Quality assessment of lettuce using artificial neural network

Ira C. Valenzuela, John Carlo V. Puno, Argel A. Bandala, Renann G. Baldovino, Robert G. De Luna, Anton Louise De Ocampo, Joel L Cuello, Elmer P. Dadios

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

The critical features in yield forecasts determination are crop health and seasonal progress. These serve as an indicator for the success of farming. Visual inspection often produces a false assumption on the quality of the lettuce crop health. To address this problem, a proposed solution is the development of a machine vision system for the assessment of the quality of the lettuce crop. This system is composed of two parts: application of digital image processing for the feature extraction of the sample lettuce and implementation of the back propagation artificial neural network for the self-learning classification of the system. ANN is a tool designed like a human brain that can learn patterns and relationship based on the input data. Also, backpropagation has been used because it has the capability to adjust its weights and biases in increasing the efficiency of its learning. A total of 253 images were collected and 70% of these were used for training the network, 15% fro validation and 15% for testing. The developed system produced was able to classify the quality of the lettuce with minimum relative error of 0.051.

Original languageEnglish (US)
Title of host publicationHNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1-5
Number of pages5
Volume2018-January
ISBN (Electronic)9781538609101
DOIs
StatePublished - Jan 24 2018
Event9th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2017 - Manila, Philippines
Duration: Nov 29 2017Dec 1 2017

Other

Other9th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management, HNICEM 2017
CountryPhilippines
CityManila
Period11/29/1712/1/17

Keywords

  • ANN
  • backpropagation
  • crop health assessment
  • Lettuce

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Human-Computer Interaction
  • Information Systems
  • Control and Optimization
  • Artificial Intelligence
  • Ecological Modeling
  • Management, Monitoring, Policy and Law

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