Mobile Stereo Crop Sensing for High-Fidelity 3D Plant Monitoring
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Solution Overview
Problem
Large-scale plant growth in greenhouses or outdoor fields faces challenges due to complex and varying environmental conditions, with conventional monitoring systems providing limited spatial and temporal fidelity, leading to imperfect sensing and control, which hampers optimal plant growth and health optimization.
Innovation Solution
A mobile platform equipped with sensors mounted at offset positions to capture stereo-spatio-temporal data measurements, allowing for increased fidelity in data collection and processing, enabling better analysis and optimization of plant growth conditions by moving around the growing area and capturing data at multiple dimensions (X, Y, Z) over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional monitoring systems are used in large-scale plant growth operations, then device complexity is reduced, but measurement precision and spatial-temporal fidelity deteriorate
Solution Approach 1:
The system divides the monitoring task into multiple sensor groups positioned at different locations (first sensor group at first position, second sensor group at second position). Each sensor group captures data from its specific viewpoint, and the system processes these segmented measurements together to achieve high-fidelity stereo-spatio-temporal data without requiring a single complex monitoring system
Solution Approach 2:
The patent adds spatial dimensionality by mounting sensor groups at offset positions (different X, Y, Z coordinates) on the mobile platform. This creates stereo vision capability similar to human binocular vision, enabling depth perception and three-dimensional plant characterization that single-point conventional systems cannot achieve
2Measurement precision
If multiple sensors at offset positions are deployed, then measurement precision improves, but device complexity increases
Solution Approach 1:
Each sensor group is configured to generate at least one common type of data measurement, making the sensor groups universal and interchangeable. This multi-functionality allows the system to maintain high measurement precision through multiple sensors while reducing operational complexity, as any sensor group can perform the same measurement functions
Solution Approach 2:
The patent combines data from multiple sensor groups through centralized processing. The first sensor group's measurements and second sensor group's measurements are merged to create comprehensive stereo-spatio-temporal datasets, achieving high fidelity through data integration rather than through complex individual sensor designs
3Productivity
If conventional management approaches are used, then ease of operation is maintained, but productivity and plant growth optimization deteriorate
Solution Approach 1:
The system continuously captures stereo-spatio-temporal data and processes it to generate actionable insights about plant conditions, growth patterns, and environmental factors. This feedback loop enables data-driven decision-making for irrigation, fertilization, and pest management, significantly improving plant growth optimization and productivity while providing clear guidance to operators
Solution Approach 2:
The mobile platform performs preliminary data collection and analysis before critical plant issues develop. By continuously monitoring multiple parameters across space and time, the system can predict and prevent problems such as disease outbreaks, nutrient deficiencies, or water stress before they significantly impact plant health, enabling proactive rather than reactive management
Data Source
AI summary
A mobile platform includes at least one first sensor mounted at a first position on the mobile platform and at least one second sensor mounted at a second position on the mobile platform, where the first position is offset from the second position. The at least one first sensor is configured to capture first data measurements of plants in the growing area. The at least one second sensor is configured to capture second data measurements of the plants in the growing area. Each of the first and second data measurements is associated with a three-dimensional position within the growing area and a time. The first and second sensors are configured to generate at least one common type of data measurement such that at least some of the first and second data measurements represent stereo-spatio-temporal data measurements of the plants in the growing area.