Individual Plant Recognition Using Images and Spatial Attributes
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Solution Overview
Problem
Current technologies are unable to accurately distinguish between individual plants in a field, which is essential for tracking growth, disease progression, and agricultural management, due to insufficient accuracy of position coordinates from sensors like GPS and IMU, especially when plants are close together.
Innovation Solution
Training machine learning models, such as convolutional neural networks (CNNs) and sequence-to-sequence models, using digital images and additional plant attributes like position coordinates, bounding shapes, and temporal data to recognize individual plants by generating unique identifiers or associations between images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If position coordinate sensors (GPS, IMU) are used to identify individual plants, then the system can obtain location data, but the measurement precision is insufficient to distinguish between closely spaced plants
Solution Approach 1:
The patent introduces machine learning models as an intermediary between the sensor data and plant identification. The model processes multiple data sources (images, position coordinates, plant attributes) and transforms them into accurate plant identifications, resolving the precision limitation of individual sensors without adding complex hardware
Solution Approach 2:
The system changes from relying solely on position coordinates to using a combination of parameters including image data, plant attributes (height, width, color), and temporal information. This multi-parameter approach significantly improves measurement precision for distinguishing closely spaced plants
2Measurement precision
If machine learning models are trained with multiple data types (images, position coordinates, plant attributes), then individual plant recognition precision is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the plant recognition system into distinct functional modules: data collection module, machine learning model module, and output module. This segmentation allows each component to be optimized independently and simplifies the overall system architecture despite handling multiple data types
Solution Approach 2:
The machine learning model is designed as a universal system that can process multiple data types (images, position coordinates, plant attributes) through a unified architecture. This multi-functionality reduces system complexity compared to having separate processing systems for each data type
Data Source
AI summary
Implementations are described herein for training and applying machine learning models to digital images capturing plants, and to other data indicative of attributes of individual plants captured in the digital images, to recognize individual plants in distinction from other individual plants. In various implementations, a digital image that captures a first plant of a plurality of plants may be applied, along with additional data indicative of an additional attribute of the first plant observed when the digital image was taken, as input across a machine learning model to generate output. Based on the output, an association may be stored in memory, e.g., of a database, between the digital image that captures the first plant and one or more previously-captured digital images of the first plant.