In agriculture, AI technologies are increasingly used for tasks like crop monitoring and yield prediction. However, inconsistent data quality can introduce biases, affecting decision-making. It's important to understand the measures being implemented to enhance the reliability of these AI technologies to ensure they provide accurate and fair outcomes.
The backbone of AI is data, and so the only way to ensure an improvement in the predictive capacity of AI is to provide it with more and more accurate data. There is a need for international organisations to develop a standard and open-access format for collecting such data that can be accessed by those working on AI in this regard.
Ya Shi He
Great point! XAI is crucial for transparency and trust, especially in agriculture where understanding model predictions can help identify biases or errors caused by poor data quality. Thanks for highlighting that!
s. Rama Gokula Krishnan Absolutely agree! Standardized and open-access data collection is key to improving AI's reliability, especially in agriculture. Collaboration at the international level can really help ensure data accuracy and consistency across different regions
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