ML Image Processing for Targeted Crop and Weed Treatment
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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 sensors to detect and identify agricultural objects, track their growth stages, and apply targeted treatments, such as chemical sprays or mechanical actions, to optimize resource use and reduce unwanted vegetation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional agricultural techniques are used for crop production, then labor and chemical use are high, but productivity and resource efficiency remain limited
Solution Approach 1:
The patent replaces traditional mechanical and chemical agricultural practices with a machine learning-based vision system. The system uses image processing and ML algorithms to detect, classify, and track agricultural objects, enabling automated decision-making that reduces chemical application and optimizes resource usage while maintaining or improving productivity
Solution Approach 2:
The system changes the operational parameters of agricultural treatment by using ML-predicted growth stages to determine optimal treatment timing and dosage. This dynamic parameter adjustment based on real-time object detection and classification enables precise chemical application only when and where needed, reducing overall chemical consumption
2Productivity
If incremental agricultural techniques are applied, then some improvements are achieved, but land, time, and cost efficiency still pose challenges
Solution Approach 1:
The patent implements continuous monitoring and tracking of agricultural objects throughout their growth cycles. The system maintains persistent object identities across multiple time points, enabling uninterrupted observation and continuous optimization of treatment timing, thereby reducing idle time and maximizing productive operational time
Solution Approach 2:
The system performs preliminary detection, classification, and tracking of agricultural objects before treatment is applied. By identifying objects and predicting their growth stages in advance, the system prepares optimal treatment plans beforehand, reducing on-site decision time and enabling faster, more efficient execution of agricultural operations
3Measurement precision
If machine learning algorithms are implemented for target detection, then precision in identifying agricultural objects is improved, but system complexity increases
Solution Approach 1:
The patent segments the complex agricultural monitoring task into distinct functional modules: image acquisition, object detection, classification, tracking, and treatment decision-making. Each module handles a specific aspect of the process, making the overall system more manageable and maintainable while achieving high detection precision through specialized algorithms in each segment
4Loss of substance
If targeted treatment is applied to identified targets, then resource consumption is reduced, but the need for accurate target identification increases system requirements
Solution Approach 1:
The system implements feedback loops where detection results inform treatment decisions, and treatment outcomes feed back into future detection and classification. This continuous feedback mechanism improves target identification reliability over time by learning from actual results and adjusting detection parameters, while ensuring resources are applied only to confirmed targets
Data Source
AI summary
A method includes obtaining, by the treatment system configured to implement a machine learning (ML) algorithm, one or more images of a region of an agricultural environment near the treatment system, wherein the one or more images are captured from the region of a real-world where agricultural target objects are expected to be present, determining one or more parameters for use with the ML algorithm, wherein at least one of the one or more parameters is based on one or more ML models related to identification of an agricultural object, determining a real-world target in the one or more images using the ML algorithm, wherein the ML algorithm is at least partly implemented using the one or more processors of the treatment system, and applying a treatment to the target by selectively activating the treatment mechanism based on a result of the determining the target.