Computer Vision Approach for Phenotypic Characterization of Horticultural Crops

Seungri Yoon; Minju Shin; Jin Hyun Kim; Ho Jeong Jeong; Junyoung Park; Tae In Ahn

doi:10.12791/KSBEC.2024.33.1.063

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2024 Vol.33, Issue 1 Preview Page Next Page

Original Articles

Computer Vision Approach for Phenotypic Characterization of Horticultural Crops 컴퓨터 비전을 활용한 토마토, 파프리카, 멜론 및 오이 작물의 표현형 특성화

31 January 2024. pp. 63-70

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Abstract

This study explored computer vision methods using the OpenCV open-source library to characterize the phenotypes of various horticultural crops. In the case of tomatoes, image color was examined to assess ripeness, while support vector machine (SVM) and histogram of oriented gradients (HOG) methods effectively identified ripe tomatoes. For sweet pepper, we visualized the color distribution and used the Gaussian mixture model for clustering to analyze its post-harvest color characteristics. For the quality assessment of netted melons, the LAB (lightness, a, b) color space, binary images, and depth mapping were used to measure the net patterns of the melon. In addition, a combination of depth and color data proved successful in identifying flowers of different sizes and distances in cucumber greenhouses. This study highlights the effectiveness of these computer vision strategies in monitoring the growth and development, ripening, and quality assessment of fruits and vegetables. For broader applications in agriculture, future researchers and developers should enhance these techniques with plant physiological indicators to promote their adoption in both research and practical agricultural settings.

Keywords

Computer vision

K-means clustering

SVM model

Gaussian mixture model

Tomato maturity

Fruit coloring

본 연구는 오픈소스 라이브러리인 OpenCV를 활용해 다양한 시설과채류의 표현형 분석에 적용 가능한 컴퓨터 비전 기술을 탐구하였다. 토마토에 대해서는 이미지의 색상을 분석하여 숙성도를 판정하며, support vector machine(SVM) and histogram of oriented gradients 기법을 통해 숙성된 토마토를 효과적으로 검출하였다. 파프리카의 경우, 색상 분포를 시각화한 후, 가우스 혼합 모델로 클러스터링을 실행하여 수확 파프리카의 색상 특성을 분석하였다. 네트 멜론의 품질 평가에서는 LAB 색상 공간, 이진화 이미지 및 깊이 매핑을 활용하여 멜론의 네트 패턴을 정량화하였다. 추가로, 오이 온실에서 화방 검출을 위해 깊이 정보와 색상 정보를 조합하여 다양한 크기와 거리의 화방을 성공적으로 검출하였다. 이 연구의 결과로, 해당 컴퓨터 비전 기술들이 시설과채류의 생장 모니터링, 숙성 및 품질 평가 등에서의 유효성을 확인하였다. 농산업에서 컴퓨터 비전의 효과적 적용을 위해, 후속 연구자나 개발자들이 재배 생리와 연관된 지표를 기반으로 이 기술들을 보완할 경우, 실제 농업 현장 및 연구에서 널리 활용될 가능성이 크다.

키워드

컴퓨터비전

K-평균 클러스터링

SVM 모델

가우스 혼합 모델

토마토 숙기

과실 착색

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Information

Publisher :The Korean Society for Bio-Environment Control
Publisher(Ko) :(사)한국생물환경조절학회
Journal Title :Journal of Bio-Environment Control
Journal Title(Ko) :생물환경조절학회지
Volume : 33
No :1
Pages :63-70
Received Date : 2023-10-05
Revised Date : 2024-01-29
Accepted Date : 2024-01-29
DOI :https://doi.org/10.12791/KSBEC.2024.33.1.063

Journal of Bio-Environment Control ISSN:1229-4675(Print) 2765-3641(Online) 생물환경조절학회지

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