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Alchanatis, V., Faculty of Agricultural Engineering, Technion-Israel Institute of Technology, Israel and Faculty of Agriculture, Technion City, Haifa 32000, Israel
Peleg, K., Faculty of Agricultural Engineering, Technion-Israel Institute of Technology, Israel and Faculty of Agriculture, Technion City, Haifa 32000, Israel
Ziv, M., Faculty of Agricultural Engineering, Technion-Israel Institute of Technology, Israel and Faculty of Agriculture, Technion City, Haifa 32000, Israel
Tissue culture techniques are finding increasingly widespread applications for cloning of many plants. Protocols for mass propagation of many species have been developed, but in spite of its advantages, large-scale commercial plant propagation by tissue cultures is largely limited to ornamental plants. This is due mainly to the intensive skilled labour required for subculturing the propagules and in transferring individual shoots or plantlets into and out of culture containers. In order to cut down the production costs, a certain degree of automation is essential. A cost effective approach for automation is proposed, whereby tissue culture plantlets are chopped into approximately uniformly sized segments, on a conveying production line while using colour computer vision for identifying and locating the number and positions of propagation organs, in images of the plantlet segments. Plantlet segments without propagation organs are rejected, while properly cut segments with viable buds or shoots are automatically selected for subculturing. In this paper, some initial results of this approach are reported, in which stationary images of manually pre-cut potato plantlet segments were analysed and classified. Using colour machine vision and a Neural Network-based classifier, a basis was laid for a practical system, which may be used for automatic classification of tissue culture segments of potato plantlets. Instead of the conventional use of black and white cameras and geometric features, colour features only are used together with colour frame manipulation capabilities, which are now available in most commercial imaging boards. This facilitates accurate, high-speed classification of plantlet images.
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Classification of Tissue Culture Segments by Colour Machine Vision
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Alchanatis, V., Faculty of Agricultural Engineering, Technion-Israel Institute of Technology, Israel and Faculty of Agriculture, Technion City, Haifa 32000, Israel
Peleg, K., Faculty of Agricultural Engineering, Technion-Israel Institute of Technology, Israel and Faculty of Agriculture, Technion City, Haifa 32000, Israel
Ziv, M., Faculty of Agricultural Engineering, Technion-Israel Institute of Technology, Israel and Faculty of Agriculture, Technion City, Haifa 32000, Israel
Classification of Tissue Culture Segments by Colour Machine Vision
Tissue culture techniques are finding increasingly widespread applications for cloning of many plants. Protocols for mass propagation of many species have been developed, but in spite of its advantages, large-scale commercial plant propagation by tissue cultures is largely limited to ornamental plants. This is due mainly to the intensive skilled labour required for subculturing the propagules and in transferring individual shoots or plantlets into and out of culture containers. In order to cut down the production costs, a certain degree of automation is essential. A cost effective approach for automation is proposed, whereby tissue culture plantlets are chopped into approximately uniformly sized segments, on a conveying production line while using colour computer vision for identifying and locating the number and positions of propagation organs, in images of the plantlet segments. Plantlet segments without propagation organs are rejected, while properly cut segments with viable buds or shoots are automatically selected for subculturing. In this paper, some initial results of this approach are reported, in which stationary images of manually pre-cut potato plantlet segments were analysed and classified. Using colour machine vision and a Neural Network-based classifier, a basis was laid for a practical system, which may be used for automatic classification of tissue culture segments of potato plantlets. Instead of the conventional use of black and white cameras and geometric features, colour features only are used together with colour frame manipulation capabilities, which are now available in most commercial imaging boards. This facilitates accurate, high-speed classification of plantlet images.
Scientific Publication
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