SVRice: An Automated Rice Seed Vigor Classification System via Radicle Emergence Testing Using Image-Processing, Curve-Fitting, and Clustering Methods

Abstract

Highlights SVRice package: a simple and effective procedure for automatic rice seed vigor classification was verified. The package consists of three important parts: image analysis, curve-fitting, and cluster analysis. Findings provide an effective rice seed vigor classification system that promises to reduce the production costs. Abstract. Rice seed vigor classification is important for seed storage management by seed producers and by farmers while planning their cultivation activities. Field emergence is a direct method of seed vigor testing but is laborious, time-consuming and subjective. This article presents the SVRice package, a simple, cost-efficient, and flexible procedure based on computer image analysis for high-throughput, automatic rice seed vigor classification. SVRice consists of four steps: dynamic imaging, image processing, curve fitting, and clustering. Seed vigor was classified based on radicle emergence indices, such as maximum radicle emergence (MaxRE), mean radicle emergence time (MRET), radicle emergence speed (t50), uniformity of radicle emergence (U7525), and area under the curve of the radicle emergence fitted curve (AUC). Parameters used to classify rice seed vigor, such as MRET, U7525, and t50, were strongly negatively correlated with the saturated salt accelerated aging (SSAA) test. A germination time of 90 h at 25°C was sufficient for effective classification based on SVRice, whereas the SSAA test took approximately 400 h to complete. The SVRice software algorithm was created to be especially suitable for assessment after six months under controlled atmosphere storage (at 15°C and 37% RH in a hermetic bag). The study showed that SVRice could unambiguously classify 40 indica rice samples with different varieties, production years, production sites, storage times, and storage conditions compared with the SSAA test. Findings provide an effective seed vigor classification system that promises to reduce the production costs and encourages competitiveness of seed producers. Keywords: Image analysis, Oryza sativa, Radicle emergence, Seed vigor, SVRice.