Insect flower visiting behavior analysis method based on machine learning model

Abstract

The invention discloses an insect flower visiting behavior analysis method based on a machine learning model, which relates to the technical field of machine learning, and comprises the following steps: acquiring insect flower visiting videos of set types of flowering plants through video acquisition equipment within a set time period, and according to the types, distribution regions and floweringseasons of the flowering plants, selecting a corresponding insect identification model from a pre-created insect identification model set, inputting the insect flower visiting video into the insect identification model to obtain the type of each insect in each frame of image of the insect flower visiting video, and according to the type of each insect in each frame of image of the insect flower visiting video, and obtaining a flower visiting behavior analysis result of each insect on the same kind of flowering plants in the same time period. The above steps are repeated to obtain the flower visiting behavior analysis results of the insects on the flowering plants of the same kind in the same time period, the flower visiting behavior analysis results of the insects on the flowering plantsof different time periods, different regions and different kinds are obtained, the insect kind identification accuracy is improved, the insect flower visiting behavior analysis result accuracy is guaranteed, and the expandability is expanded.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a method for analyzing insect flower-visiting behavior based on a machine learning model. Background technique [0002] Whether in natural ecosystems or agricultural ecosystems, most flowering plants rely on insects including flies, butterflies, beetles, and bees for pollination to maintain their own reproduction and generational continuity. Pollinating insects are rewarded with food as they pollinate plants. In the long evolutionary process, this mutualistic relationship has promoted the co-evolution of insects and plant flower organs, that is, some specific plants depend on specific insects for pollination. Therefore, the diversity of pollinators in an ecosystem is important for maintaining the diversity of plant species. [0003] To study the diversity of pollinator insects, it is first necessary to clarify which types of insects visit which types of plants, and on ...

AI Technical Summary

Problems solved by technology

[0004] At present, the identification method of insect species mainly uses the image recognition algorithm to identify the species of insects in the video of each insect visiting flowers. Only by using the existing image recognition algorithm to identify the species of insects in the image and according to the species, study the flower-visiting behavior of pollinators. Due to the influence of the light intensity in the collection environment, the clarity of each insect’s flower-visiting video is inconsistent. For clear Insect flower-visiting video with low accuracy, the accuracy of insect species identification is low, resulting in inaccurate analysis results of insect flower-visiting behavior

In addition, the patent application with application number 201710662841.2 discloses a method and system for insect recognition and early warning. This application is only applicable to images containing a single insect. When there are multiple insects in the image, the image must be segmented and Only the single insect can be identified, resulting in low efficiency, and the trained deep learning recognition model disclosed in the application only recognizes insects based on the static characteristics of the insect, and the accuracy is not high

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