Spray Nozzle Degradation Detection in Autonomous Agricultural Vehicles
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Solution Overview
Problem
Existing fault detection systems in agricultural spray systems struggle to reliably identify all faults causing underspray or overspray during targeted spray operations, leading to inconsistent spray performance.
Innovation Solution
A system that detects statistical anomalies in nozzle instructions using image sensors and generates composite scores to identify deviations, allowing for automatic adjustment or fallback modes, and utilizes machine-learned models to predict and compensate for degradation in spray performance based on data from multiple vehicles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing fault detection systems are used to monitor spray performance, then some faults can be detected, but the systems fail to reliably identify all faults causing underspray or overspray
Solution Approach 1:
The system segments the monitoring task by dividing the fleet of agricultural vehicles into multiple data sources and processing the information in hierarchical stages. Individual vehicle data is first processed locally, then aggregated at the fleet level for comprehensive anomaly detection, allowing reliable fault identification without requiring complex centralized monitoring of every parameter simultaneously
Solution Approach 2:
The system uses universal composite scores that can detect multiple types of spray performance deviations (underspray, overspray, nozzle failures) across different vehicle types and operating conditions. This multi-functional approach enables a single detection mechanism to reliably identify various fault conditions without requiring separate specialized detectors for each fault type
2Manufacturing precision
If targeted spray operations are implemented to reduce crop stress and costs, then spray precision is improved, but the system becomes more vulnerable to performance degradation from component faults
Solution Approach 1:
The system implements continuous feedback by monitoring actual spray performance against expected performance through composite scores. When deviations are detected indicating component faults or performance degradation, the system provides feedback signals that trigger alerts to operators and can initiate corrective actions, ensuring consistent spray performance throughout targeted operations
Solution Approach 2:
The system performs preliminary detection of potential faults by analyzing trends in spray performance data before actual underspray or overspray occurs. By identifying degradation patterns early through composite score analysis, the system enables preventive maintenance actions that maintain spray precision and prevent performance consistency issues before they affect crop treatment
3Productivity
If individual nozzle control is used for targeted spray to apply precise droplet sizing, then cost is reduced and effectiveness is improved, but it becomes difficult to detect faults causing local spray degradation
Solution Approach 1:
The system merges data from multiple individual nozzle measurements into aggregate composite scores that highlight abnormal patterns. By combining information across multiple nozzles and vehicles, the system detects faults in individual nozzles that would be difficult to identify when examining single nozzle data in isolation, maintaining productivity while improving fault detection capability
Solution Approach 2:
The system adds a new dimension of analysis by examining spray performance data across the fleet dimension rather than only at the individual vehicle or nozzle level. This dimensional shift allows detection of faults in individually controlled nozzles by comparing performance patterns across multiple vehicles and nozzles, making fault detection easier without sacrificing spray operation efficiency
Data Source
AI summary
For each of a plurality of spray nozzles of an autonomous agricultural vehicle, a set of instructions provided by the autonomous agricultural vehicle to the spray nozzle is accessed. Each instruction in the set is generated by analyzing a respective image of a portion of a geographic area captured by the autonomous agricultural vehicle. A spray performance of at least one spray nozzle from among the plurality of spray nozzles is identified as an outlier by analyzing the sets of instructions respectively provided to the plurality of spray nozzles. An action with respect to the at least one spray nozzle identified as the outlier is performed. The action may be to place the vehicle in fallback state in which a targeted spray may be switched to broadcast spray.