Sensor Fusion for Insect Identification in Crop Fields
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Solution Overview
Problem
Current detection technologies for identifying beneficial and harmful organisms in agricultural fields are inaccurate, making it difficult to determine damage thresholds and select appropriate control measures, as they struggle to specify individual pests, especially in low numbers or early stages of development.
Innovation Solution
A method and system that utilize sensors to detect species in agricultural fields, generate species suggestions, and calculate the probability of occurrence based on location and time of detection, incorporating models that include environmental and crop-specific parameters to improve identification accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Difficulty of detecting and measuring
If LiDAR systems are used to detect flying insects, then detection capability is improved, but species identification accuracy is insufficient
Solution Approach 1:
The patent combines multiple sensor types (LiDAR for detection, optical sensors for imaging, acoustic sensors for wingbeat frequency) into an integrated monitoring system. This merging allows the system to leverage the strengths of each sensor type: LiDAR provides excellent detection capability while optical and acoustic sensors contribute to species identification accuracy through image analysis and wingbeat frequency characterization.
Solution Approach 2:
The patent introduces machine learning algorithms and data fusion techniques as intermediaries between raw sensor data and species identification. These computational intermediaries process and integrate data from multiple sensors, transforming the insufficient identification capability of individual sensors into accurate species classification through pattern recognition and comparative analysis.
2Reliability
If manual trap placement and control is used, then infestation control can be achieved, but labor and time consumption increase
Solution Approach 1:
The automated monitoring system performs infestation detection and assessment autonomously without human intervention. Sensors continuously monitor the field, automatically detect harmful organisms, and provide real-time data on infestation levels. This self-service capability eliminates manual trap placement and inspection, significantly reducing labor and time consumption while maintaining reliable infestation control through continuous automated surveillance.
Solution Approach 2:
The patent replaces manual mechanical trap management with an automated electronic monitoring system. Instead of physically placing and checking traps, the system uses sensors, processors, and communication networks to automatically detect, identify, and report infestations. This substitution of mechanical manual operations with automated electronic systems maintains infestation control effectiveness while dramatically reducing labor requirements and time loss.
3Productivity
If attractants are used in traps, then pest attraction is improved, but determination of infestation densities becomes difficult
Solution Approach 1:
The patent extracts and removes attractants from the monitoring system entirely. Instead of using chemical or visual attractants that distort pest behavior and complicate density determination, the system relies on passive detection using sensors that can identify harmful organisms at a distance. This extraction of attractants eliminates the information loss problem while maintaining productivity through automated detection capabilities that can monitor infestations without influencing pest movement or distribution.
Data Source
AI summary
The present invention relates to the identification of beneficial insects and/or harmful organisms in a field for crop plants.The presence of a species in the field is captured by one or more sensors. One or a plurality of suggestions is/are generated as to which species it could be. Based on one or more models, the probability that the detected species could be a proposed species is calculated in each case. The site at which the species is detected, the detection time and preferably further parameters that affect the presence of the proposed species in the field are included in a model. The expressive capacity of the sensor or sensors is increased by means of the modeling.