Drone Land Analysis Using AI Plant Health and Soil Moisture Mapping
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Solution Overview
Problem
Current methods for estimating plant health and soil moisture levels are subjective and imprecise, often relying on transect samples that may not represent the entire site accurately due to terrain and habitat accessibility issues.
Innovation Solution
A land analysis system utilizing an unmanned aerial vehicle equipped with cameras and an altimeter, which captures images and altitude data along a predetermined flight path, processes them using machine learning models to identify plant material, determine geographic coordinates, and assess health and moisture levels, providing objective and comprehensive site coverage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a biologist visits the site to collect plant and soil samples along a transect, then the biologist can analyze samples in a lab to identify plant health and soil status, but the estimates are subjective and imprecise because samples are taken from only a portion of the site
Solution Approach 1:
The patent uses drone-captured images as optical copies of the entire site to create comprehensive plant health assessments. Instead of physically sampling only portions of the site, the system captures visual copies of all areas and analyzes them using machine learning models, thereby achieving precise estimates across the complete site area without the subjectivity of manual sampling.
Solution Approach 2:
The patent replaces the mechanical sampling process (physical collection of plant and soil samples by biologists) with an automated optical and computational system. Drones capture images, and machine learning algorithms automatically analyze plant health from these images, eliminating the need for manual field sampling and laboratory analysis while improving precision and objectivity.
2Area of stationary object
If a biologist manually samples a specific area or portion of the site, then the biologist can analyze samples to estimate plant health, but the coverage is limited and does not represent the entire site accurately
Solution Approach 1:
The patent employs dynamic drone flight paths that can be adjusted to cover the entire site efficiently. The system transitions from static, limited transect sampling to dynamic aerial surveillance that captures images across all areas of the site, maximizing coverage while minimizing the time required through automated flight patterns and real-time image processing.
Solution Approach 2:
The patent moves the assessment from a two-dimensional ground-level transect approach to a three-dimensional aerial perspective. By capturing images from above, the system can cover the entire site area simultaneously, including hard-to-reach areas, thereby expanding coverage without proportionally increasing the time required for assessment.
3Measurement precision
If traditional sampling methods are used to assess plant health, then the process is simpler to implement, but the data precision and objectivity are reduced
Solution Approach 1:
The patent implements a feedback loop where machine learning models are trained on labeled training data from the site, then used to automatically assess plant health in new images. The system continuously improves precision by using the captured images and analysis results to refine its models, providing increasingly accurate and objective assessments while maintaining high levels of automation throughout the process.
Data Source
AI summary
A land analysis system uses drone-captured images to detect plant health and/or soil moisture levels at a site. For example, the system instructs a drone to fly along a flight path, capture images of the land below, and measure altitude data. The system processes the images using, for example, artificial intelligence, to identify locations at which plant material may be present. The system then further processes the images to identify the plant health of the plant material at the identified locations. The system further uses the altitude data to determine the strata of plants at the identified locations. Optionally, the system can further process the images to identify the soil moisture levels at the identified locations.