Plant Identification Mapping for Selective Field Treatment
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Solution Overview
Problem
Existing farming technologies struggle to accurately identify and treat individual plants within a field, particularly when dealing with mixed species, leading to inefficiencies and high labor costs in manual treatment methods, and existing imaging systems fail to effectively distinguish between plant groups.
Innovation Solution
A farming machine equipped with an image sensor and a plant identification model that captures images, identifies plant species or groups, and applies targeted treatments using a control system to selectively treat individual plants based on their species, genera, or characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual treatment methods are used to treat individual plants, then treatment accuracy is improved, but labor costs and time consumption increase significantly
Solution Approach 1:
The patent replaces manual mechanical treatment with an automated system that uses imaging technology to identify plants and robotic mechanisms to apply treatment. The farming machine includes an image sensor to capture images, a plant identification model to process images and identify plant species, and a treatment mechanism to selectively apply treatment, thereby eliminating manual labor while maintaining treatment accuracy.
2Extent of automation
If imaging technology is used to identify individual plants, then automation is improved, but the ability to properly identify individual plants from plant groups deteriorates
Solution Approach 1:
The patent applies segmentation by dividing the plant identification task into multiple processing stages: capturing images of plant groups, processing images to identify individual plants within groups, determining plant species, and selectively treating identified plants. This multi-stage segmentation enables the system to distinguish individual plants even when they are part of dense plant groups.
Solution Approach 2:
The patent introduces a plant identification model as an intermediary between the image sensor and the treatment mechanism. This model processes captured images, identifies plant species, and provides guidance for selective treatment, thereby bridging the gap between automated imaging and accurate plant identification in complex group settings.
3Productivity
If treatments are applied to large areas rather than individual plants, then productivity is improved, but treatment precision deteriorates
Solution Approach 1:
The patent implements local quality by enabling the treatment mechanism to apply different treatments to different locations within the field based on real-time plant identification. The system processes images to identify specific plants or plant groups and directs treatment only to those identified targets, allowing each location to receive appropriate treatment while maintaining overall system productivity through automated operation.
Data Source
AI summary
A farming machine moves through a field and includes an image sensor that captures an image of a plant in the field. A control system accesses the captured image and applies the image to a machine learned plant identification model. The plant identification model identifies pixels representing the plant and categorizes the plant into a plant group (e.g., plant species). The identified pixels are labeled as the plant group and a location of the pixels is determined. The control system actuates a treatment mechanism based on the identified plant group and location. Additionally, the images from the image sensor and the plant identification model may be used to generate a plant identification map. The plant identification map is a map of the field that indicates the locations of the plant groups identified by the plant identification model.