Farmers save 59% of herbicides through computer vision
Zielgruppe
Farmers
Herausforderung
Traditional broadcast spraying wastes enormous quantities of herbicides—farmers spend $25 billion annually on 3 billion pounds of herbicides, with research indicating as little as 1% of pesticides applied via broadcast actually reach the target pest. This leads to environmental pollution, herbicide resistance (250+ resistant weed species), and unnecessary costs.
Lösungsansatz
The system uses boom-mounted cameras that scan over 2,100 square feet of crop area per second. Images are processed with an AI, trained on over 1 million images. The AI distinguishes crops from weeds using technology similar to facial recognition, then precision nozzles spray only where weeds are detected.
Mehrwert
Their solutions saved farmers an estimated 8 million gallons of herbicide and delivered an average herbicide savings of 59%, as of 2024.
Referenzen
John Deere, Blue River Technology
Read more here: https://emerj.com/artificial-intelligence-at-john-deere/
Image credentials: Dan Meyers/ Unsplash
Weitere interessante Use Cases
Enhance productivity with knowledge base and content generation
JPMorgan Chase deployed LLM Suite, a proprietary generative AI assistant powered by OpenAI's technology, to over 200,000 employees across all divisions to boost productivity in writing, research, and coding tasks.
Detect unnoticed out-of-stock inventory in stores
Target built a proprietary Inventory Ledger system using ensemble machine learning models to detect and automatically correct 'unknown out-of-stocks'—situations where inventory systems believe products are in stock but shelves are actually empty.
Transforming Excel Data into Predictive Business Intelligence
Foundation models transform Excel data into forecasts, enabling small companies to predict demand and costs without ML expertise.