Soil Moisture Prediction Based on Hyperspectral Image using CNN(Convolution Neural Network) (합성곱신경망을 이용한 초분광영상기반 토양수분 예측)

Nam-Youl Jeon (전남열); Lee Bong Kyu (이봉규)

doi:10.29056/jsav.2021.12.08

Soil Moisture Prediction Based on Hyperspectral Image using CNN(Convolution Neural Network)

Journal of Software Assessment and Valuation
Abbr : JSAV
2021, 17(2), pp.75-81
DOI : 10.29056/jsav.2021.12.08
Publisher : Korea Software Assessment and Valuation Society
Research Area : Engineering > Computer Science
Received : August 9, 2021
Accepted : December 20, 2021
Published : December 31, 2021

Nam-Youl Jeon ¹, Lee Bong Kyu ¹

¹제주대학교

Accredited

ABSTRACT

Since plant growth is greatly influenced by moisture, it is important to control the soil to have optimal moisture for the plant being grown. Recently, researches on automatically analyzing plant growth information including soil moisture using spectral images are being conducted. However, hyperspectral images are difficult to use due to huge amount of data appearing in spectral bands. In this paper, we propose a method to solve the complexity of hyperspectral images using a CNN. Since the proposed method automatically analyzes the entire band of the target hyperspectral using deep learning, there is no need to make an effort to find a specific band for analysis of each image. In order to show the effectiveness of the proposed system, we conduct an experiment to analyze moistures using hyperspectral images obtained from soil.

KEYWORDS

Soil moisture, Plant growth, Hyperspectral image, Spectral band, CNN

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* References for papers published after 2023 are currently being built.

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Journal of Software Assessment and Valuation 2023 KCI Impact Factor : 0.35

Soil Moisture Prediction Based on Hyperspectral Image using CNN(Convolution Neural Network)

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* References for papers published after 2023 are currently being built.

Journal of Software Assessment and Valuation 2023 KCI Impact Factor : 0.35

Soil Moisture Prediction Based on Hyperspectral Image using CNN(Convolution Neural Network)

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KEYWORDS

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* References for papers published after 2023 are currently being built.