Seed-Lab : a database management system for seed laboratories

Seed-Lab, is a software system for managing seed laboratory information. It was developed at the Oregon State University Seed Testing Laboratory. Seed-Lab was built around a database development package Revelation, which provides its own programming language. Seed-Lab shields the user from the complexities of database management by supporting menu driven inquiries. Specifically, Seed-Lab allows inquiries about customers, accounts, invoices, plant species, types of tests, seed samples, and test results. Furthermore, Seed-Lab enforces several domestic and international regulations governing the computing and reporting of seed testing results. Seed-Lab runs on a local area network of microcomputers and is portable to any other network capable of running the Novell Netware operating system

Application of the Dichromatic Reflection Model to Wood

The applicability of the dichromatic reflection model to describe wood-light interactions in Douglas-fir veneer was investigated. Spectral reflectance measurements taken with illumination along and across the fibers were analyzed by the methodology proposed by Lee et al. (1990). Differences between observed and predicted spectral reflectances were small overall, and increased towards the blue end of the spectrum. Transmission through cell walls, interreflection between cell walls, and an optically active interface are possible explanations for these differences. Average reflectances were higher when samples were illuminated across the directions of the fibers. Rotary-peeled veneer, however, presents surface irregularities where the wood fibers have been pulled away from the surface of the material and where the along-fiber brightness is higher than its corresponding across-fiber measurement

Classification of Wood Surface Features by Spectral Reflectance

A database of spectral-reflectance curves of Douglas-fir veneer surface features is presented and analyzed via principal-component analysis. The paper describes how such analysis can be used to model and classify the spectral-reflectance curves by feature type. For modeling (curve-reconstruction) purposes, three principal components were sufficient by most criteria. For classification purposes, seven principal components achieved classification accuracies (with quadratic discriminant analysis) on the order of 99%, comparable to the accuracies achieved with the raw spectral data. The best seven principal components were not those associated with the largest variation in the data. This paper suggests how comparable classification accuracies might be achieved in a system operating at production speeds in a mill