In this digital age, new technologies are emerging that are reshaping farming methods, significantly reducing the dependence on human physical energy and other processes in agriculture. Essentially, the use of artificial intelligence (AI) technology facilitates agriculture by improving crop yields, conserving water, and automating tasks through precision farming, smart irrigation, and early detection of diseases. These cut across the entire agricultural value chain.
When it comes to precision agriculture, AI uses sensor and satellite data to tailor soil treatment and fertilizer application to precise field sections, reducing waste by up to 40%.
When it comes to smart irrigation, it combines soil moisture monitoring with weather data to water plants only when needed, reducing water usage by 30-60%.
Additionally, AI also has the ability to detect pests and diseases. Use computer vision tools like Plantic to analyze smartphone photos and drone images to diagnose crop stress before symptoms become widespread.
It also assists in yield and market forecasting by predicting yield and market price fluctuations, helping farmers plan distribution and sales.
All of this brings practical benefits to producers in terms of reduced input costs. The infrequent use of chemicals and water saves on consumable costs.
Resource conservation: Minimize environmental runoff and protect long-term soil health. Therefore, the use of autonomous labor such as drones and smart robots is being considered to handle repetitive tasks such as weeding and harvesting, especially when manual labor is in short supply.
As an update, the International Federation of Agricultural Research Centers has created a mobile phone app that identifies pests and diseases. So, as the practical application of AI in agriculture continues to develop, two experts from the University of Tennessee explained how AI technology can help farmers optimize production planning, compliance, and costs at the Southern Cotton Ginjo Association conference.
Nowadays, farmers can use tablets from the middle of their corn fields.
While AI is a powerful tool for farmers, data security and recommendation reliability still require special attention.
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For example, artificial intelligence is becoming more commonplace on U.S. farms. However, farmers are advised to always review AI-generated recommendations and use them with caution.
Artificial intelligence is becoming increasingly common in agricultural operations, and farmers can use it to assist with various tasks on the farm. Aaron Smith, professor of agricultural and resource economics and farm bill policy, and Satish Samiapan, associate professor of biosystems engineering and soil science at the University of Tennessee, presented several examples of how AI is already transforming farms across the Mid-South at the Southern Cotton Ginjo Association’s summer conference in Florence, Alabama.
For considering production plans
One of the most powerful applications, Smith described, is a Kentucky farmer using AI to analyze 10 years of production data. According to him, “This report analyzes 10 years of accurate planting and harvest data for corn, soybeans and wheat on the farm,” Smith said. “This is based on his accurate planting and harvesting records from the John Deere Operations Center.”
AI analysis reveals important insights that can have a significant impact on the profitability of grower operations, including delayed planting costs and specific management recommendations. Mr. Smith emphasized the practical value of such analysis.
“How you take this and turn it into actionable decisions is where I’m really seeing the benefit. It’s really helpful in terms of doing analysis,” he said. “It can also be helpful in terms of saving money or showing the potential for more profitable outcomes.”
About important compliance support
Smith also demonstrated how AI can help with regulatory compliance, particularly around pesticide spraying. He used a simple example to show how farmers can upload herbicide labels and ask specific questions about application timing and conditions.
“Is it OK to spray Liberty on cotton, given the accompanying label requirements, current weather conditions and time of year?” he asked. “In our program, we said, ‘Yes, we can probably spray today, but instead of waiting until the afternoon, we’re going to aim to spray between now and late morning.'”
The system automatically ingested weather data and provided legitimacy based on temperature, sunlight, rain, wind, and weed conditions.
But Smith cautioned about verification. Asking leading questions can result in off-label recommendations, so we caution users to be extremely careful and always refer to the source label or documentation. This can be done by asking the program to show you the exact source of the information.
Narrow down to cost optimization
Smith shared another practical application where farmers use AI to optimize input purchasing decisions.
With AI technology, biological seed treatments are evolving beyond the hype.
About remote sensing
Samiapan focused on AI applications using images from satellites, drones, and ground robots. He said his research aims to understand the meaning of photos for farmers and use it to recommend future steps.
One innovative application is detecting stress in plants before visible symptoms appear.
“By examining the spectral information from cotton leaves, we were able to detect a black nematode infestation in less than two weeks before visual symptoms appeared.”
Looking to the future
Both Smith and Samiapan pointed out that AI is still evolving. Samiapan added that although some technologies exist today, AI needs to be commercialized over the next few years to meet growers’ needs. Smith emphasized the importance of data quality and security, distinguishing between consumer-grade AI platforms and enterprise systems that protect proprietary farm data.
As AI continues to develop, both experts see great potential in agriculture. They suggest the importance of understanding how to use these tools effectively while maintaining proper monitoring and validation of AI-generated recommendations.
