Quality assessment of lettuce using artificial neural network

College

Gokongwei College of Engineering

Department/Unit

Manufacturing Engineering and Management

Document Type

Conference Proceeding

Source Title

HNICEM 2017 - 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management

Volume

2018-January

First Page

1

Last Page

5

Publication Date

1-24-2018

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. © 2017 IEEE.

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Digitial Object Identifier (DOI)

10.1109/HNICEM.2017.8269506

Disciplines

Manufacturing

Keywords

Lettuce—Quality; Neural networks (Computer science)

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