Artificial intelligence (AI) is emerging as a game-changer in the tech sector, with far-reaching implications for every industry. In agriculture, this technology is already making its mark, bringing new possibilities and challenges. Let's explore how AI is transforming the farming landscape.

Revolutionize Agriculture with AI's Power

How It Works

OpenAI's ChatGPT stands at the forefront of AI, integrated into many businesses. It generates text by "reading" internet content and recognizing word contexts. However, it has limitations like "hallucinations" due to relying on public internet data. Retrieval Augmented Generation (RAG) helps avoid these by training on private user information. This gives companies more confidence in AI-generated results. 1: ChatGPT's ability to generate text based on existing patterns is remarkable. It's like a supercharged auto-complete function, constantly predicting the next likely word. But this reliance on the internet can lead to inaccuracies. For example, if the internet source is incorrect, ChatGPT may provide false information. This highlights the need for verification and feedback. RAG, on the other hand, focuses on private data, providing a more reliable source for AI models. 2: The concept of RAG is crucial in ensuring the accuracy and reliability of AI in agriculture. By training on specific, private information provided by users, companies can have more control over the output. This means that when it comes to making decisions in farming, such as crop protection recommendations, the AI is based on accurate and relevant data. It's a step forward in using AI to enhance agricultural practices.

Environmental Impact

AI models consume more energy than traditional data centers. A Google search requires 0.3 watt-hours, while a single ChatGPT question needs 2.9 watt-hours. With 5.3 billion global internet users, this could lead to a significant increase in energy requirements. The International Energy Agency estimates that global energy consumption in the AI and cryptocurrency sectors will double by 2026. 1: The energy consumption of AI is a major concern. Google's total greenhouse gas emissions increased by 48% from 2019 to 2023, mainly due to data center energy consumption. However, Google is taking steps to address this issue. They are developing a new Tensor Processing Unit that is over 67% more energy-efficient. Additionally, they have identified practices to reduce energy requirements by up to 100 times and emissions by up to 1,000 times. 2: Generative AI is also water-intensive. Large language models use GPUs in data centers that need to be water-cooled. Google's data centers consumed 6.1 billion gallons of fresh water in 2023, and global AI demand may require 4.2 to 6.6 billion cubic meters of fresh water in 2027. This highlights the need for sustainable solutions in the use of AI.

How AI Is Being Used

The Farmers Business Network (FBN) introduced Norm, an AI-powered ag adviser based on ChatGPT. It aims to simplify farming processes and broaden farmers' knowledge. FBN's team of agronomists builds recommendations and fine-tunes Norm's responses. 1: Norm focuses on providing responses based on FBN's proprietary data, such as agronomist recommendations and product information. While ChatGPT learns from user inputs, Norm keeps farmers' data private. By asking farmers to share a ZIP code, it can provide location-based responses. This personalized approach enhances the usefulness of the AI for farmers. 2: Norm is currently available for farmers who apply or blend their own crop protection. FBN is continuously adding new tools like a fertility model to assist farmers further. It's also integrated into the online shopping experience, allowing farmers to ask questions while browsing. Looking ahead, FBN plans to bring grain markets and trading tools to Norm.Walther is focused on recommending AI solutions for agribusinesses. He works with various clients to streamline workflows. For example, with an agronomist, he automates data entry processes, saving time and allowing them to cover more acres. 1: By having AI analyze data and make recommendations, agronomists can focus on more strategic tasks. This not only saves time and money but also brings their business to the next level. It shows how AI can be a powerful tool in improving agricultural productivity. 2: AGCO is using AI for market forecasting, quality control, and customer support. AI helps handle heavy customer loads during peak times by pulling relevant information. It also analyzes feedback on machinery to identify potential problems and improve quality control. 3: The use of AI in these areas shows the versatility of the technology in agriculture. It can help businesses operate more efficiently and provide better services to customers. As AI continues to evolve, its applications in agriculture are likely to expand even further.