OpenAI · Elon Musk · MIT Technology Review
Agriculture is ready for AI, but its data isn’t
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Artificial intelligence is transforming what is possible in agriculture, but industry leaders should be wary of investing in AI without first laying the groundwork.
Key facts
- For a company like Wilbur-Ellis, a 104-year-old, family-owned agricultural distributor, that means understanding who your customers are, which fields they farm, which inputs they need
- Artificial intelligence is transforming what is possible in agriculture, but industry leaders should be wary of investing in AI without first laying the groundwork
- However, what AI vendors usually won’t tell you is that these solutions are only effective if you have a clean, solid data foundation
- In each case, the AI is failing because the data it was trained on was not sufficient to produce trustworthy outputs
Summary
The use cases are promising, especially for an industry navigating volatile fertilizer costs, unpredictable weather, and margins that leave little room for error. However, what AI vendors usually won’t tell you is that these solutions are only effective if you have a clean, solid data foundation. The promise is compelling, but what rarely comes up is the question of whether the data foundation underneath those promises is accurate and complete. For instance, a yield prediction model fed inconsistent historical data will generate imprecise forecasts.