The imperative for global food security, coupled with increasing climate volatility, is driving urgent demand for advanced agricultural technologies. This patent aligns with the accelerating trend towards precision agriculture, where data analytics and AI are crucial for optimizing resource use and mitigating risks. Regulatory pressures for sustainable farming practices and consumer demand for resilient food supply chains further amplify the need for solutions that can reduce crop loss and improve operational efficiency, making this technology highly relevant for immediate adoption.
Achieves Low-Cost, High-Accuracy Prediction: Utilizes existing geospatial data and machine learning to significantly reduce implementation and operational costs compared to manual methods or expensive sensors, while accurately predicting waterlogging risk.
Minimizes Yield Loss Through Early Intervention: Identifies high-risk areas before waterlogging occurs, enabling timely preventive measures like drainage or soil improvement, which could significantly reduce crop yield losses.
Promotes Data-Driven Agricultural Management: Supports decision-making based on objective waterlogging prediction maps, moving beyond traditional reliance on experience. This builds a foundation for precise cultivation management by understanding field-specific characteristics through data.
This patent establishes robust protection for a map generation device, method, program, and methods/devices for generating learned models, covering a broad scope across 10 claims. It has demonstrated strong novelty and inventiveness, having overcome five prior art documents during examination, providing licensees with a stable and defensible position for business expansion.
This patent primarily covers geospatial data-driven waterlogging prediction. White space exists in integrating real-time ground sensor data for hyper-local analysis or developing autonomous robotic systems for immediate, targeted field interventions based on the generated maps.
Waterlogging can cause 10-30% crop yield loss. For a 100-hectare farm with ~$650K (AI est.) annual revenue, if 10% of risk areas experience waterlogging and yield drops by 20% in those areas, the annual loss is ~$13.5K (AI est.). Implementing this technology could reduce this loss by 25%, avoiding ~$3.5K (AI est.) annually. For large agricultural corporations managing multiple fields and crops, potential annual losses could range from ~$65K to ~$650K (AI est.). A 10% reduction in these losses could lead to an estimated economic benefit of ~$350K/year (AI est.).
X: Prediction Accuracy & Comprehensiveness
Y: Implementation & Operational Cost Performance