Preparing For Artificial Intelligence
Tuesday, June 11, 2024
Exploring AI use on-farm.
By Dale Cowan
It seems everywhere we turn someone is talking about artificial intelligence (AI) and all its various applications. In agriculture, huge investments are being made in AI platforms ranging from equipment companies to crop inputs.
ChatGPT is probably the most used and accessible platform to most anyone with a computer or mobile device and couple of dollars of disposable income per month. Ask the platform a question and it will come up with an answer from the Internet on anything that is readily available, archived and can be found. ChatGPT at present lacks any reasoning; it only regurgitates what it has found and does a nice job of presenting its findings, but needs some critical thought before it should be acted upon.
What might it actually mean for a farmer if we fast forward a few years? How would AI be used every day or at least weekly on the farm?
Let’s ask Auricle AI a question.
“Based on my current crop of soybeans (name variety) planted May 15, my present soil test information, tillage practice, ground cover, soil type, microclimate in the field, weather forecast, what is the likelihood of white mould developing? Give me a time frame and select the products required with rates, nozzles and volume to be applied and provide an ROI.”
The programming that makes this happen is referred to as a large language model. This supports questions being asked of the data available to the model, which means anyone can ask the question without having to be a computer programmer.
In a matter of a few seconds, an answer is returned. Wow, how great would that be?
There are companies that believe this is all possible. The answer will come from many linked databases, ranging from your own farm data, fungicide manufacturer, nozzle company, sprayer company, weather station data services, your local input supplier, your agronomist (maybe). All these sources of data need to be seamlessly connected to support a decision.
The real question is, will any of them replace your trusted advisor?
To make this a reality, we need to examine where we are today and look at the gaps in connected data.
To start, the foundational piece is that businesses have customers. They have farms that have fields that need a georeferenced boundary. Without this fundamental piece, nothing gets connected. How many farms – with all of their fields with georeferenced boundaries – are sitting in one database connected to the cloud?
Next is a background map of soil type and topography that can be linked to a field boundary seamlessly.
After that is the recording of the following field activities: Tillage; fertility; applications of inputs with dates; rates; products and method (banded, broadcasted incorporated and to what depth); hybrid varieties; plant population dropped; plants emerged; growth models tracking crop growth stage; and observational data from possibly drones, satellites, sensors and manual entry, all linked to a specific field boundary.
Bits and Splits - stock.adobe.com photo
Having sophisticated AI interactions will take massive amounts of connected data collected in near real time from equipment and sensors feeding automatically into georeferenced databases.
Where are we today and where are the gaps? Surprisingly, a lot of the connectivity is possible to build the field foundation information. Sensors on equipment are collecting data and can be fed into software systems. It’s possible to collect all field activities digitally if investments in technology on-farm have been made.
Some of the softer skills may need more work. My colleagues in the United States who are working on data collection offered some interesting insights: The most popular hybrid planted in a certain county was either “1” or “A” – the first entry on a pick list that did not have the hybrids pre-entered. Sometimes the simplest things can limit the usefulness of the data.
To have effective AI, it will take more than one connected database. A lot of different data sources need to be connected. Data privacy, ownership and the liability of the output will need a thorough discussion.
In the spirit of AI, I asked ChatGPT the following question: What can a farmer do to prepare their farm for AI?
Implement systems to collect and manage your data. Jodie Aldred photo
Response from ChatGPT:
Integrating AI into farming operations can enhance efficiency, productivity, and decision-making. Here are several steps a farmer can take to prepare their farm for AI:
- Understand AI applications in agriculture
- Educate yourself about the various AI applications in agriculture, such as precision farming, crop monitoring, predictive analytics, and autonomous machinery.
- Assess farm needs
- Identify specific challenges and areas where AI can make a positive impact on your farm. This could include crop management, soil health, irrigation, pest control, and resource optimization.
- Collect and manage data
- AI relies heavily on data. Implement systems to collect and manage data related to your farm, including weather conditions, soil quality, crop health, and machinery performance. Ensure data is accurate, consistent, and secure.
- Invest in sensors and IoT devices
- Deploy sensors and Internet of Things (IoT) devices to gather real- time data from your farm. These devices can monitor soil moisture, temperature, crop health, and other relevant parameters.
- Implement precision agriculture practices
- Use AI to analyze data and implement precision agriculture practices. This involves optimizing inputs such as water, fertilizers, and pesticides based on specific conditions within a field, leading to more efficient resource use.
- Choose AI tools and platforms
- Explore and choose AI tools and platforms that align with your farm’s needs.
There are various AI solutions available for agriculture, ranging from machine learning algorithms for data analysis to robotics for field operations.
- Invest in training
- Provide training for farm personnel to understand and operate AI technologies effectively. This may involve bringing in experts for workshops or online training courses.
- Integrate AI with existing systems
- Ensure that AI technologies can seamlessly integrate with your existing farm management systems. Compatibility is crucial for a smooth transition and efficient operation.
- Consider autonomous machinery
- Explore the use of autonomous machinery and drones equipped with AI for tasks such as planting, harvesting, and monitoring crop health.
These technologies can improve efficiency and reduce labor requirements.
- Data security and privacy
- Implement robust data security measures to protect sensitive farm data.
This is particularly important as AI relies on vast amounts of data, and ensuring the privacy and security of that data is essential.
- Start small and scale up
- Consider starting with a small-scale implementation of AI on a specific aspect of your farm.
Once you observe the benefits and gain confidence, you can gradually scale up the use of AI across different farm operations.
- Stay informed
- Keep up-to-date with advancements in AI and ag technologies.
Continuous learning will help you adapt to new opportunities and stay ahead of industry trends.
By taking these steps, a farmer can effectively prepare their farm for the integration of AI technologies, leading to improved efficiency and sustainable agricultural practices.
Take it for what it’s worth. It’s not a bad list of things to consider.
Will AI technology replace the trusted advisor? I don’t think it will, but I do believe the advisor that uses this technology will replace the ones who do not. BF