| 研究課題/領域番号 |
22KF0179
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| 補助金の研究課題番号 |
21F21397 (2021-2022)
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| 研究種目 |
特別研究員奨励費
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| 配分区分 | 基金 (2023)
補助金 (2021-2022) |
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| 応募区分 | 外国 |
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| 審査区分 |
小区分41040:農業環境工学および農業情報工学関連
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| 研究機関 | 京都大学 |
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研究代表者 |
近藤 直 (2021, 2023) 京都大学, 農学研究科, 教授 (20183353)
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| 研究分担者 |
HUANG ZICHEN 京都大学, 農学研究科, 外国人特別研究員
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| 受入研究者 |
近藤 直 (2022) 京都大学, 農学研究科, 教授 (20183353)
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| 外国人特別研究員 |
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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| 研究期間 (年度) |
2023-03-08 – 2024-03-31
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| 研究課題ステータス |
完了 (2023年度)
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| 配分額 *注記 |
2,200千円 (直接経費: 2,200千円)
2023年度: 600千円 (直接経費: 600千円)
2022年度: 1,100千円 (直接経費: 1,100千円)
2021年度: 500千円 (直接経費: 500千円)
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| キーワード | image / fluorescence / grading / green fruit / machine vision / grader / green fruits / fluorescence imaging / label-free / disease |
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| 研究開始時の研究の概要 |
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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| 研究実績の概要 |
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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