Remote-sensing estimation method for chlorophyll of apple leaves

Abstract

The invention discloses a remote-sensing estimation method for chlorophyll of apple leaves. The remote-sensing estimation method comprises synchronously acquiring hyperspectral reflectivity and corresponding chlorophyll relative content of the apple leaves by virtue of an SVC HR-1024i type hyper-spectrometer and an SPAD-502 chlorophyll meter, carrying out relevant analysis on original spectral reflectivity and a first-order derivative derivative spectrum so as to extract spectrographic red edge parameters of the apple leaves, and optimizing an artificial neural network by virtue of a traditional univariate regression algorithm, a BP neural network and a radial basis function network, and establishing a chlorophyll content inversion model. Compared with a traditional univariate model, the regression precision of an artificial network model is obviously increased, and the radial basis function network is high in study speed and precision and has a relatively reliable fitting result; and the artificial network model is the chlorophyll content inversion model which is worthy of being popularized.

Description

technical field [0001] The invention belongs to the technical field of agriculture and relates to a remote sensing method for estimating apple leaf chlorophyll. Background technique [0002] Vegetation chlorophyll content has a good correlation with photosynthetic capacity, growth stage and nitrogen level, and has become an effective means of evaluating vegetation growth. Since the spectral reflectance of green plants is affected by chlorophyll content in the visible light band, and mainly dominated by leaf structure and cellulose in the near-infrared band, the reflectance spectrum of plants can be used to estimate the pigment content. In recent years, hyperspectral remote sensing has become a major development trend in monitoring vegetation chlorophyll content due to its advantages of high spectral resolution, simplicity, effectiveness, and non-destructiveness. [0003] The red edge is the strong reflection formed by vegetation’s strong absorption of chlorophyll in the red...

AI Technical Summary

Problems solved by technology

Affected by conditions such as field management, fertilization and climate, scholars have realized that traditional statistical analysis methods are not very accurate in estimating chlorophyll content, and it is necessary to build an estimation model with both accuracy and stability; at the same time, the estimation of plant chlorophyll based on red edge parameters Relevant research has been carried out on some crops such as corn and wheat, but relatively few applications on fruit trees

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