| Project/Area Number |
22KF0179
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| Project/Area Number (Other) |
21F21397 (2021-2022)
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Multi-year Fund (2023)
Single-year Grants (2021-2022) |
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| Section | 外国 |
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| Review Section |
Basic Section 41040:Agricultural environmental engineering and agricultural information engineering-related
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| Research Institution | Kyoto University |
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Principal Investigator |
近藤 直 (2021, 2023) 京都大学, 農学研究科, 教授 (20183353)
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| Co-Investigator(Kenkyū-buntansha) |
HUANG ZICHEN 京都大学, 農学研究科, 外国人特別研究員
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| Host Researcher |
近藤 直 (2022) 京都大学, 農学研究科, 教授 (20183353)
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| Foreign Research Fellow |
HUANG ZICHEN 京都大学, (連合)農学研究科(研究院), 外国人特別研究員
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| Project Period (FY) |
2023-03-08 – 2024-03-31
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| Project Status |
Completed (Fiscal Year 2023)
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| Budget Amount *help |
¥2,200,000 (Direct Cost: ¥2,200,000)
Fiscal Year 2023: ¥600,000 (Direct Cost: ¥600,000)
Fiscal Year 2022: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2021: ¥500,000 (Direct Cost: ¥500,000)
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| Keywords | image / fluorescence / grading / green fruit / machine vision / grader / green fruits / fluorescence imaging / label-free / disease |
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| Outline of Research at the Start |
There are two topics in this research. The first topic is to build an acoustic-based hybrid localization system for a greenhouse robot. Experiments will be conducted to evaluate its localization performance, including accuracy and Doppler shift to moving objects. The second topic is the application of fluorescence imaging-based sensing methods to the precision agriculture of green peppers. Early-stage diseases will be studied and detected using fluorescence images. Also, a label-free method will be established to track individual peppers.
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| Outline of Annual Research Achievements |
We developed a fluorescence image acquisition system for analyzing the phenotype of green peppers. The fruits had some fluorescence substance whose excitation wavelength was 365 nm and emitted blue fluorescence, which can be captured by cameras within the visible light spectrum. Notably, the "Manganji sweet peppers" in Kyoto, Japan, represent a distinctive variety with significant demand for agricultural big data under their regional brand. In response to the need for individual green pepper traceability with big data, we identified fluorescence features in green pepper images that are imperceptible under natural light. Through mechanistic analysis, we determined that over 93% of these fluorescence features naturally arise during the growth process and remain stable. Leveraging feature point matching algorithms, we constructed a label-free tracking system that enabled the traceability of individual green peppers, achieving an 83% accuracy rate in line with commercial application standards. The integration of the indoor positioning system and fluorescence imaging system aligns with the overarching objective of this research, which is to elevate precision agriculture to a level where plant-to-plant accuracy is attainable. We have authored or co-authored 6 SCI papers for showing the new findings as well as presentations at 5 international academic conferences. These publications and conference contributions play a crucial role in amplifying the impact of our research endeavors.
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