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the australian farmer
liable and verifiable quality metrics. With these tools, hard-working Australian farmers will be even better equipped to make the most of this new era. Once AI Assessment enjoys wider acceptance from buyers, the playing field will level and more exciting possibilities will emerge. Whilst it is difficult to specu- late as to what this playing field will look like, we can be certain that farmers and trading companies relying on AI assessments will gain a significant competitive ad- vantage through risk reduction and quality assurance – and several countries are already making moves in the right direction. Argentina, for example, has pioneered the establishment of AI standards for the assessment of barley variety, while the Indian government – one of the world’s largest grain buyers – is actively supporting startups developing AI Assessment technologies. The hard-earned global recognition of the qual- ity of Australian agricultural produce has been the result of dedicated efforts to set and follow indus- try-wide quality standards. Under the new quality assessment religion, AI will sit at the altar, blessing those who recognize its powers with credibility that begets global demand. Dr. Sivam Krish is a Generative AI Expert and the CEO of GoMicro, a company offering AI-powered quality assess- ment solutions for grain and fresh produce.
records, making agricultural products into tradable digital assets. New forms of digital platforms will manage trade entirely electronically, in the same way that stock trading operates today. Whilst it will take some time to eventuate, there are some real benefits that farmers will soon enjoy. For farmers, AI Assessment will first be used to adjust machine settings, assess downgrade risks (the biggest initial benefit), and make cleaning decisions. The tech- nology will then be adapted for the purpose of storing, selling and making blending decisions from a position of complete control. The next obvious step for farmers would be to in- stall AI assessment devices on harvesting machines. This is already happening with protein measurements where harvesting machines are being attached with protein measurement devices - allowing farmers to create protein maps of their paddocks. This map is then used for variable rate fertiliser application. The same can be done for herbicide applications based on the detection of weed seeds. Machine-mounted AI Assessment technologies will certainly help re- duce input costs. Such devices can also be attached to belts or augers that fill trucks, making it possible to assess the quality of the entire load with much better precision than current sampling methods. Subjectivity opens the doors for malpractice. AI is fundamentally objective, it will help create more re-
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