulating a robotic path planning task for an autonomous sprayer

Module 9: Climate Adaptation and Predictive Weather Modeling

• Integrating global climate models with local micro-climate data
• Long-term trend analysis for shifting crop planting windows
• AI for extreme weather event prediction: Frost, hail, and heatwaves
• Carbon sequestration modeling and tracking for "Carbon Farming"
• Optimizing greenhouse environments through predictive climate control
• Practical session: Using a predictive weather model to determine the optimal planting date for a specific crop

Module 10: AI Ethics, Data Governance, and Sustainability

• Data ownership: Who owns the data generated by a smart farm?
• Ensuring data privacy and security in the agricultural supply chain
• Addressing bias in AI: Ensuring technology accessibility for smallholder farmers
• Measuring the "Environmental ROI": Tracking biodiversity and soil health
• Developing a roadmap for the scalable adoption of AI in your organization
• Practical session: Drafting an AI governance policy and a sustainability impact report for a digital farm

Requirements:

• Participants should be reasonably proficient in English.
• Applicants must live up to Armstrong Global Institute admission criteria.

Terms and Conditions

1. Discounts: Organizations sponsoring Four Participants will have the 5th attend Free

2. What is catered for by the Course Fees: Fees cater for all requirements for the training – Learning materials, Lunches, Teas, Snacks and Certification. All participants will additionally cater for their travel and accommodation expenses, visa application, insurance, and other personal expenses.

3. Certificate Awarded: Participants are awarded Certificates of Participation at the end of the training.

4. The program content shown here is for guidance purposes only. Our continuous course improvement process may lead to changes in topics and course structure.

5. Approval of Course: Our Programs are NITA Approved. Participating organizations can therefore claim reimbursement on fees paid in accordance with NITA Rules.

Booking for Training

Simply send an email to the Training Officer on training@armstrongglobalinstitute.com and we will send you a registration form. We advise you to book early to avoid missing a seat to this training.

Or call us on +254720272325 / +254725012095 / +254724452588

Payment Options

We provide 3 payment options, choose one for your convenience, and kindly make payments at least 5 days before the Training start date to reserve your seat:

1. Groups of 5 People and Above – Cheque Payments to: Armstrong Global Training & Development Center Limited should be paid in advance, 5 days to the training.

2. Invoice: We can send a bill directly to you or your company.

3. Deposit directly into Bank Account (Account details provided upon request)

Cancellation Policy

1. Payment for all courses includes a registration fee, which is non-refundable, and equals 15% of the total sum of the course fee.

2. Participants may cancel attendance 14 days or more prior to the training commencement date.

3. No refunds will be made 14 days or less before the training commencement date. However, participants who are unable to attend may opt to attend a similar training course at a later date or send a substitute participant provided the participation criteria have been met.

Tailor Made Courses

This training course can also be customized for your institution upon request for a minimum of 5 participants. You can have it conducted at our Training Centre or at a convenient location. For further inquiries, please contact us on Tel: +254720272325 / +254725012095 / +254724452588 or Email training@armstrongglobalinstitute.com

Accommodation and Airport Transfer

Accommodation and Airport Transfer is arranged upon request and at extra cost. For reservations contact the Training Officer on Email: training@armstrongglobalinstitute.com or on Tel: +254720272325 / +254725012095 / +254724452588