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Identification of idealized leaf types using simple dimensionless shape factors by imaged analysis


, : Identification of idealized leaf types using simple dimensionless shape factors by imaged analysis. Transactions of the ASAE 39(4): 1525-1533

Identification of plants is important to develop robotics for pest, disease, and weed control with machine vision. Leaf shape is a common source of information used to identify plants. Intelligent vision systems are the next generation in machine vision. The addition of intelligence into vision systems requires an understanding and structuring of human visual techniques. Image acquisition and processing software was developed and evaluated to identify idealized leaf types using simple dimensionless shape factors. Compactness, roundness, elongation, lobation, and roughness were defined and used as the shape factors. The shape factors require neither a heavier computer load nor a larger amount of computer memory. They are not only size and rotational independent, but also suitable for imitating human visual memories. The Makino's 50 figures which illustrated the terminologies applied to the lamina shapes of leaves by a diagram were selected as idealized leaf types. It was theoretically possible to identify 1,280 leaf types using the shape factors by image analysis with sufficient resolution. Results indicate the simple dimensionless shape factors will be useful to identify plants by their leaf shapes, and to compose knowledge bases of the intelligent vision systems.

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