Quality assessment of lettuce using artificial neural network

• Main Authors: Valenzuela, Ira C., Puno, John Carlo V., Bandala, Argel A., Baldovino, Renann G., De Luna, Robert G., De Ocampo, Anton Louise, Cuello, Joel, Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2018
• Subjects: Lettuce—Quality; Neural networks (Computer science); Manufacturing
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1733; https://animorepository.dlsu.edu.ph/context/faculty_research/article/2732/type/native/viewcontent
• Institution: De La Salle University

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.