Method and device for rapidly detecting nutritional information of tea tree based on hyperspectral imaging technique

Abstract

The invention relates to a method and a device for rapidly detecting main nutritional information during the growth of a tea tree based on a hyperspectral imaging technique. The method comprises the following steps of: firstly, actually measuring three main nutritional element content, such as N, P and K, of leaf samples and forming a database by using the measuring result as a reference measuring result; secondly, acquiring hyperspectral image data of the leaf samples of the tea tree in different visible light and near-infrared wave bands and transferring the data to a computer by using an image acquisition card; thirdly, preprocessing the data to complete the corresponding characteristic extraction and associating the characteristic variables with the measured N, P and K content in the established database to build a predictive model of the N, P and K content of the leaf; and finally, performing the corresponding data acquisition and characteristic extraction on the samples to be measured and predicting the N, P and K content of the leaf by using the built model. The method and the device have the advantages of high detection speed, simple and convenient operation, more comprehensive information and improvement on the accuracy and the stability of the detection result.

Description

technical field [0001] The invention relates to a rapid detection method and device for main nutritional information in tea tree growth based on hyperspectral imaging technology. technical background [0002] Tea tree is a perennial plant. The growth of tea tree requires a lot of nutrients. Tea tree nutrients are an important part of its synthesis of various organic compounds. They participate in various metabolic processes in the growth and development of tea trees and have important physiological functions. The deficiency of nutrient elements of tea tree directly affects the growth and development of tea tree, and also has an adverse effect on the quality and yield of tea. Tea tree is a plant for leaves, fresh leaves must be picked many times every year, and tea tree will consume a lot of nutrients after each picking, such as carbon, hydrogen, oxygen, nitrogen, phosphorus, potassium, calcium, magnesium, manganese, boron, zinc, etc. Among them, except carbon, hydrogen, and...

AI Technical Summary

Problems solved by technology

The lack of plant nutrient elements is not only directly related to the external characteristics of leaves such as texture, color and shape, but also closely related to the internal tissue structure of leaves. Computer image processing can well characterize the external characteristics of leaves, and near-infrared spectroscopy can be very good. To better reflect the internal tissue structure of leaves, computer image processing and near-infrared spectroscopy are considered to be the two most effective methods for the rapid detection of crop nutritional information. However, when it comes to the rapid detection of crop nutritional information, they all have Certain limitations: Spectral technology can detect the internal feature information of crop leaves well, but cannot express the external features such as color, texture and shape of crop leaves; although computer image processing can express the external features of crop leaves, it does not understand the local features. The bands are refined and enhanced, so the refinement of crop spectral information is not obvious; currently, most of them are completed based on a single technical means, which is not enough to accurately and comprehensively reflect the nutritional status of crops

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