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Academic Journal of Environmental Biology, 2022, 3(1); doi: 10.38007/AJEB.2022.030106.

Wheat Biomass Estimation Based on Neural Network and Hyperspectral Vegetation Index


Logensh Sainni

Corresponding Author:
Logensh Sainni

Saadah University, Yemen


Wheat biomass is an important indicator reflecting the growth status of wheat. Wheat biomass management is one of the most important links in wheat breeding and plant production management in agro-ecosystems, and it is also one of the key factors affecting wheat growth. Wheat production and income. The purpose of this work is to study wheat biomass estimation based on neural network and hyperspectral vegetation index, and to study and establish a biomass prediction model based on hyperspectral vegetation index. Corresponding characteristic parameters (spectral reflectance, spectral index, red edge width, characteristic wavelength) were extracted by analyzing the canopy spectrum, the correlation between characteristic parameters and biomass was analyzed, and a biomass prediction model based on neural network was established. The comparison results of vegetation index and traditional multiple regression model show that the total sample prediction R2 of the multiple linear regression model is 0.869, which is smaller than the total sample prediction R2 of the BP neural network. The biomass estimation model is higher than the wheat biomass estimation model of BP neural network.


Neural Networks, Hyperspectral Vegetation Index, Wheat Biomass, Remote Sensing Monitoring

Cite This Paper

Logensh Sainni. Wheat Biomass Estimation Based on Neural Network and Hyperspectral Vegetation Index. Academic Journal of Environmental Biology (2022), Vol. 3, Issue 1: 56-64. https://doi.org/10.38007/AJEB.2022.030106.


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