Leaf wetting time monitoring method and system

Abstract

The invention discloses a leaf wetting time monitoring method and system, and the method and system can calculate the wetting time of a leaf more accurately. Moreover, the method and system does not need to carry out the adjustment of equipment in the whole process according to the growth condition of plants, and are simper in calculation process. The method comprises the steps: S1, collecting a fluorescence image of the leaf; S2, carrying out the clustering segmentation of the fluorescence image through employing K-means clustering, carrying out the binarization of a clustering segmentation result, and obtaining a binary image; S3, carrying out the correction of the binary image in a mode of on / off alternate filtering; S4, enabling the preset water drop shape features and size to serve as the judgment standard for judging whether the leaf is wetted or not, employing a support vector machine based on the statistical learning theory to serve as a classifier for distinguishing whether the fluorescence image of the leaf is wetted or not, carrying out the recognition of the corrected image, and obtaining the number of images of the wetted leaves; S5, calculating the wetting time of the leaf according to the number of images of the wetted leaves and the photographing time interval.

Description

technical field [0001] The invention relates to the technical field of plant disease monitoring, in particular to a method and system for monitoring leaf wet time. Background technique [0002] Leaf wetting is one of the leading factors for the infection and prevalence of many plant leaf diseases, which makes leaf wetting time one of the key environmental input factors in the early warning of greenhouse vegetable diseases. In recent years, people have made some progress in the study of leaf wet time. Currently, there are two main methods for monitoring leaf wetness time: sensor measurement and model prediction. Among them, the electronic blade sensor has many practical applications. The resistance-based leaf sensor converts the leaf wetness into a voltage or current value, thereby determining a wet and dry threshold to count the leaf wet time. However, this type of sensor cannot fully simulate the characteristics of plant tissue, and cannot accurately reflect the wetness ...

AI Technical Summary

Problems solved by technology

The mechanism model is complex and requires many parameters to be input, and some parameters are difficult to obtain under the existing conditions

Although the empirical type requires fewer input parameters, it is limited by regional and human subjective factors

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