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Assessment of sowing material is a significant concern in seed science. A promising tool for assessing seed material is Corona Discharge Photography or Gas Discharge Visualization (GDV). In this study, this tool was applied to determine relationships between sowing material characteristics and GDV parameters; an Adaptive Neuro-Fuzzy Inference System (ANFIS) was utilized to interpret the experimental data. By using ANFIS, a three input fuzzy inference system was constructed to define the contiguous relations between GDV parameters (i.e., glow area and shape factor) and root length.

A serious problem of vegetable production is the quality of sown seeds. In this regard, assessment of seed quality before sowing and storage is of great practical interest. The modern level of scientific research requires the use of instrumental automated methods of seed quality evaluation, allowing to obtain more information and in a shorter time. The material for the study was a variety of samples from the collection of Brassica oleracea L., var. capitata, Raphanus sativus L., var. radicula, and Lepidium sativum L. seeds from the Federal Scientific Center of Vegetable Breeding and the Timofeev Selection Station. Digital X-ray images of seeds were obtained using a mobile X-ray diagnostic device PRDU-02. Automatic analysis of digital X-ray images was performed in the software “VideoTesT-Morphology 5.2.” The following latent defects of cabbage seeds of economic importance were revealed and identified: irregular darkening, significant “patterning” with deep separation of embryo parts, “angularity of seeds” leading to the loss of their viability. Automatic analysis of digital X-ray images of seeds confirmed the informativeness of brightness indices of digital X-ray images, as well as shape indices. Their connection with sowing qualities of the studied seeds was established.

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