Automatic detection methods for trefoil stage and seven-leaf stage of corn

Abstract

The invention provides automatic detection methods for a trefoil stage and a seven-leaf stage of a corn. According to the methods, segmentation is automatically carried out on a collected downward viewing image of a corn in the field and an image feature is extracted; and it is determined whether the corn in the image area enters a trefoil stage by utilizing the extracted image feature; and furthermore, on the basis of utilization of a feature of an initial image at the trefoil stage, it is automatically determined whether the corn enters a seven-leaf stage with regard to different sowing modes. According to the invention, an image feature parameter that characterizes the number of leaves of a corn is utilized as a determination basis; real-time determination is carried out on a growth period of the corn; and the detection result has high accuracy; therefore, the methods provided in the invention have an important guiding significance for an analysis of a relationship between a corn developmental phase and a meteorological condition, identification on an agricultural meteorological condition for corn growth as well as for farming activities on corns.

Description

technical field [0001] The invention belongs to the intersection field of digital image processing and agricultural meteorological observation, and specifically relates to an automatic detection method for the three-leaf stage and seven-leaf stage of corn, that is, taking the field corn bottom-view image sequence as the object, and using image features to detect whether the corn is Ways to reach the three-leaf and seven-leaf stages. Background technique [0002] Corn is one of the main food crops in my country, and its planting area is very extensive. In order to improve the yield and quality of maize, it is necessary to understand its development speed and process, and analyze the relationship between its various development stages and meteorological conditions, so as to identify the agrometeorological conditions for maize growth. However, for a long time, the observation of each developmental stage of maize has been mainly through manual observation, which is greatly affe...

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Problems solved by technology

[0003] In 2007, Liu Hongjian and others published the paper "Application of Image Processing Technology in Extracting Corn Image Skeleton" on "Agricultural Network Information", which used image processing technology to study the extraction technology of corn plant type skeleton, but the paper's The method is only suitable for a single corn plant in a single background, but not suitable for complex field conditions; in 2010, Li Rongchun et al. published a paper on "Maize Science" "Research on the Monitoring of Summer Maize Population Growth Based on Image Processing Technology". The method of extracting ground coverage is used to estimate the leaf area index (LAI) and dry matter accumulation (DMA) of summer maize, and establish the regression relationship between coverage and leaf area index (LAI) and dry matter accumulation (DMA) model, so as to complete the estimation of the growth of summer maize populations, but only using the image feature of coverage cannot accurately observe the developmental stages of maize; In "Research on Dynamic Monitoring Technology", a method for measuring the growth parameters of field corn based on binocular stereo vision was proposed, and some growth parameters of winter wheat and summer corn were measured. , there are many environmental factors, and the resolution of the image is limited, so it is difficult to carry out 3D reconstruction of the plant type of field corn in practical applications, so this method is not feasible in the observation of the development period

To sum up, although many related unit technologies have appeared in crop growth monitoring, due to certain limitations, it is difficult to apply them to the automatic observation of crop development in the actual field environment.

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