Precision Agriculture - Basics

Course Number:

AG 105

Transcript Title:

Precision Agriculture - Basics

Created:

Oct 31, 2026

Updated:

Oct 31, 2026

Total Credits:

3

Lecture Hours:

22

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.

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.

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

Course Content

Outcome #1: Demonstrate knowledge of the history, significance, and technologies of precision agriculture in workplace applications.

• Historical Milestones:

  • Identification and analysis of key historical developments in precision agriculture.

    • The introduction of GPS technology to farming.

    • The development and use of yield monitors.

    • The rise of variable rate application techniques.

    • Adoption of remote sensing technologies

  • Examination of the impact of technological advancements on agricultural practices.

  • Case studies highlighting transformative moments in agricultural innovation.

    • The Green Revolution.

    • Introduction of precision planting technologies.

    • Advances in drone-based crop monitoring

• Core Technologies:

  • Comprehensive understanding of:

    • GPS and GIS for spatial data management.

    • Drones for aerial imaging and monitoring.

    • Internet of Things (IoT) devices for real-time data collection.

  • Exploration of how each technology integrates to optimize farming operations with real-world examples demonstrating successful applications.

Outcome #2: Apply spatial and temporal mapping tools to analyze soil and crop variability effectively.

• Understanding Variability:

  • Differentiation between spatial and temporal variability.

  • Importance of identifying variability for agricultural decision-making.

• Mapping and Analysis Tools:

  • Overview of commonly used tools for spatial and temporal mapping.

    • GIS software (e.g., ArcGIS, QGIS).

    • Precision farming platforms (e.g., Trimble, Ag Leader).

    • Soil Sampling and analysis kits.

  • Practical applications through hands-on case studies.

    • Field zoning based on soil analysis.

    • Variable rate irrigation strategies.

    • Yield mapping and forecast adjustments.

  • Integration.

    • Integration of mapping tools with technologies like drones and LoT for enhanced precision in analysis.

Outcome #3: Utilize agricultural sensors to monitor soil, crop health, and weather for decision-making.

• Sensor Types and Applications:

  • Soil Moisture sensors (e.g., TDR, capacitance probes).

  • Crop health sensors (e.g., chlorophyll meters).

  • Weather monitoring systems (e.g., automated weather stations).

• Data Interpretation:

  • Methods for translating sensor data into actionable insights.

  • Best practices for incorporating sensor data into farm management strategies.

    • Cross-referencing sensor data with historical trends.

    • Automating responses through smart irrigation systems.

Outcome #4: Process and interpret remote sensing data, including multispectral and hyperspectral imaging, for actionable insights in crop health management.

• Data Processing Techniques:

  • Steps for analyzing multispectral and hyperspectral imagery.

    • Data pre-processing (calibration, noise reduction).

    • Spectral analysis for specific crop health indicators.

    • Image classification and interpretation.

  • Tools and software commonly used for data processing.

    • ENVI

    • Pix4D.

    • Agisoft Metashape.

• Vegetation Indices:

  • Calculation and application of vegetation indices, such as:

    • NDVI (Normalized Difference Vegetation Index).

    • EVI (Enhanced Vegetation Index).

  • Case studies demonstrating the impact of remote sensing data on crop management.

Outcome #5: Develop a comprehensive precision agriculture plan to address workplace challenges, emphasizing sustainability and productivity.

• Plan Development:

  • Framework for synthesizing learned technologies and methodologies into actionable plans.

  • Strategies for addressing real-world agricultural challenges.

• Sustainability and Productivity:

  • Sustainable farming practices, such as:

    • Crop Rotation

    • Reduced tillage

    • Precision irrigation.

  • Methods to balance productivity with conservation, such as:

    • Leveraging real-time data for minimal input wastage.

    • Implementing renewable energy solutions on farms.

Suggested Texts and Materials

• Primary Textbook: Shannon, D. Kent, et al., Precision Agriculture Basics. ASA-CSSA-SSSA, 2020.

• Supplemental readings: Weekly articles from journals like Precision Agriculture and case studies.

• Equipment: Drones equipped with multispectral cameras, GIS software, and soil analysis kits.