Maximising Farm Crop Yields

Problem

Precision farming aims to maximise crop yields while minimising the use of resources such as water, fertilisers, and pesticides. However, traditional methods often rely on historical data that can be confounded by factors such as weather conditions and crop types, leading to inaccurate predictions and suboptimal farming practices. Standard AI models often fail to distinguish between correlation and causation, resulting in inefficient resource use and lower (sometimes even worse than without AI) yields.

Solution

CausaDB uses causal models to de-confound historical data, accurately identifying the true factors that impact crop yields. This allows for more precise optimisation of planting, watering, and pesticide use. By leveraging our API, farmers can integrate CausaDB’s advanced analytics into their existing precision farming systems. This results in more efficient farming operations and maximised crop yields. Built-in explainability and uncertainty quantification provide farmers with clear insights and confidence in the recommendations.

Value

• Optimise farming operations to maximise crop yields
• Minimise resource use such as planting, watering, and pesticides
• Improve sustainability and profitability of farming practices

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