Agricultural Sprayer Boom with Image-Based Weed Detection
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Solution Overview
Problem
Existing vehicle-based chemical spraying systems are complex, incompatible with different manufacturers' equipment, and prone to errors due to limited cross-talk between electronics and mechatronics systems, leading to inaccurate row identification, excessive complexity, and wastage of chemicals in varying environmental conditions.
Innovation Solution
A system with a boom arrangement of electronically controllable sprayer nozzles and image-capture devices that uses geospatial location correction data and a defined confidence threshold to accurately identify crops and weeds, adapt to uneven land, and operate independently of row identification, ensuring precise chemical application regardless of environmental changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional GPS is used for location determination, then the system can be implemented with standard technology, but the location accuracy deteriorates to 1-10 meters error range
Solution Approach 1:
The system uses a universal communication interface that enables compatibility with different manufacturers' electronic and mechatronic systems. The standardized data exchange protocol allows the spraying system to work with various vehicle platforms and sensor systems, making the high-accuracy positioning capability universally applicable across different agricultural machinery configurations.
Solution Approach 2:
The system replaces conventional mechanical GPS positioning with an electronic communication-based positioning system that integrates multiple data sources including satellite positioning, inertial sensors, and electronic map matching. This electronic substitution enables centimeter-level accuracy by processing and fusing multiple signal sources through electronic control units.
2Adaptability or versatility
If row-based processing is implemented, then the system can identify crop rows, but the system fails when proper rows are not demarcated in the agricultural field
Solution Approach 1:
The system dynamically adapts its processing mode based on field conditions. When rows are properly demarcated, it uses row-based processing for precise targeting. When rows are not demarcated, it automatically switches to a grid-based or feature-based processing mode, allowing the same system to handle diverse field configurations without requiring manual reconfiguration.
Solution Approach 2:
The system changes its operational parameters based on detected field characteristics. It monitors row demarcation quality and adjusts processing parameters such as targeting methodology, navigation mode, and spray activation criteria accordingly. This parameter adaptation enables the system to maintain effectiveness across varying field preparation states.
3Measurement precision
If multiple sensor units are used to improve detection accuracy, then the detection capability is enhanced, but the system becomes over-engineered with too much processing and complexity
Solution Approach 1:
The system performs preliminary processing and filtering of sensor data before full analysis. It pre-identifies potential targets of interest using simple detection algorithms, then applies more complex multi-sensor fusion only to these candidate targets. This preliminary action reduces the overall processing load and energy consumption while maintaining high detection accuracy for actual targets.
Solution Approach 2:
The system uses a tiered sensing approach where basic detection is performed by all sensors continuously, but full multi-sensor fusion and detailed analysis are applied selectively to regions of interest. This partial application of full processing power reduces energy consumption while maintaining detection accuracy where it matters most.
4Measurement precision
If chlorophyll sensors are used to distinguish crops from weeds, then spectral detection is enhanced, but the system still fails to accurately differentiate between two green-looking objects
Solution Approach 1:
The system merges data from multiple sensing modalities including visible spectrum imaging, near-infrared detection, thermal sensing, and spatial pattern recognition. By combining these different types of information, the system creates a multi-dimensional characterization of each detected object, enabling differentiation between crops and weeds based on the unique combination of their spectral, thermal, and spatial signatures rather than relying on a single sensor type.
5Manufacturing precision
If conventional spraying systems are used, then the system structure is simple, but chemical application accuracy deteriorates in varying environmental conditions
Solution Approach 1:
The system continuously monitors environmental conditions such as wind speed, temperature, humidity, and plant stress indicators during operation. It uses this real-time feedback to dynamically adjust spray parameters including droplet size, spray rate, and target selection criteria. This feedback mechanism enables the system to maintain precise chemical application accuracy across varying environmental conditions by adapting to current field conditions.
Data Source
AI summary
A system mounted in a vehicle includes a boom arrangement including a predefined number of electronically controllable sprayer nozzles and a plurality of image-capture devices. One or more hardware processors of the system are configured to obtain a plurality of images from the plurality of image-capture devices and receive geospatial location correction data from an external device. The one or more hardware processors are configured to execute mapping of pixel data of weeds or a crop plant in an image to distance information and cause a specific set of electronically controllable sprayer nozzles from amongst the predefined number of electronically controllable sprayer nozzles to operate based on a defined confidence threshold.