The Trap of Presumption: A Case Study of Agricultural Research for Early Detection of Brown Planthopper Damage in Rice Fields

• 발행기관: 한국육종학회
• 저자명: 장규진(Gyujin Jang) 최낙중(Nak Jung Choi) 문중상(Jungxiang Wen) 김학진(Hak-Jin Kim) 정용석(Yong-Suk Chung)
• 간행물 정보: 『한국육종학회지』Vol.56 No.4, 439~448쪽, 전체 10쪽
• 주제분류: 농수해양 > 기타농수해양
• 파일형태: PDF
• 발행일자: 2024.12.01

국문 초록

The lack of understanding of topics in uncharted research areas can often be mitigated through a careful review of the existing literature. However, when a field is not well-studied, relying on assumptions before starting a project should be avoided. This article highlights the dangers of such presumptions as demonstrated by the case of brown planthopper (Nilaparvata lugens) detection in rice field. Although unpiloted aerial vehicles (UAVs) have shown promise in various agricultural applications, their effectiveness in the early detection of brown planthopper damage was initially assumed based on the expectation of visible symptoms. The image analysis in the current study indicated that images obtained from a camera mounted on a UAV could not detect the symptoms of the very early stages of damage from brown planthoppers. An overlooked factor was whether the pest damage was uniformly distributed across an entire rice plant. If symptoms appear consistently, early detection using a top-down view from a UAV is possible; otherwise, detection may be delayed. Our findings emphasize the need for thorough preliminary research to avoid failure. By investigating the biological characteristics of the target pest and the potential limitations of detection methods, researchers can greatly improve their chances of success. We hope that readers will recognize the importance of thoroughly examining unexplored areas before embarking on new research.

목차

Introduction

Material and Method

Result

Discussion

Acknowledgments

References

키워드

pest early detection UAV image analysis field management

참고문헌

• Int J Remote Sens
• Spat Inf Res
• Forests
• Remote Sens
• Sensors
• Nat Sci
• J Appl Remote Sens
• Pest Manag Sci
• Remote Sens
• Front Plant Sci
• Drones
• Remote Sens
• Agri Engineering
• Pest Manag Sci
• Comput Electron Agric
• Commun Math Biol Neurosci
• Sensors
• Trans Chin Soc Agric Mach