2001 Fiscal Year Final Research Report Summary
Development of PF Systems Using Field Environmental Sensors based on Multi-spectrum Machine Vision
| Project/Area Number |
10460111
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| Research Category |
Grant-in-Aid for Scientific Research (B)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
農業機械学
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| Research Institution | HOKKAIDO UNIVERSITY |
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Principal Investigator |
TERAO Hideo Hokkaido Univ., Grad. School of Agr. Prof., 大学院・農学研究科, 教授 (50001467)
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| Co-Investigator(Kenkyū-buntansha) |
ISHII Kazunobu Hokkaido Univ., Grad. School of Agr., Inst., 大学院・農学研究科, 助手 (70301009)
NOGUCHI Noboru Hokkaido Univ., Grad. School of Agr., Asso. Prof., 大学院・農学研究科, 助教授 (40228309)
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| Project Period (FY) |
1998 – 2001
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| Keywords | Precision Farming / Multi-spectrum Machine Vision / Crop Nutrition Stress / GIS / Spectroscopic Performance / Image Analysis / Industrial Unmanned Helicopter |
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| Research Abstract |
The objective of the research was to develop a machine vision, which could detect crop status. Crop status including crop health and growth was one of the most important information for promoting precision agriculture. The vision system was able to estimate both chlorophyll content and crop growth parameter composed of crop height and leaf area by calculating Leaf Color Index (LCI) and Vegetation Cover Ratio (VCR) from the image. Finally, the developed vision system was installed on an unmanned helicopter. The helicopter-base sensing system was able to effectively gather field information compared to a general ground-base system.
<Development of a real-time vision sensor>
In order to sense the crop status and efficiently conduct field operation through the timely information, the real-time vision sensor was developed. The R-G-NIR machine vision (MS2100, Duncan Tec.) was adopted to the vision sensor hardware. In this research, an estimator of the chlorophyll content and crop growth used L
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CI and VCR as input parameter. The VCR defined as ratio of leaf area to whole image area had significantly high correlation with crop height. In addition, the relationship between vision information and yield was also investigated. Actual Growth Index (AGI) which was product of LCI with VCR was defined to express the absolute volume of chlorophyll content. High correlation between AGI and yield, which was R^2 of 0.684, was obtained in the filed experiment. Therefore, it was concluded that the vision system could estimate yield before harvest.
<Sensing platform based on an unmanned helicopter>
This study developed a system that can generate a GIS map regarding crop status obtained by the vision sensor (MS2100) mounted on an unmanned helicopter. As for the unmanned helicopter used in this research, an RTK-GPS was adopted as a positioning sensor, and an inertial sensor that provides posture (roll and pitch angles) was installed in the helicopter. When obtaining the image by the vision sensor on the helicopter, sonic distortions caused by change of helicopter's posture arise in the image. In order to remove this distortion, geometric correction by converting from image coordinate to global coordinate was badly needed. By applying geometric correction to the image, it was possible to generate a map including maximum error of 29 cm. This accuracy was high enough to utilize this helicopter-base sensing system to Precision Farming. Less
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