2001 Fiscal Year Final Research Report Summary
Fundamental research far an intelligent plant factory based on the computerized network.
| Project/Area Number |
11460121
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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 |
生物環境
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| Research Institution | Ehime University |
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Principal Investigator |
HASHIMOTO Yasushi Ehime Univ., Faculty of Agriculture, Professor, 農学部, 教授 (30036298)
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| Co-Investigator(Kenkyū-buntansha) |
NISHINA Hiroshige Ehime Univ., Faculty of Agriculture, Professor, 農学部, 教授 (70134509)
TAKEUCHI Toshinobu Biotechnology Research Dept., Shikoku Research Institute Inc., 研究員
MORIMOTO Tetsuo Ehime Univ., Faculty of Agriculture, Associate Professor, 農学部, 助教授 (50127916)
HATOU Kenji Ehime Univ., Faculty of Agriculture, Instructor, 農学部, 助手 (50274345)
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| Project Period (FY) |
1999 – 2001
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| Keywords | Plant factory / Computer network / Image transmission / Image compression / Image processing and recognition / Artificial intelligence / Neural network / Intelligent control |
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| Research Abstract |
In this study, an effective data communication technique for a decentralized plant production system consisting of many plant factories, which works on the network, was explored.
1. Image data are characterized by large size. An effective image compression technique for plant was developed to deal well with such large sized data on the network. It was applied for diagnosing the growth conditions of plants cultivated in different plant factories by transmitting their plant images. This technique was very useful for data communication among several plant factory systems. (Paper 1)
2. A stereo sensing technique was developed to construct 3-dimensional plant shape in real-time processing. A fruit image was first obtained by composing two images took from the right and left directions using two CCD cameras and then extracted its outline by displaying with linear lines. From these procedures, we obtained a 3-dimensional plant model. This technique significantly shortened the processing time fo
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r 3-D image construction. We could obtain a more effective 3-dimentional model, which looks like an image taken by a real human vision, by using the concept of a tele-existence method. (Papers 2 and 3)
3. An image recognition technique for a visual sensor of a harvesting robot was developed and applied it to the evaluation of the quality of seedlings which are moving on the belt conveyor. This technique provided a high-speed processing (5 images/s) for evaluation. This technique was useful to achieve automation of a plant evaluation system. (Paper 4)
4. Shape evaluation of fruit is quite empirical and uncertain. A new technique was developed to evaluate the fruit shape quantitatively using attractor, fractal dimension and neural networks. There was high correlations among identification errors, the shapes of attractors and fractal dimensions for evaluating fruit shape. So, these approaches allow the quantitative evaluation of the complexity of the fruit shape and are useful for the data communication among plant factories. (Paper 5)
5. Finally, total mechanization and automation for decentralized plant factories were investigated using newly developed image processing techniques mentioned above. (Paper 6) Less
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Research Products
(12 results)
