Satellites, AI to help certify fields growing organic cotton
Cotton fields that meet predetermined standards and those that demonstrate potential for a seamless transition to organic cultivation will be supported by the initiative to introduce AIs and satellites.
Points to ponder:
- Collaboration: The European Space Agency (ESA), the Global Organic Textile Standard (GOTS), and the artificial intelligence company Marple are all involved in the initiative.
- Objective: By developing cutting-edge risk assessment methods, the primary objective is to enhance organic cotton’s integrity and deter supply chain fraud.
- AI and satellite data: AI models will be trained using ESA satellite data, allowing cotton fields in India to be automatically classified according to organic cultivation standards.
- How to Identify a Cotton Field: The satellite data will be “read” by AI models, who will then classify and identify cotton fields that comply with predetermined organic standards.
- Progress Backing: The initiative will also help cotton fields that show that traditional and eco-friendly farming methods can be used to seamlessly transition to organic cultivation.
- Accurate Estimates of Yield: By consolidating standard yield measurements, the undertaking will assist GOTS with producing precise evaluations of natural cotton yields in unambiguous areas.
- Test Run: In 2021, the pilot program was a huge success in Uzbekistan, where it was able to tell the difference between organic and conventional cotton fields with an accuracy rate of 98%.
- India’s implementation: The undertaking will be executed across different cotton-developing locales in India, where natural cotton creation assumes a huge part.
- Opportunities in the economy: New economic opportunities for them and their communities will result from an increase in the number of small-scale farmers participating in the certified organic market.
- Demand from Clients: The drive expects to address the raising customer interest in natural cotton inside the material business.
- Timelines: The project’s first results are expected to be available by the end of 2023.