ML-Guided Crop Treatment Targeting With Real-Time Pose Estimation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current agricultural technologies face challenges in efficiently and sustainably managing crop production to meet the increasing global food demand, particularly in effectively detecting and controlling undesirable vegetation, which affects land, chemical, time, labor, and cost efficiency.
Innovation Solution
A machine learning-based system mounted on agricultural vehicles that uses image processing and sensor fusion to detect and control undesirable vegetation by identifying agricultural targets, performing pose estimation, and activating treatment mechanisms based on real-time data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional agricultural techniques are used for detecting and controlling undesirable vegetation, then the process is simple and easy to implement, but the precision and efficiency are insufficient, leading to increased land, chemical, time, and labor costs
Solution Approach 1:
The patent replaces traditional mechanical and manual detection methods with machine learning-based image processing systems. The system uses trained ML models to automatically identify and classify agricultural objects (crops, weeds, flowers) from images captured by cameras or other sensors mounted on agricultural vehicles, eliminating the need for manual field inspection and significantly improving detection precision while reducing labor costs.
Solution Approach 2:
The patent introduces machine learning models as an intermediary between raw image data and treatment decisions. The ML models process images, identify target objects, determine their types (crop, weed, flower), and provide guidance for selective treatment. This intermediary layer enables precise differentiation between desirable and undesirable vegetation, improving both detection accuracy and treatment efficiency.
2Adaptability or versatility
If traditional agricultural techniques are used for treating vegetation, then the method is simple to apply, but the treatment is not selective, resulting in excessive chemical use and increased environmental impact
Solution Approach 1:
The patent applies local quality by enabling selective treatment of individual plants or vegetation patches based on their identified type. The system can apply different treatments (herbicides, fertilizers, water) to different locations in the field based on real-time identification results. This allows precise application of chemicals only where needed (on weeds) while sparing desirable plants, significantly reducing overall chemical usage and environmental impact.
Solution Approach 2:
The patent implements a feedback loop where the ML-based detection system continuously monitors the field, identifies vegetation types, and guides treatment application in real-time. The system processes images, determines which plants are weeds versus crops or flowers, and directs treatment mechanisms to apply chemicals selectively. This closed-loop feedback system ensures treatments are applied only to target objects, improving adaptability and reducing unnecessary chemical application.
3Productivity
If manual detection and treatment methods are used, then the system is simple and low-cost, but the productivity and efficiency are low, unable to meet increasing food production demands
Solution Approach 1:
The patent enables continuous detection and treatment operations by mounting the ML-based detection system and treatment mechanisms on agricultural vehicles that can traverse the field continuously. The system processes images in real-time as the vehicle moves, identifying and treating target objects without stopping. This continuous operation significantly improves productivity compared to manual methods, allowing large areas to be covered efficiently while reducing the time required for vegetation management.
Data Source
AI summary
A method performed by a treatment system disposed on a moving platform, the treatment system having one or more processors, a storage and a treatment mechanism, comprising: receiving one or more images of an environment in which the moving platform is operating; identifying, in real-time, a pose of the moving platform using sensor inputs; identifying one or more target objects by processing the one or more images using a machine learning (ML) algorithm; and controlling the treatment mechanism to treat the one or more target objects by orienting the treatment mechanism towards the one or more target objects at least partially based on the pose.