Leaf wet time monitoring method and system

Abstract

The invention discloses a method and system for monitoring the wetting time of leaves, which can calculate the wetting time of leaves more accurately, and does not need to adjust equipment according to the growth and development of plants in the whole process, and the calculation process is relatively simple. The method includes: S1, collecting fluorescence images of leaves; S2, clustering and segmenting the fluorescence images using K-means clustering, and binarizing the results of the clustering and segmentation to obtain binarized images; S3, The binarized image is corrected by means of open and close alternate filtering; S4, the preset water droplet shape feature and area size are used as the criterion for judging whether the leaf is wet or not, and the support vector machine based on statistical learning theory is used to distinguish the leaf A classifier for whether the fluorescence image is wet or not, identifies the corrected image, and obtains the number of images of leaves being wet; S5, calculates the wet time of the leaves according to the number of images of the leaves being wet and the time interval of taking pictures.

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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