Abstract
The overall goal of this research was to study the application of a machine vision system for classification of vegetable seeds. Specific objectives were to (1) design an automated classification system by using machine vision, (2) test the operation of the automated machine vision system with an algorithm for classification of seeds using color feature, and (3) to determine the germination rate in percentage. The seeds of three vegetables were classified by color features. These were Tomato (Lycopersicon esculentum Mill.), Cabbage (Brassica oleracea), and Broccoli (Lactuca sativa). An automated machine vision system using Factorial Vision FAV-500 (Yamatake Honeywell S/C) for classification of seeds was developed. On-line testing, including image acquisition and processing times, and classification of color feature required about 0.08 and 0.10s. The vegetable seeds were classified in different fractions by color features representing different germination rates.
