Autonomous Payload Selection for Precision Plant Treatment
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Solution Overview
Problem
Conventional agricultural treatment methods are inefficient and wasteful, as they rely on coarse resolution applications of chemicals, often targeting entire fields or rows of crops rather than individual plants, leading to unnecessary resource expenditure and environmental impact, and struggle to accurately identify and treat specific botanical objects like buds or blossoms within complex vegetation structures.
Innovation Solution
An autonomous agricultural treatment delivery system equipped with sensors and precision navigation, using computer vision and machine learning to identify and target specific agricultural objects, such as buds or blossoms, and apply treatments with micro-precision via emitters that propel agricultural projectiles along calculated trajectories, allowing for precise application of fertilizers, herbicides, or other treatments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If conventional coarse resolution chemical application methods are used to treat entire fields or rows of crops, then coverage area is improved, but treatment precision and resource efficiency deteriorate
Solution Approach 1:
The system segments the treatment area from field-level to row-level to individual plant-level, enabling progressive refinement of treatment precision while maintaining comprehensive coverage through multi-scale operational capability
Solution Approach 2:
The system applies different treatment strategies to different spatial scales: coarse application for area coverage, fine application for precision treatment of individual plants, buds, or blossoms based on locally detected conditions
2Productivity
If conventional spray methods with boom sprayers are used to disperse chemicals, then application speed is improved, but chemical waste and environmental impact deteriorate
Solution Approach 1:
The system extracts only the necessary treatment actions from comprehensive field treatment by identifying specific plants, buds, or blossoms requiring treatment, eliminating unnecessary chemical application to non-target areas
Solution Approach 2:
The system applies partial action by treating only the specific portions of crops that require treatment rather than applying chemicals uniformly across entire fields, reducing overall chemical usage while maintaining treatment effectiveness
3Area of stationary object
If multi-spectral imagery from satellites or aircraft is used for crop analysis, then large area monitoring is improved, but measurement precision for individual plants deteriorates
Solution Approach 1:
The system segments monitoring from area-level to individual plant-level by using sensors mounted on vehicles to capture data at multiple scales, enabling both broad area coverage and high-resolution individual plant detection
Solution Approach 2:
The system transitions from two-dimensional satellite/aircraft imagery to three-dimensional spatial data collection by using vehicle-mounted sensors that capture images from multiple angles and distances, enabling precise identification of individual plants within the broader field context
Data Source
AI summary
Computer vision and autonomous identification of objects are used for the application of a treatment to an object. Robotics and mobility technologies navigate a delivery system which is configured to identify and apply an agricultural treatment to an identified agricultural object. The delivery system identifies a subset of payloads to provide one or more actions based on data representing a policy for one or more subsets of agricultural objects. The delivery system causes one or more cartridges to be charged based on the subset of payloads.