Machine identification of buds in images of plant shoots

Abstract

Tissue cultures find increasingly widespread applications for cloning of many plants. Commercial propagation by tissue cultures is limited to ornamental plants, because the cost of skilled labor required cannot compete with conventional propagation methods. To cut down the cost, some automation is essential. A cost-effective approach is to chop the plantlets into segments on a conveying production line while using machine vision for identifying and locating the number and positions of propagation organs in images of the plantlet segments. Plantlet segments without propagation organs will be rejected, while segments with viable buds will be selected for subculturing. To this end, a machine-vision-controlled automatic subculturing system for potato tissue cultures is proposed as a simpler and more cost-effective solution than the popular trend of imitating the manual sub-culturing task by a robot. A simple and relatively fast image-processing algorithm particularly suitable for classification of potato tissue cultures, was developed. In lieu of the general Medial Axis Transform approach, this specialized algorithm takes advantage of the inherent difference between the geometrical shape and gray scale levels of the stems and the leaves as well as of the rather simple connectivity rules of attachment between them. The results indicate that machine inspection and classification of tissue culture plantlets is possible, but considerably more work needs to be done before this technique is fully developed for automating tissue culture processes.