How is artificial intelligence (AI) being used in agriculture?

This article explores how AI advancements, from automated fruit harvesting to livestock facial recognition, are helping to drive efficiency, productivity, and sustainability across our agricultural industries.

The benefits of AI for agriculture

AI offers food and fibre producers an innovative approach to managing persistent challenges such as workforce shortages, climate variability, and resource degradation.

AI tools are analysing the vast datasets collected across Australian farm businesses, refining data into actionable insights that improve farm decision-making – unlocking greater efficiencies, increasing yield, and boosting sustainable resource use for a more food secure future.

Automated and high precision monitoring

AI-powered drones, satellite imaging, and sensors enable farmers to monitor crops and livestock with high precision. These tools help detect early signs of disease, nutrient deficiencies, or water stress, allowing for timely interventions.

For example, AI-driven imaging technology can identify crop diseases before they spread, minimising losses and reducing excessive pesticide use.

READ MORE: What is smart farming?

Predictive analytics

By analysing large datasets, AI can predict weather patterns, pest infestations, and optimal planting times, improving farm decision-making by reducing uncertainty.

For example, AI models can forecast drought conditions, prompting farmers to implement water-saving strategies in advance.

Improved yields and cost savings

AI-driven machinery, like autonomous tractors and robotic harvesters, can reduce one of the most significant costs to a farm business: labour. Precision farming tools also enable farmers to optimise water, fertiliser, and pesticide use, reducing waste and cost.

For example, machine learning algorithms analyse soil health data to recommend precise nutrient applications, ensuring maximum crop yields while reducing input costs.

Enhanced pest and disease control

AI image recognition and machine learning algorithms help identify pests and diseases early, allowing for targeted treatments before crop or herd health is impacted. In addition, smart spraying systems can apply pesticides only where needed, reducing chemical use and environmental impact.

For example, AI-based models enable real-time weed detection and selective herbicide application.

READ MORE: Guide to crop disease and management

Livestock health and welfare monitoring

AI-powered facial recognition and biometric tracking are being utilised to monitor cattle health and behaviour. Wearable sensors track the health and activity levels of livestock, detecting signs of illness or distress. These systems help farmers respond quickly to health issues, reducing losses and improving animal welfare.

Supply chain optimisation

AI can assist in demand forecasting, logistics planning, and inventory management across agrifood supply chains, reducing food waste and improving market efficiency.

For example, AI models can analyse consumer behaviour and supply chain trends to help producers and retailers optimise pricing and distribution. This ensures that fresh produce reaches consumers faster, minimising food waste from spoilage.

Real-world examples of AI integration in agriculture

AI is already making a tangible impact across various agricultural industries. Here are some specific examples of AI applications and the companies driving these advancements:

RELATED: How exactly is AI used in agriculture?

AI application #1: Monitoring of livestock health

Companies like Ceres Tag and ProTag have developed smart tags for real-time livestock monitoring, providing insights into health metrics, behaviour, and location that enables farmers to detect early signs of illness and optimise feeding strategies for better productivity.

Image supplied: Ceres Tag

READ MORE: Guide to animal diseases and management

Platforms like AgriWebb integrate AI to optimise herd management, enhance productivity, and streamline compliance and traceability, ensuring that producers can meet regulatory requirements more efficiently.

AI application #2: Crop, soil, and irrigation system analysis

AI platforms are providing insights to farmers and agronomists on soil condition to help with irrigation management and fertiliser application. AquaTerra sensors measure soil moisture and temperature, helping farmers to adjust irrigation schedules and nutrient applications to maximise yields while conserving resources. ​

READ MORE: What is precision irrigation?

AI application #3: Automated fruit harvesting

Using computer vision and machine learning algorithms, robots equipped with AI are helping orchardists manage seasonal labour availability. Ripe Robotics’ autonomous robot (“Eve”) can pick apples, plums, peaches, and nectarines, using AI to analyse fruit for size, colour and quality.

Over time, Eve’s vast dataset can help with disease detection, damage prevention, and decision-making to increase yield and quality.

RELATED: Robotics in agriculture

Image supplied: Ripe Robotics

AI application #4: Yield forecasting

Bitwise Agronomy’s GreenView uses AI crop analysis to ‘see’ like humans – but more accurately, more consistently, and more rapidly. Using cameras mounted on tractors or drones, GreenView scans and analyses vineyards and berry farms, delivering 90% better accuracy in yield forecasting, enabling improved planning of harvests and marketing.

AI application #5: Boosting livestock reproduction

InFarm has delivered a prototype autonomous camera system that leverages machine vision and AI to identify and monitor cows. For producers in remote northern Australia, the cameras provide ‘eyes and ears’ where regular handling and monitoring of a herd is logistically challenging, time-consuming, and expensive.

AI application #6: Microclimate monitoring for optimised farm decisions

The Yield Technology Solutions uses AI and IoT to provide hyper-local weather insights and predictive analytics for speciality crops. Their Sensing+ platform collects real-time data from on-farm sensors and combines it with AI-driven models to deliver actionable insights on irrigation, fertilisation, planting, and harvest. 

Current challenges of AI in agriculture

Despite its benefits, several challenges constrain the widespread adoption of AI in agriculture:

High initial investment

AI technologies require significant upfront costs for infrastructure, sensors, and machinery, creating financial barriers for smaller-scale farm businesses.

Ongoing costs of maintenance and training

Maintaining AI systems and providing specialised training adds ongoing expense.

Potential job displacements

AI-driven automation reduces manual labour needs, potentially displacing unskilled or seasonal workers such as fruit pickers.

While AI may create new job opportunities in tech-driven roles (e.g., data analysts) the transition can be challenging, requiring retraining programs.

Data privacy and security concerns

AI systems rely on large datasets collected from farms and agribusinesses, including production practices, yields, and financial information. Concerns about data misuse or unauthorised access could prevent farmers from fully utilising AI solutions.

Dependence on internet connectivity

Many AI solutions require stable internet access, which can be lacking in rural and remote areas, limiting access to these technologies.

Ethical and environmental considerations

AI’s role in decision-making raises ethical questions, especially when systems are used to determine resource allocation, labour needs, or pricing. The lack of transparency in how AI models make decisions can also create distrust among farmers and consumers.

Reshaping modern agriculture through AI

AI is reshaping agriculture by enhancing productivity, improving sustainability, and addressing global food security challenges. As the technology continues to evolve, its role in optimising farming practices and supply chains will become even more crucial.