How Farmdar Achieves 95% Accurate Sugarcane Yield Predictions Using AI-Driven Satellite Analytics

Farmdar, an agritech company operating in Thailand, Singapore, Pakistan, and Brazil, has developed AI-powered platforms CropScan™ and YieldPro™ to improve sugarcane yield predictions for millers. Traditional methods relying on manual sampling and public satellite data often produce inconsistent and inaccurate results due to infrequent updates and low resolution, leading to revenue losses. Farmdar’s platforms automate crop identification and health monitoring, reducing the need for manual checks and providing timely, field-level insights.

The company selected PlanetScope® satellite imagery for its high revisit rate and resolution, enabling accurate crop type identification and health tracking across vast farmlands. CropScan automates crop identification, while YieldPro monitors crop health from planting to harvest, even during monsoon seasons when cloud cover is common. Both platforms aim to optimize mill capacity and farm yields by providing reliable, scalable data.

Farmdar’s accuracy claims were initially met with skepticism, but field-based validation activities confirmed the 95% accuracy rate for yield predictions. Co-Founder Muhammed Bukhari noted that procurement and harvest planning teams, once doubtful, became strong advocates after rigorous testing. The measurable impacts on efficiency and cost savings have reinforced customer confidence in the technology.

By integrating Planet’s satellite data with its proprietary platforms, Farmdar enables millers to manage millions of acres with precise, timely insights. The collaboration extends to future plans, including research into Planet’s Tanager™ hyperspectral constellation for applications such as emissions detection and vegetation health monitoring. Interested parties can explore Planet’s e-book or contact an agriculture advisor for further details.