Development of Paddy Grown Soybean Cultivation Area Classification Technology Using DeepLabv3+

Chiyong An; Tae-An Kang; Chulmin Song; Jinki Park

doi:10.5389/KSAE.2024.66.6.059

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2024 Vol.66, Issue 6 Preview Page Next Page

Development of Paddy Grown Soybean Cultivation Area Classification Technology Using DeepLabv3+ DeepLabv3+를 활용한 논콩 재배면적 분류 기술 개발

2024. pp. 59-70

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Abstract

This study aims to develop a deep learning-based crop classification to effectively assess the cultivation area expansion of paddy-grown soybeans in South Korea. As the cultivation area of paddy fields decreases and the cultivation of soybeans increases, accurately identifying the cultivation area of strategic crops has become crucial. To achieve this, we utilized high-resolution UAV imagery and the DeepLabv3+ deep learning algorithm. DeepLabv3+ can extract information from a wide receptive field, enabling high-accuracy classification and segmentation even in complex agricultural images. The results of this study indicate that the model can predict the cultivation area of soybeans with an accuracy of 94.86%, with a precision of 93.81% and a recall of 93.71%. This research is expected to provide valuable information for agricultural policy and crop management.

Keywords

Paddy field

soybean

rice

cultivation field extraction

DeepLabv3+

Unmanned Aerial Vehide

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Information

Publisher :The Korean Society of Agricultural Engineers
Publisher(Ko) :한국농공학회
Journal Title :Journal of Korean Society of Agricultural Engineers
Journal Title(Ko) :한국농공학회논문집
Volume : 66
No :6
Pages :59-70
Received Date : 2024-09-13
Revised Date : 2024-11-11
Accepted Date : 2024-11-12
DOI :https://doi.org/10.5389/KSAE.2024.66.6.059

Journal of Korean Society of Agricultural Engineers ISSN:1738-3692(Print) 2093-7709(Online) 한국농공학회논문집

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