The Digital Transformation
Australia’s agricultural sector is evolving rapidly, led by advancements in Remote Sensing (RS), Geographic Information Systems (GIS), and Artificial Intelligence (AI). These technologies are driving innovation across all industries, particularly in agriculture, which is now among the most technologically advanced sectors in Australia. With the global population projected to reach 9.8 billion by 2050, the demand for improved efficiencies to meet rising food demands is greater than ever. Climate change, shifting global markets, evolving customer demands, along with the push towards net-zero emissions are accelerating these changes. With the introduction of new legislation and mandatory climate reporting, leveraging farm data for emissions tracking is now an essential part of navigating this complex market. However, this data also presents a significant opportunity to enhance operational efficiencies and productivity. Recognising this, farmers are increasingly integrating data and technology into their planning and decision-making to optimise resource management and ensure long-term profitability and sustainability.
How Are GIS and RS Applied in Agriculture?
GIS (Geographic Information Systems) is a tool that uses spatial data to create interactive maps for visualizing and analysing agricultural land. Remote sensing involves gathering information about the Earth’s surface using satellites, aerial platforms, or drones. Satellites cover larger areas and offer a longer time series of data, reducing the need for extensive on-site data collection and sampling – equating to significant cost and labour savings for farmers. Aerial platforms and drones capture high-resolution images, with valuable applications in precision agriculture that can enhance yields and efficiencies through targeted management activities. Examples of remote sensing applications in agriculture include:
- Multispectral sensors (e.g., Sentinel-2) detect light beyond the visible spectrum, helping to assess vegetation health and soil moisture.
- LiDAR sensors measure terrain and canopy height, which can be used to monitor land-use change and model carbon stocks in trees.
- Thermal imaging identifies crop temperature variations, aiding irrigation management and early disease detection.
The NDVI Index and Its Applications
NDVI (Normalized Difference Vegetation Index) measures plant health by assessing how vegetation reflects light. Higher NDVI values are associated with healthier plants, while lower values indicate stress. This information can help identify crop issues early, guide precision agriculture practices, improve irrigation strategies and support yield predictions.
Remote sensing, GIS, and AI modelling provide scalable solutions to improve the measurement, reporting and verification (MRV) of carbon stocks by reducing the time and cost of traditional physical sampling. During project design, remotely sensed data is combined with on-farm records to identify non-arable, marginal, or low-productivity land, such as saline zones or nutrient-poor soils.. Additional datasets are then analysed to stratify the land into distinct zones with comparable characteristics (Figure 1). Strategic implementation of carbon farming projects on this land offers the opportunity for farmers to diversify their operations, create an additional revenue stream, and enhance land resilience and productivity. Other key applications include:
- Monitoring land use change: using historical datasets to monitor land use over time.
- Identifying exclusion zones: remotely sensed data determines exclusion zones such as roads and existing vegetation.
- Modelling carbon sequestration: Estimating carbon storage potential to calculate carbon credits.
- Ongoing monitoring: Reducing the need for physical audits through continuous monitoring using remote sensing.
Figure 1. Remotely sensed layers, a. thorium, b. potassium and c. bulk density used to inform d. stratification (zoning).
AI and the Future of Digital Mapping
An emerging example of AI-driven automation is Meta’s Segment Anything Model (SAM), which can rapidly analyse images and identify objects without prior training. In agriculture, this could efficiently and accurately transform land classification and mapping by automating exclusion mapping tasks (Figure 2). Whilst still evolving, these advancements signal a future where AI-powered tools enhance farm and project management with minimal manual intervention.
Figure 2. Meta’s Segment Anything Model for agricultural land parcels (ESRI).
Work Smarter, Not Harder
The surge of information from satellites, drones, and on-farm records means that there is now more data than humanly possible to process. However, AI has transformed this landscape through the ability to aggregate, process and analyse vast amounts of data, quickly and accurately. This significantly reduces the manual workload, while providing real-time insights to enhance decision-making. By streamlining processes and automating repetitive tasks, more time can be dedicated to high-level priorities such as strategic planning and long-term decision-making.
Challenges vs. Opportunities
Despite its benefits, the adoption of AI, GIS, and remote sensing face barriers to widespread adoption. However, each challenge also presents an opportunity:
- High Costs: High-resolution satellite and drone imagery can be expensive, limiting access to smaller farms.
- Opportunity: Open-source platforms like QGIS and satellite data from Sentinel-2 and Landsat provide viable alternatives.
- Complex Data Integration: Combining data from different sources requires advanced technical expertise.
- Opportunity: Planfarm TerraWise has an experienced team operating in this space and can work with farmers to provide practical solutions.
- Cultural Barriers: Agriculture and tech industries can be misaligned in their communication and priorities.
- Opportunity: Growing collaboration is bridging this gap, with tech firms tailoring solutions to agricultural needs.
- Poor Internet Connectivity: Remote areas face connectivity challenges, restricting real-time data processing.
- Opportunity: Offline solutions and satellite-based internet help resolve the connectivity gaps.
The Future Is Digital
As technology advances, embracing these challenges presents a huge opportunity for innovation and growth. While much remains uncertain, one thing is clear, change is inevitable, and those willing to adapt and evolve will be best positioned to secure a sustainable and prosperous future in this rapidly changing industry.
References
Segment Anything Model by Meta AI
Advancing Image Segmentation with SAM: Segment Anything Model | ESRI
Agriculture in Australia: growing more than our farming future | Australia 2025: smart science
Innovations in Australian agriculture | The Australian Farmer
