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Original Articles

Agricultural Applicability of AI based Image Generation

AI 기반 이미지 생성 기술의 농업 적용 가능성

Seungri Yoon1
,
Yeyeong Lee1
,
Eunkyu Jung1
,
Tae In Ahn2*
윤 승리1
,
이 예영1
,
정 은규1
,
안 태인2*
1Graduate student, Department of Agriculture, Forestry and Bioresources (Horticultural Science and Biotechnology), Seoul National University, Seoul 08826, Korea
2Assistant Professor, Department of Agriculture, Forestry and Bioresources (Horticultural Science and Biotechnology), Seoul National University, Seoul 08826, Korea
1서울대학교 농림생물자원학부 대학원생
2서울대학교 농림생물자원학부 조교수
*Corresponding Author
30 April 2024. pp. 120-128
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Abstract

Since ChatGPT was released in 2022, the generative artificial intelligence (AI) industry has seen massive growth and is expected to bring significant innovations to cognitive tasks. AI-based image generation, in particular, is leading major changes in the digital world. This study investigates the technical foundations of Midjourney, Stable Diffusion, and Firefly—three notable AI image generation tools—and compares their effectiveness by examining the images they produce. The results show that these AI tools can generate realistic images of tomatoes, strawberries, paprikas, and cucumbers, typical crops grown in greenhouse. Especially, Firefly stood out for its ability to produce very realistic images of greenhouse-grown crops. However, all tools struggled to fully capture the environmental context of greenhouses where these crops grow. The process of refining prompts and using reference images has proven effective in accurately generating images of strawberry fruits and their cultivation systems. In the case of generating cucumber images, the AI tools produced images very close to real ones, with no significant differences found in their evaluation scores. This study demonstrates how AI-based image generation technology can be applied in agriculture, suggesting a bright future for its use in this field.

Keywords
AI-based image generation
Diffusion model
Generative AI
Image-to-image generation
Prompt engineering
Text-to-image generation

2022년 ChatGPT 출시 이후, 생성형 AI 산업은 엄청난 규모로 성장하였으며, 인지 작업에 혁신을 가져올 것으로 기대되고 있다. 특히 AI 기반 이미지 생성 기술은 현재 디지털 세계의 핵심적인 변화를 주도하고 있다. 본 연구는 대표적인 AI 이미지 생성 도구인 미드저니, 스테이블 디퓨전, 그리고 파이어플라이의 기술적 원리를 분석하고, 이미지 생성 결과를 비교함으로써 그 유용성을 평가하였다. 실험 결과, 이 AI 도구들은 대표 시설원예 작물인 토마토, 딸기, 파프리카, 오이의 과실 이미지를 실제와 유사하게 재현하였다. 특히 파이어플라이는 실제 온실 재배 작물 이미지를 매우 사실적으로 묘사하는 능력을 보여주었다. 그러나 모든 도구들은 작물이 자라는 온실의 환경적 맥락을 완전히 반영하는 데에 있어서 다소 한계를 보였다. 프롬프트 개선 및 레퍼런스 이미지를 활용하여 딸기 과실 이미지와 시설 딸기재배 시스템을 보다 정교하게 생성하는 과정도 포함되었으며, 이러한 접근은 AI 이미지 생성 기술의 세밀한 조정이 가능함을 보여준다. 오이 과실 이미지 생성 능력을 비교한 결과, AI 생성 도구들은 실제 이미지와 매우 유사한 이미지를 생성해 냄으로써 이미지 생성 점수(CLIP score)에 있어서 통계적 차이를 보이지 않았다. 본 연구는 AI 기반 이미지 생성 이미지 기술이 농업 분야에 활용될 수 있는 방안을 모색하며, 생성형 AI의 농업에 대한 적용을 긍정적으로 전망한다.

키워드
생성형 인공지능
이미지 기반 이미지 생성
인공지능 기반 이미지 생성
프롬프트 엔지니어링
텍스트 기반 이미지 생성
확산 모델
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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 :2
  • Pages :120-128
  • Received Date : 2024-04-16
  • Revised Date : 2024-04-26
  • Accepted Date : 2024-04-29
  • DOI :https://doi.org/10.12791/KSBEC.2024.33.2.120
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