Research on the disease status mapping system for reducing environmental load

Research Project

Project/Area Number	16580211
Research Category	Grant-in-Aid for Scientific Research (C)
Allocation Type	Single-year Grants
Section	一般
Research Field	Agricultural environmental engineering
Research Institution	National Agriculture and Food Research Organization
Principal Investigator	MATSUO Kentaro National Agricultural Research Center for Tohoku Region, Vegetable and Floricultural Research Team, Researcher, 東北農業研究センター・寒冷地野菜花き研究チーム, 研究員 (80355346)
Co-Investigator(Kenkyū-buntansha)	ZHANG Shu-huai Hirosaki University, Assistant Professor, 農学生命科学部, 助教授 (90261429) NISIWAKI Kentaro National Agriculture and Food Research Organization, Researcher, 東北農業研究センター・雑草バイオタイプ・総合防除研究チーム, 研究員 (40355269)
Project Period (FY)	2004 – 2006
Project Status	Completed (Fiscal Year 2006)
Budget Amount *help	¥3,700,000 (Direct Cost: ¥3,700,000) Fiscal Year 2006: ¥700,000 (Direct Cost: ¥700,000) Fiscal Year 2005: ¥1,000,000 (Direct Cost: ¥1,000,000) Fiscal Year 2004: ¥2,000,000 (Direct Cost: ¥2,000,000)
Keywords	spectral camera / spectroscopic image / lettuce / near-infrared / disease damage / GPS / 分光カメラ / 腐敗病 / 斑点病 / 分光 / うどんこ病 / スペクトル分光器 / 病害発生診断
Research Abstract	The target of our research is to detect disease damage of vegetable at the early stage and to map this information. 1) To develop spectroscopic imaging system A spectral camera, a linear slider, sixteen Halogen lamps, a personal computer, and a signal conversion processor were used in experimental setup. This records spectroscopic image of the object by the automatic operation. Also, this makes composite image of set wavelength from spectroscopic image and saves all of reflection intensity at specified position on the image. 2) To study how to detect disease damage of vegetable at the early stage One kind of lettuce (Dreed variety : SAKUSESU (TSURUTA SEED CO.))was used in this experiment. We inoculated lettuces with bacterial soft rot. We captured lettuces' spectral image with experimental setup and digital camera on 3 or 4 days. We tried to detect bacterial soft rot by analyzing captured images. The near-infrared reflection intensity of the dead part is lower than that of the healthy part. We was able to create a disease picture by using this information, before we was able to detect it. But whether the reason of the death of the leaf was the bacterial soft rot or increasing age was not clear. This system was not able to capture near-ultraviolet region, so we should improve the lighting and conduct the experiment again. 3) To develop mapping system This system makes identification ID that GPS puts out a name and saves the image. After finishing saved images, this system builds a map using identification ID and position information.