With the rise of publicly available artificial intelligence (AI) like ChatGPT, farming practices are poised for a major transformation. While the potential benefits are overwhelming, there is a dark side to the democratization of AI in farming. AI could lead to more confusion than clarity for farmers. What can be done to address this issue?
The development of AI applications in agriculture has been going on for years at tech companies and is now being applied in farming practice. For example for image recognition of weeds used by smart weeding systems and spray technology. But what is happening now with making AI publicly available is a different cake. It will not only scale up the application of AI more uncontrollably and thereby also make it more confusing for farmers. What knowledge and data is behind this advice or prescription I am getting? Who is the source?
One of the most significant advantages of AI platform like ChatGPT is its ability to learn from past experiences and adapt to changing conditions. For example by analyzing data from sensors, drones, and other sources, AI systems like ChatGPT can identify patterns and make predictions about future trends. In this way ChatGPT could be a valuable resource for farmers in remote areas who may not have access to expert advice. With its ability to connect with experts from around the world, ChatGPT could provide farmers with the latest information and best practices. Could be, but how will all this work out in practice?
What does ChatGTP itself think about it? So, I asked ChatGPT the following question; How can farmers use you for their practice? Then in a blink of an eye this pumped up;
As an AI language model, I can be used by farmers in a variety of ways. Here are a few examples:
Well, I was equally impressed by these proposals of this AI magic box. But it’s no more than a first attempt. There is still much to discuss about this. There are some challenges that need to be addressed before AI systems like ChatGPT can be fully integrated into farming practices.
For one, there is a need to ensure that the data being used to train ChatGPT is accurate and representative of farm practice conditions. And that AI is tuned to maximize profit or benefit for the farmers, not for suppliers of pesticides or fertilizers for example. Additionally, there is a need to develop user-friendly interfaces that allow farmers to interact in a way that is intuitive and efficient. And so there are other issues of concern.