Introduction to Drone Operations and Autonomous Vehicles in Agriculture

Course Number:

AG 106

Transcript Title:

Introduction to Drone Operations and Autonomous Vehicles in Agriculture

Created:

Oct 31, 2026

Updated:

Oct 31, 2026

Total Credits:

4

Lecture Hours:

33

Lecture / Lab Hours:

22

Lab Hours:

0

Satisfies Cultural Literacy requirement:

No

Satisfies General Education requirement:

No

Grading Options

A-F, P/NP, Audit

Default Grading Options

A-F

Repeats available for credit:

0

Suggested Outcome Assessment Strategies

The determination of assessment strategies is generally left to the discretion of the instructor. Here are some strategies that you might consider when designing your course: writings (journals, self-reflections, pre writing exercises, essays), quizzes, tests, midterm and final exams, group projects, presentations (in person, videos, etc), self-assessments, experimentations, lab reports, peer critiques, responses (to texts, podcasts, videos, films, etc), student generated questions, Escape Room, interviews, and/or portfolios.

Department Required: 
A capstone project in the form of a comprehensive farm management plan, assessed uniformly across all sections.

Course Activities and Design

The determination of teaching strategies used in the delivery of outcomes is generally left to the discretion of the instructor. Here are some strategies that you might consider when designing your course: lecture, small group/forum discussion, flipped classroom, dyads, oral presentation, role play, simulation scenarios, group projects, service learning projects, hands-on lab, peer review/workshops, cooperative learning (jigsaw, fishbowl), inquiry based instruction, differentiated instruction (learning centers), graphic organizers, etc.

Required Course Activities (Labs & Practical Exercises)

• Data Integration Labs:
  Combining RGB, NDVI, LiDAR, and thermal data for decision-making

• Mission Execution Scenarios:
  Planning and executing drone/autonomous vehicle missions in real-world agricultural settings

• Sustainability Projects:
  Assessing and implementing precision agriculture solutions

Course Content

Outcome #1:Operate drones and autonomous vehicles for mission planning and data acquisition.

• Regulatory and Safety Considerations

  • FAA regulations and legal considerations for drone and autonomous vehicle operators

  • Compliance with federal, state, and local laws governing UAVs and autonomous ground vehicles

• Safety Protocols

  • General operational safety for drones and autonomous vehicles

  • Specific protocols for:

    • Drones: Airspace awareness, weather conditions, battery management, emergency landings

    • Autonomous Vehicles: Obstacle detection, traffic awareness, vehicle control failure procedures

• Pre-Flight and Operational Readiness

  • Drone setup and calibration

  • Pre-flight inspections and system diagnostics

  • Flight safety procedures and vehicle start-up protocols

• Operation Techniques

  • Drone Piloting Techniques:

    • Manual vs. autonomous control

    • Navigating various environmental conditions

  • Autonomous Programming for Precision Tasks:

    • Flight path automation

    • Pre-set routing for vehicles and drones

• Emergency Protocols and Troubleshooting

  • Common system failures and resolutions:

    • Communication failures (loss of signal, GPS malfunction)

    • Mechanical failures (propeller damage, motor failure)

    • Battery issues (low charge, overheating)

    • Software failures (firmware corruption, sensor misalignment)

    • Navigation errors (incorrect waypoints, environmental obstacles)

Outcome #2: Evaluate effectiveness of sensor data (Thermal, RGB, NDVI, and LiDAR) for agricultural applications.

• Sensor Types and Applications

  • RGB Sensors: Visual mapping and object identification

  • NDVI Sensors: Vegetation health monitoring

  • LiDAR Sensors: Terrain mapping and structure assessment

  • Thermal Sensors: Crop stress detection and pest infestation analysis

• Sensor Calibration, Data Acquisition, and Evaluation

  • Calibration Procedures: Ensuring accuracy and consistency in sensor readings

  • Data Collection Methods: Differences in drone vs. autonomous vehicle data acquisition

  • Data Processing: Converting raw sensor data into actionable insights

• Practical Applications

  • Crop health assessment and stress monitoring

  • Detecting pest infestations and applying corrective measures

  • Evaluating soil conditions and moisture levels

Outcome #3: Develop effective field routing and flight programming techniques.

• Field Routing vs. Flight Programming

  • Field Routing (Autonomous Vehicles):

    • Route optimization for efficient data collection

    • Obstacle avoidance and sensor-based navigation

    • Integration with GIS-based mapping software

  • Flight Programming (Drones):

    • Waypoint planning for mission execution

    • Airspace and terrain considerations in flight programming

    • Automation for large-scale agricultural monitoring

• Routing Models and Mapping Software

  • Routing Models:

    • Grid-based path planning

    • AI-driven adaptive routing

    • Coverage path optimization

  • Mapping Software:

    • ArcGIS for data visualization

    • Pix4D for drone-based photogrammetry

    • Agisoft Metashape for 3D modeling

    • DJI Terra for automated drone mission planning

• Executing Complex Missions

  • Key components of a complex mission:

    • Multi-sensor data integration

    • Automated vs. manual control transitions

    • Large-area coverage strategies

Outcome #4: Prepare data-driven approaches that apply to water management, pest control, and crop monitoring.

• Regulatory considerations for environmental impact

• Water Management Techniques

  • Precision irrigation planning using GIS-based analysis

  • Monitoring soil moisture variations with remote sensing

  • Flood risk assessment using LiDAR data

• Pest Control Methodologies

  • Early detection using thermal and NDVI sensors

  • Targeted spraying using drone-enabled application

  • AI-based pest movement prediction

• Crop Health Monitoring

  • Stress detection using multispectral and hyperspectral imaging

  • Long-term trend analysis with historical drone data

  • Integration with farm management software

Suggested Texts and Materials

• Primary Textbook: I.M. Davis, Drone in Agriculture, American Technical Publications.

• Supplemental Readings: Weekly journal articles and case studies. Journals recommended: Journal of Precision Agriculture (Springer), Remote Sensing Applications (Elsevier), Agricultural Systems (Elsevier). Books: Smart Agriculture (M. Naeem & A. Khosla), Precision Agriculture for Sustainability (J. Stafford).
• Equipment: Drones equipped with RGB and thermal sensors, GIS software, and calibration tools.