Intelligent analysis system applied to behavioristics of multiple types of high-density tiny insects

Abstract

The invention discloses an intelligent analysis system applied to behavioristics of multiple types of high-density tiny insects. The intelligent analysis system comprises an operation platform subsystem and an analysis subsystem, the operation platform subsystem is used for providing an activity medium capable of obtaining the behaviors of the tiny insect bodies and shooting the behaviors of the tiny insect bodies; a behavior acquisition module in the analysis subsystem acquires behavioral videos of high-density tiny insects through the operation platform subsystem, an analysis modeling module acquires behavioral big data of various types of high-density tiny insects according to the behavioral videos and establishes a behavioral model, and a simulation prediction module performs simulation prediction on the high-density tiny insects according to the behavioral model. The comprehensive prevention and control strategy and the prevention and control effect of the tiny-class pests in the field ecosystem are dynamically predicted, and the visual demonstration module is used for displaying the behavior data. The method can accurately and efficiently obtain the behavioral big data resources of the minimal class of pests, carries out multi-dimensional analysis modeling, and provides a favorable tool for scientific research personnel to explore the behavior mechanism between biocenosis in a field ecosystem.

Description

technical field [0001] The present application belongs to the technical field of biological behavior research, and in particular relates to an intelligent analysis system applied to the behavior of various types of high-density tiny parasites. Background technique [0002] Since the rapid development of deep learning technology, it has become an inevitable trend to use computer vision to process and analyze video data in all walks of life to improve the accuracy and efficiency of target detection and reduce costs. In the field of plant protection (especially very small targets, such as mites), there is no mature analysis system that can meet the needs of various types of high Due to the demand for quantitative analysis and modeling of density behavior, most researchers can only use the research mode based on manual observation and statistics, supplemented by computer analysis. This mode directly leads to low research efficiency, high errors, and is limited by a single number...

AI Technical Summary

Problems solved by technology

In the field of plant protection (especially for very small targets, such as mites), there is no mature analysis system that can meet the needs of multiple types of high Due to the demand for quantitative analysis and modeling of density behavior, most researchers can only adopt a research mode based on manual observation and statistics, supplemented by computer analysis. This mode directly leads to low research efficiency, high error, and is limited by the single number of insects. or fewer restrictions, resulting in a single factor in the research model, unable to fit the habitat where multiple random factors coexist in the field

Although, with the continuous development of computer technology, many researchers have enhanced the weight of computer-aided research and development design, and used machine vision to innovate many research methods. In terms of the behavioral analysis of single or a few insects that are clearly visible to the naked eye Remarkable results have been achieved, but these innovations are not yet sufficient for the behavioral research analysis of very small insects on the leaf habitat of damaged plants at high densities

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