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How exactly is AI used in agriculture?

Artificial Intelligence (AI)  in agriculture uses machine learning and predictive analytics to analyse data, which has led to substantial productivity and sustainability improvements in farming practices.

AI has become crucial for transforming agriculture production and processing, move over traditional farming practices, data-driven and efficient methods are here to stay. By harnessing AI technologies, farmers can now make well-informed decisions with real-time and historical data, optimise resources, and boost productivity to meet the demands of a growing global population which will also help with addressing climate change challenges. 

At the forefront of modern agriculture, otherwise known as agrifood technology are AI tools such as machine learning, satellite imagery, and predictive analytics. These technologies mark a significant departure from conventional farming practices, bringing in innovation and efficiency. 

Benefits of AI in Agriculture

Cost Savings 

While implementing AI in agriculture can be costly upfront, the long-term savings through efficiency, productivity, and analytical roadmaps are undeniable. But there are also underlying costs savings that should be taken into consideration, for example, labour and machinery utilisation. AI in agriculture automates tasks like crop monitoring and yield predictions, cutting labour costs. AI-driven predictive analytics enables farmers to forecast future yields based on historical data and weather patterns. This allows for more effective planning of sowing and harvesting schedules, ultimately enhancing overall productivity and profitability. Real-time insights also help farmers proactively address management issues, ensuring healthier crops and minimised losses. 

Data Driven Farming: Precision and Efficiency 

AI in agriculture enables data-driven decisions by analysing vast amounts of data on weather patterns, soil quality, and previous crop yields. By identifying factors affecting crop yields, such as temperature or moisture levels, farmers can make more informed decisions on sowing, irrigation, and fertilisation. This precision agriculture minimises waste and maximises productivity. AI automates tasks like crop monitoring, freeing up farmers' time for other activities. With AI-powered predictive analytics, farmers can gather and process data more efficiently, enabling better decisions at every stage of the farming process, from soil health assessment to harvest planning. 

Early disease and pest detection  

AI in agriculture offers early disease and pest detection, a critical solution to prevent crop damage and yield loss. Using image recognition technology, AI analyses crop images for signs of pests or disease, enabling farmers to take proactive measures. With advanced sensors and continuous monitoring, AI identifies issues in real-time, so that farmers can intervene promptly, ensuring healthier crops and increased yields. AI applications like Plantix provide a vast database of plant diseases, offering insights and solutions for effective disease management. This early detection capability enhances the efficiency and effectiveness of the crops lifecycle, optimising its overall health and productivity. 

Sustainability and resilience  

AI in agriculture fosters sustainability and resilience by optimising resource usage and enhancing environmental stewardship. Through AI-powered applications, farmers can monitor energy, water, and chemical inputs, enabling adjustments for more sustainable practices. In addition, the early detection of pests and diseases allows for targeted pesticide use, reducing overall chemical usage. AI also tracks soil health metrics like moisture, pH, and nutrients, informing decisions on irrigation and fertilisation for improved soil health. By leveraging AI-driven insights on weather patterns and plant growth, farmers can optimise irrigation, minimising water usage while maximising crop yield and sustainability efforts. 

10 AI applications in agriculture you should know about

1. Revolutionising Agricultural Spray Applications for the Cotton Research and Development Corporation (CRDC) project 

Agricultural Spray Applications for the Cotton Research and Development Corporation (CRDC) project is on a mission to reduce spray drift from agricultural pesticides, curbing adverse effects on nearby crops and wildlife that cost the cotton industry $18million in 2018 alone.  
 
2. Ripe Robotics  

Ripe Robotics pioneers agricultural innovation, creating robotic fruit harvesters for apples and stone fruit, aiming for full automation. "Eve," their robot, adeptly harvests apples, plums, peaches, and nectarines in Shepparton, Australia, using AI and big data to optimise operational efficiency and evaluate fruit quality in real-time. 
 
3. VROC  

VROC has created an artificial intelligence platform tailored for agriculture's big data, featuring a user-friendly, no-code interface. This tool  is used by farmers and agronomists for predictive insights derived from data, aiding decision-making throughout the entire value chain. 
 
4. Robotics ready AI in viticulture  

The Yield, Yamaha, UTS, Food Agility, and Treasury Wine Estates have collaborated to investigate the potential of merging robotics, micro-climate weather services, and artificial intelligence (AI) in wine grape cultivation. This initiative aims to evaluate field robots' capacity to monitor vine growth and health, integrating data into The Yield's Digital Playbook and AI forecasts to predict wine grape yield and other harvest attributes during each growth cycle. 

5. DataFarming  

DataFarming, an Australian ag-tech firm, offers intuitive precision ag solutions, integrating AI for real-time spatial data analytics. By leveraging AI, they provide cloud-based solutions to enhancing efficiency and decision-making. 
 
6. Bitwise Agronomy  

Bitwise Agronomy employs AI in their Greenview product, which utilises computer vision and machine learning to accurately count and measure horticultural crop components like berry and bunch number, aiding in yield estimation. This innovative technology addresses challenges such as inaccurate yield estimates by providing precise data for operational decisions, offering a cost-effective solution through off-the-shelf camera usage. 

Pictured: Fiona Turner, CEO of Bitwise

7. The Yield Technology Solutions 

The Yield Technology Solutions is an Australian ag-tech company using IoT and AI to transform agriculture with scalable digital solutions. Their Sensing+ microclimate solution offers tailored information and predictions via user-friendly apps, helping growers make timely decisions. Through AI models driven by aggregated microclimate data, The Yield provides accurate yield predictions, enhancing commercial outcomes. 
 
8. The ARC Research Hub for Driving Farming Productivity and Disease Prevention  

The ARC Research Hub for Driving Farming Productivity and Disease Prevention, funded by the Australian Government and industry partners, focuses on integrating AI technologies into agriculture. Hosted by Griffith University, the Hub collaborates with leading universities and research organisations to develop innovative AI solutions that enhance farming efficiency, reduce costs, and mitigate disease risks, ultimately strengthening Australia's competitive edge in the global market. 
 
9. Pairtree Intelligence  

Pairtree is an independent dashboarding platform that centralises agribusiness data, empowering farmers with AI-driven decision support tools. By bridging operational data gaps, Pairtree enhances productivity and sustainability in agriculture, offering scalable solutions tailored to farmers' needs for improved efficiency and profitability. 
 
10. AquaTerra Solutions  

AquaTerra Solutions leverages artificial intelligence to analyse soil data collected by their sensing hardware and IoT platform. This AI-powered analytics interface provides farmers with actionable insights on irrigation management and fertiliser application, enabling them to make data-driven decisions for improved efficiency and productivity in farming operations. 

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