Moving Platform Target Detection for Precision Crop Treatment
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Solution Overview
Problem
Current agricultural technologies face challenges in efficiently and sustainably managing land, chemicals, time, and labor to meet the increasing global food demand, with existing methods being incremental and not fully addressing the scale of population growth.
Innovation Solution
A machine learning-based agricultural vehicle system that uses image processing and sensor fusion to detect and control undesirable vegetation by identifying agricultural targets, performing pose estimation, and applying treatments based on real-time data analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional agricultural methods are used to manage land and crops, then labor and chemical usage are high, but productivity and efficiency remain limited
Solution Approach 1:
The patent replaces manual mechanical agricultural operations with an autonomous robotic system equipped with computer vision and machine learning capabilities. The robot uses image processing to identify crops and weeds, then applies targeted treatments mechanically, eliminating the need for broad chemical application and manual labor while improving productivity.
Solution Approach 2:
The autonomous robot performs agricultural tasks independently without human intervention. It autonomously navigates the field, identifies targets using its own sensors, makes treatment decisions through onboard processing, and executes interventions, thereby reducing dependency on external chemical inputs and human labor.
2Productivity
If manual agricultural operations are performed, then labor requirements are high, but precision and efficiency are limited
Solution Approach 1:
The system replaces human manual operations with an autonomous robotic platform that uses computer vision for target identification and automated mechanisms for treatment application. This substitution eliminates labor requirements while dramatically improving operational efficiency through continuous, precise, and rapid intervention capabilities.
Solution Approach 2:
The patent introduces an autonomous robot as an intermediary between human operators and agricultural fields. This intermediary performs all field operations independently, translating high-level objectives into precise actions through onboard sensors, processing units, and actuation systems, thereby eliminating direct human labor while maintaining operational efficiency.
3Measurement precision
If broad chemical treatment is applied to fields, then chemical usage increases, but target precision and resource optimization decrease
Solution Approach 1:
The autonomous robot applies treatments locally and selectively to individual identified targets rather than treating entire fields uniformly. Using computer vision to distinguish between crops and weeds, the system delivers precise localized interventions, minimizing chemical consumption while maintaining high target identification precision and avoiding unnecessary treatment of non-target areas.
Solution Approach 2:
The system replaces broad chemical application mechanisms with precision-targeted delivery systems controlled by computer vision and machine learning. The robot identifies specific weed targets, calculates optimal treatment parameters, and applies minimal necessary treatment only where needed, dramatically reducing overall chemical consumption while maintaining high precision through automated image analysis and real-time decision-making.
Data Source
AI summary
A method includes receiving, by the treatment system, during operation in an agricultural environment, one or more images comprising one or more agricultural objects in the agricultural environment, identifying, in real-time, one or more objects of interest from the one or more agricultural objects by analyzing the one or more images, wherein the analyzing results in a first object being identified as belonging to one or more target objects and a second object being identified as not belonging to the one or more target objects, logging one or more results of the identification of each of the one or more objects of interest and a corresponding treatment decision; and activating the treatment mechanism to treat the one or more target objects.