You may have heard the latest buzz about a game-changing technology that will forever change the worlds of business, communication, entertainment, education and… agriculture. We’re talking about massive advancements in artificial intelligence or “AI.” AI tools and software are now accessible to all of us at little or sometimes even no cost. AI is not a new thing. If you have a smartphone and have used its voice recognition tools or typed in a Google search, you have used AI. If you remember “Jarvis” from the Iron Man superhero movies or “Hal” in the movie 2001: A Space Odyssey, you’re probably aware of the idea that AI has the potential to make our lives easier in many ways. It can assist us in using information and data to make complex decisions. AI technology can work on our behalf to accomplish tasks quickly, efficiently, and with a high degree of accuracy.
In late 2022, several organizations began to release a type of AI publicly called “generative artificial intelligence.” The AI that’s gotten much attention is called “ChatGPT.” ChatGPT (the name of a product by OpenAI) is a type of generative AI known as a large language model or LLM. LLMs can understand human language and can use our spoken or written words and ideas to analyze situations and give output and feedback that is also in the form of human language. These AI models are trained using information from the Internet. An LLM like ChatGPT is doing its work based on having previously read and processed billions of documents, websites, videos, news reports, technical documents, educational textbooks, university bulletins and other sources of knowledge. When the user asks a question or “prompts” an LLM model, the question is processed, and the vast data warehouse is used to structure and formulate an answer that sounds humanlike or natural in tone. This seems impressive to most people who use a model like ChatGPT or others (Gemini, Claude2, Copilot, Llama2, and many more). However, the model only uses super high-speed computer processors to create an answer based on statistics and what the training data would most likely predict as an answer. The LLM uses statistical probabilities to structure and craft answers word-by-word.
Beyond text that might appear on your computer or phone screen, generative AI can also generate other types of information like pictures, video, music, voice or other forms of communication. Depending on the AI model, they might also be able to detect objects in a photo or video, describe them and present information about the object. Examples include the task of describing the maintenance requirements for a specific machine or care recommendations for an animal or resources that specify control options for a crop pest. The best way to learn more is to tinker around and learn what’s possible with these models. More on this later.
Companies and public organizations are just now beginning to embed this technology into new products or services. For example, the AI-Farms project at the University of Illinois has trained an LLM to answer agricultural crop questions. It is trained on several thousand pieces of extension service information developed by University of Illinois researchers and educators. Private companies are also diving deeply into AI-based products to support the ag industry. At the University of Wisconsin-Madison, researchers are beginning to create specialized, trained models to answer questions and serve as a technical support system on agricultural safety issues, farm-level regulatory compliance and management best practices for woodland landowners.
Below are seven farm and ranch-related use cases that we will see in the coming months or years. Some of these are happening now!
- Recognition of specific pests like weeds, diseases, and insects and recommendations for appropriate control.
It is increasingly easier to train a computer using tools such as machine learning to recognize an object’s unique features. The software can examine shapes, sizes, colors and other distinctive features of a particular object, like the spot pattern on a bug or the number of leaves and leaf length-to-width ratio on a plant. If the software can identify a particular pest (or any object) with a high degree of certainty, the task of pulling up the model’s past “training data” that might include university fact sheets, product labels or other information and presenting it to the user is straightforward. It is doubtful that these tools will ever replace the need for a highly skilled and experienced professional. Still, they can act as a well-trained assistant and save time and resources, especially in common, time-consuming and repetitive tasks.
- Summarizing farm and ranch news and information communicated through print, podcasts, YouTube videos or other formats.
Whether you are a big farm or ranch operator or a smaller market grower, agriculture is becoming increasingly specialized. Information overload is a real issue, often adding to producers’ stress and mental load. Many of the newer LLM tools can read text from a report or newsletter that the user inputs, and it will provide short, concise and accurate summaries. It is also possible for these tools to summarize YouTube videos, podcasts and other familiar news sources for the industry. Imagine having a series of 30 or 40 half-hour webinars on a complex topic like robotic applications in dairy farming or vegetable production. AI allows a person to spend an hour showing the AI tool the links and sources of information. Then the software can prepare bullet point summaries of crucial information or even more sophisticated tasks like outlining common threads or differing viewpoints among videos, presenters, pros and cons, and other considerations.
- Machinery and equipment setup, maintenance, troubleshooting, and safety.
In theory, it is now very possible for an LLM to be given access to all the information in a particular operator’s manual for a tractor, harvester, or other machine. This will likely be something that manufacturers will offer in the future. If the operator’s information is read into and processed by AI, rather than reading the manual and searching for the correct information, the user can “ask questions” directly to an AI tool and be given answers that are rooted in the manual. As is the case with all AI training information, it is essential to understand that many sources of information and data are proprietary and protected by copyright law. But this application will be commonplace in the coming five years or sooner.
- Interpreting complicated regulations, standards, and rules and providing farm-specific action plans for compliance.
Whether they are state, federal or local codes, rules, regulations or standards, the documents that dictate best practices and regulatory requirements are densely written and sometimes challenging to understand. An LLM can digest old or new regulatory documents and provide summaries, including easy-to-understand checklists or outlines that make compliance more straightforward.
- Helping with communication – things like complicated family conversations, drafting emails or letters, or training programs for hired employees.
As is the case with all these use cases, generative AI can act as a powerful consulting “assistant” with many unique or routine communication tasks. It should never replace the thoughtful consideration of a real person, but it can help ensure you communicate with clarity and completeness. For example, say you know that your family is due for a multi-generational conversation about estate planning. If an LLM like ChatGPT 4.0 is given a detailed story, context, history, and considerations, it can be used to provide ideas on how to structure conversations, anticipate emotions and responses and suggest ways for everyone to participate. Similarly, it can be used as an idea starter or fact-checker for letters, emails and reports. As is the case with any AI, it is important to be able to explain “what you want” from the software but also be careful not to disclose specific private data or information that might be sensitive or protected.
- Language and technical information translation.
Increasingly, there is interest in using AI to rapidly translate educational information, communicate workplace policies or just routine person-to-person communication. The new generative AI tools are being tested and shown to be just as, if not more accurate, than paid apps or online “translation” tools. Look for more developments in this area of use because an LLM can be used to translate text or spoken word. However, it is possible to “speak” the translated content in a human-sounding voice using generative AI. This will open up amazing possibilities in the workplace and be an essential tool for travel.
- Brainstorming – assisting with strategic planning, evaluating “what if” scenarios, or considering all appropriate points in looking at the pros and cons of a specific decision or alternatives.
When we know we must make a difficult decision or take an essential step in business, often the most challenging part is getting started. Overcoming inertia is one step. But there’s often also a lingering sense that you will not have all the information you need or that you might overlook an important consideration. Again, AI can assist you in getting started. It will not do the work for you or “make” a final decision. But, if you carefully describe your situation in detail, it might suggest ideas, starting points, or pathways you’d not thought about previously. It might also suggest additional resources, sources of expertise, or viewpoints to consider.
As has been mentioned several times in this article – generative artificial intelligence in the form of large language models like ChatGPT and others must never replace human wisdom and decision-making. But it is a tool that can save us considerable time and lead to more well-rounded, holistic and complete “thinking” about important issues. It can also digest vast amounts of complex information and then produce output that is translated into more easily acted-upon steps or practices or literally translated into another language that is more easily communicated to others.
This summer, I will be preparing a step-by-step tutorial that any person interested in agricultural applications of AI try using “free tools” that are available on the web through various open-source platforms. This tutorial will include “walkthroughs” that farmers and ranchers can use based on the seven use cases described in this article that you can easily tailor to meet your own needs and situation. I will also include best practices for “prompts” or carefully formatted and described “questions” that will encourage the LLM to answer in a helpful and useable way. To get on the notification list and receive a link when this tutorial becomes available this summer, email me at shutske@wisc.edu
John Shutske is a Biological Systems Engineering Department professor at the University of Wisconsin-Madison. John’s research, teaching, and Extension work have focused on agricultural technology, safety, risk assessment, human factors, workplace health exposures, and mental health. John also teaches undergraduate and graduate students in the areas of agricultural product design, engineering ethics, professional development and lifelong learning.
John Shutske, Ph.D.
Professor and Agricultural Safety & Health Specialist
Website: agsafety.wisc.edu
Twitter: https://twitter.com/WIAgLeader
LinkedIn: https://www.linkedin.com/in/johnshutske/
Department of Biological Systems Engineering
and UW–Madison Division of Extension
460 Henry Mall
Madison, WI 53706
Email: shutske@wisc.edu
Cell: 608-514-2504
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