3D Plant Modeling for Canopy Light Interception Analysis
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Solution Overview
Problem
Current methods for data management in agriculture fail to analyze conditions affecting individual plants within a field or orchard, preventing early identification of plant health and vigor issues and subsequent nutrient or mediation applications to enhance yield.
Innovation Solution
Creating 3D models of agricultural fields using oblique aerial RGB imaging and photogrammetry to segment individual plants, simulate sunlight radiation, determine canopy light interception, and forecast yield based on environmental factors, allowing for precise nitrogen and water requirement calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If data is collected for an entire field or orchard using conventional methods, then overall field coverage is achieved, but individual plant conditions cannot be analyzed
Solution Approach 1:
The patent segments the orchard into individual plant units by creating separate 3D models for each tree using aerial imaging. This allows the system to maintain overall field coverage while simultaneously analyzing individual plant conditions, resolving the contradiction between area coverage and measurement precision.
Solution Approach 2:
The patent transitions from 2D aerial imagery to 3D photogrammetric models, adding a vertical dimension that enables individual plant segmentation and analysis. This dimensional enhancement allows simultaneous achievement of field-wide coverage and plant-level precision.
2Device complexity
If conventional field-level data collection is used, then data processing is simpler, but early insights into plant health cannot be obtained
Solution Approach 1:
The patent performs preliminary 3D modeling and canopy light interception analysis during the growing season, before harvest. This early assessment provides timely insights into plant health and yield potential, allowing growers to take corrective actions while the contradiction between processing complexity and time loss is resolved.
3Productivity
If site-specific plant management is implemented, then yield optimization is improved, but data collection and analysis complexity increases
Solution Approach 1:
The patent replaces complex manual data collection methods with automated aerial imaging and photogrammetry systems. This substitution reduces the operational complexity of site-specific data collection while enabling precise yield optimization through individual plant analysis.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate, early yield prediction and site-specific management by providing insights into canopy light interception and plant-specific requirements, improving crop management and sustainability.
Implementation Method 1
creating 3D models of an agricultural field with multiple plants in the form of a densified point cloud using oblique aerial RGB imaging and photogrammetry
Implementation Method 2
simulating sunlight radiation in the 3D models; determining a shading effect of branches and neighboring plants on each individual plant at any time of the day; determining canopy light interception of each plant
Data Source
AI summary
The embodiments disclose a method comprising creating 3D models of an orchard with multiple plants in the form of a densified point cloud using oblique aerial RGB imaging and photogrammetry, identifying and segmenting individual plants of the orchard from the 3D models, simulating sunlight radiation in the 3D models, determining a shading effect of branches and neighboring plants on each individual plant at any time of the day, determining canopy light interception of each plant, analyzing canopy geometry of each plant in the 3D models, forecasting potential yield of each plant based on the measured canopy light interception and calculating nitrogen and water requirements of each plant based on the potential yield and other predetermined field, environmental and climate factors and validating the yield forecasting model using the canopy light interception data by measuring the actual yield for each plant.