Automated Pest Detection in Horticulture Using Image Analysis
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Solution Overview
Problem
In modern industrial horticulture, detecting pest infestations and assessing associated risks across multiple locations and plant types is challenging due to the lack of comprehensive information and effective solution identification for master growers.
Innovation Solution
A computer-implemented method using image data from visual observer devices to classify pests, generate distribution data, and calculate risk levels, providing recommendations based on pest classification, count, and destructiveness profiles, and offering filtered solutions tailored to grow operation preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual pest inspection methods are used by master growers, then operational simplicity is maintained, but detection precision and information completeness deteriorate
Solution Approach 1:
The patent replaces manual visual inspection by master growers with an automated computer vision system using image capture devices, processors, and machine learning classifiers. This substitution of mechanical/manual detection with automated optical and computational systems directly improves detection precision while eliminating the need for extensive manual labor and expert presence.
Solution Approach 2:
The system enables self-service detection where the grow operation's own infrastructure (image capture devices, processors, classifiers) performs pest detection autonomously without requiring external expert intervention. The master grower receives automated alerts and information rather than needing to personally inspect each location, improving detection capability while reducing operational complexity for the human operator.
2Loss of information
If comprehensive data collection across multiple locations is implemented, then information completeness improves, but loss of time and operational efficiency worsen
Solution Approach 1:
The patent implements continuous automated image capture and processing across multiple grow operation locations, eliminating interruptions and gaps in data collection. Image capture devices continuously monitor plants, and the system processes images as they are captured, ensuring complete information availability without the time losses associated with manual inspection schedules or human availability constraints.
Solution Approach 2:
The system performs preliminary automated analysis of images as they are captured, generating pest detection results and risk assessments before the master grower needs to review the information. This preliminary processing ensures that when the grower accesses the data, comprehensive analysis is already complete, eliminating the time required for manual review and decision-making.
3Measurement precision
If automated image analysis is deployed, then detection precision improves, but device complexity increases
Solution Approach 1:
The patent segments the complex automated detection system into distinct functional modules: image capture devices for data acquisition, processors for image analysis, machine learning classifiers for pest identification, and risk assessment modules for evaluation. This segmentation allows each component to be optimized independently and simplifies deployment and maintenance while maintaining high overall detection precision through coordinated operation of specialized subsystems.
Data Source
AI summary
Disclosed are techniques for detecting and mitigating pest infestations within a grow operation. In some embodiments, such techniques comprise receiving, from a visual observer device, image data associated with a location within a grow operation. The image data is then used to determine at least one pest classification and a count associated with the pest classification. Distribution data is generated based on the at least one pest classification, count, and location. A level of risk can then be determined based on the distribution data. In some embodiments, if the level of risk is greater than a threshold level of risk, a recommendation may be generated based at least in part on the distribution data.