Method for losslessly evaluating ozone sensitivity of plant through leaves

Abstract

The invention relates to a method for losslessly evaluating ozone sensitivity of a plant through leaves, belongs to the field of atmospheric pollution biological monitoring, and the method can be applied to lossless and continuous determination of injury symptoms of ozone sensitive plants such as soybeans, poplar trees, silver birches, tilia amurensis and quercus liaotungensis. The method comprises the following steps: classifying damage types according to a leaf surface image, then segmenting the leaf surface image, extracting and analyzing characteristics such as symptom shape and area size, establishing an extraction method based on an ISODATA algorithm in a Lab color mode, obtaining a spot rate, and then performing damage degree grading (figure 1). The method has the advantages that compared with an existing naked eye observation or destructive sampling method, the method can analyze the damaged area and proportion of the leaf surface without damage, continuous monitoring and accurate grading are achieved, the problem that the damage degree cannot be quantitatively compared due to the fact that ozone damage symptoms of different plants are different can be solved, the plant ozone damage can be simply and directly judged, and the method has the advantages of rapidness, no damage, continuous observation and the like.

Description

technical field [0001] The invention relates to a method for nondestructive evaluation of plant ozone sensitivity by leaves, belongs to the field of air pollution biological monitoring, and is especially suitable for nondestructive, continuous monitoring and quantitative evaluation of plant ozone damage under natural conditions. Background technique [0002] High concentrations of atmospheric ozone can cause oxidative damage to plants, cause changes in plant metabolism to varying degrees, cause changes in physiological and biochemical processes such as plant pigment content, tissue structure, and cell ultrastructure, and cause plants to show external damage symptoms. These external symptoms are reflected in the image, showing differences in color, shape, etc., which provides a basis for evaluating the ozone sensitivity of plants using image processing technology. Ozone damage to plants is undetectable in any residual elements, but can cause symptoms such as spots, patches, a...

AI Technical Summary

Problems solved by technology

This type of method is highly subjective and cannot be used for rapid and non-destructive quantitative detection. After the leaves are removed, it is impossible to continuously monitor the development of the damage symptoms of the same leaf. It will also have a certain impact on the growth of

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