Comparison of Alternative Modes of Data Input to the Pesticide Information Retrieval System.

Abstract

The pilot Pesticide Information Retrieval System (PEST) -- a Component of the Environmental Technical Information System -- was initially developed to perform keyword searches over a database created from each installation's Pest Control Reports. The system can summarize this data by pesticide used, pest, date, installation, location, and several other parameters to produce summary reports containing information on the amount of active ingredient used. Before PEST can be used in the field, an efficient method must be developed for entering all pesticide application data collected daily at each installation into the system database. This report explores three feasible methods of entering data into this system on a production basis and provides time/cost estimates for these methods. The three methods evaluated were interactive data input, optical mark reading from the installations, and optical mark reading from a central location. It was found that all three methods would reduce the number of steps in the current recordkeeping procedure and that all would reduce the level of error. The scanning at a central location method is the most efficient method, but also the most expensive.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1982
Accession Number
ADA122045

Entities

People

  • Manette Messenger
  • R. Webster
  • T. Brown

Organizations

  • Construction Engineering Research Laboratory

Tags

Communities of Interest

  • Biomedical
  • Human Systems

DTIC Thesaurus Topics

  • Acquisition
  • Army Training
  • Computers
  • Construction
  • Control Systems
  • Cost Effectiveness
  • Cost Estimates
  • Costs
  • Databases
  • Department Of Defense
  • Engineering
  • Information Retrieval
  • Information Systems
  • Information Transfer
  • Pest Control
  • Pesticides
  • Technology Transfer

Fields of Study

  • Agricultural and Food sciences

Readers

  • Computer Science/Computer Engineering/Data Science/Digital Signal Processing.
  • Systems Analysis and Design
  • Vector-Borne Disease and Entomology

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Information Retrieval