Plant health state detection method based on thermal imaging technology

Abstract

The invention provides a plant health state detection method based on a thermal imaging technology. The plant health state detection method based on the thermal imaging technology provided by the embodiment of the invention comprises the following steps: firstly, reading a temperature data file comprising plants and reference leaves and carrying out a vegetation soil separation algorithm and a reference temperature algorithm to obtain a vegetation soil separated data file and a canopy temperature Tc; obtaining a reference temperature Tp according to temperature data of thermal infrared images of the reference leaves; and finally, calculating according to the canopy temperature Tc, the reference temperature Tp and an air temperature Ta to obtain a vegetation transpiration diffusion coefficient, so as to obtain a health condition grade of the plants. Compared with the prior art, a three-temperature model is used for calculating the vegetation transpiration diffusion coefficient in the embodiment and the plant growth vigor is further evaluated; the operation is simple and the efficiency is high.

Description

technical field [0001] The invention relates to a technology for evaluating plant growth, in particular to a method for detecting plant health status based on thermal imaging technology. Background technique [0002] In recent years, with the advancement of remote sensing technology, plant canopy temperature has been applied in the field of remote sensing. Existing studies have used vegetation surface temperature, air temperature and vegetation index to calculate crop water deficit, which is used to explore the problem of water deficit in areas with incomplete vegetation coverage. Later, canopy temperature difference was widely used to calculate evapotranspiration and photosynthesis, and was further applied to calculate crop water deficit. On the basis of these studies, surface temperature has become a reliable technique for obtaining bioenvironmental information and is widely used in CO 2 flux monitoring, pollution monitoring and agriculture. However, to use these method...

AI Technical Summary

Problems solved by technology

This method has high accuracy, but it is inefficient when processing complex or large-scale images, and it is time-consuming and laborious, and is not suitable for processing a large number of images; using software supervised classification refers to marking the representatives of soil and vegetation separately in the software Afterwards, through the supervised classification function of the software, the software judges the boundaries of soil and vegetation by itself, and obtains the classification results

This method is relatively simple and has a certain degree of accuracy, but its applicability is limited due to the need for professional software

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