Monocular Depth Estimation for Plant Treatment Precision
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Solution Overview
Problem
Current farming systems face challenges in accurately identifying and treating individual plants in a field due to limitations in existing depth sensing technologies, which are often costly, difficult to calibrate, and require controlled environments, leading to error-prone operations in farming machines.
Innovation Solution
A farming machine equipped with one or more sensors that capture images and apply a depth identification module, using a convolutional neural network to extract depth information from visual data, classify plants, and determine treatment actions, while compensating for ground planes and identifying crops versus weeds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If depth sensing technology is used to identify and treat individual plants, then measurement precision of plant depth is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces complex mechanical depth sensing systems (LIDAR, structured light, time-of-flight sensors) with a monocular camera system combined with neural network-based monocular depth estimation. This substitution maintains depth measurement capability while dramatically reducing system complexity, cost, and calibration requirements.
Solution Approach 2:
The patent uses a single camera to capture visual information and generates a depth map as a computational copy of the scene. This depth map serves as a virtual representation of depth information without requiring physical depth sensors, thereby simplifying the hardware system while preserving measurement capability.
2Measurement precision
If traditional depth sensing systems are used in farming machines, then depth information is obtained, but reliability decreases due to calibration difficulties and controlled environment requirements
Solution Approach 1:
The monocular depth estimation system is self-calibrating and does not require controlled environments or complex calibration procedures. The neural network model automatically adapts to varying lighting conditions, camera angles, and field environments, making the system reliable for mobile farming applications without external calibration infrastructure.
Solution Approach 2:
The system changes the operational parameters from fixed, controlled environment requirements to adaptive, variable field conditions. The neural network model processes visual information under diverse lighting, weather, and motion conditions, transforming the system from environment-sensitive to environment-adaptive, thereby improving reliability.
3Measurement precision
If multiple sensors (visual and depth) are used to identify plants, then classification accuracy is improved, but device complexity and error propagation increase
Solution Approach 1:
The patent merges the functions of visual sensing and depth sensing into a single monocular camera system. The same camera that captures visual information for plant identification also provides depth information through monocular depth estimation, eliminating the need for separate depth sensors and reducing overall system complexity while maintaining classification accuracy.
Solution Approach 2:
The monocular camera system performs multiple functions: visual capture for plant identification, monocular depth estimation for depth information, and potential panoptic segmentation for comprehensive scene understanding. This multi-functional approach replaces multiple specialized sensors, reducing complexity while preserving or enhancing measurement precision.
Data Source
AI summary
A farming machine includes one or more image sensors for capturing an image as the farming machine moves through the field. A control system accesses the image(s) and creates a labelled three-dimensional point cloud representing the field. The control system identifies and treats plants based on the labelled point cloud. To do so, the control system applies pre-processing functions to the labelled point cloud to determine characteristics of the field and/or modify labels in the point cloud. Point clusters in the point cloud are identified as plants, crops, weeds, ground, etc., using the determined characteristics and modified labels. The control system derives feature values for the plants based on the determined characteristics and labels. If the feature value indicates that the plant should be treated, the farming machine actuates a treatment mechanism to treat the plant.