De Grano and Pabico (2007) Automating the Classification of Tomato (Lycopersicon esculentum) Maturity Using Image Analysis and Neural Networks

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Automating the Classification of Tomato (Lycopersicon esculentum) Maturity Using Image Analysis and Neural Networks

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CONTRIBUTORS:

	Author:	De Grano, Alona V.
	Author:	Pabico, Jaderick P. (University of the Philippines Los Banos)

JOURNAL:

Transactions of the National Academy of Science and Technology, Philippines, 29(1), 131 - 132.

YEAR:

2007

PUB TYPE:

Journal Article

SUBJECT(S):

tomato grading; neural network; image processing; feed-forward; back-propagation

DISCIPLINE:

Engineering and Applied Sciences

HTTP:

http://www.ics.uplb.edu.ph/node/229

LANGUAGE:

English

PUB ID:

103-444-127 (Last edited on 2008/07/19 02:15:25 GMT-6)

SPONSOR(S):

ABSTRACT:
Color in tomato (Lycopersicon esculentum) is the most important external characteristic to assess ripeness and postharvest life, and is a major factor in the consumer’s purchase decision. The degree of ripening is usually estimated visually by human graders comparing the tomato color to a chart that classify fresh tomatoes into six maturity stages according to the USDA standard classification: Green, Breakers, Turning, Pink, Light Red, and Red. This manual practice of tomato maturity classification often results into errors due to human subjectivitiy, visual stress, and tiredness. We developed a color image analysis procedure and a neural network model to automate the classification of tomato maturity. We captured using a computer-connected digital camera 6,000 color images of locally grown and harvested tomatoes equally representing the six maturity stages (1,000 each). The average classification by five expert graders from a local commercial farm was used as the maturity classification of each tomato. Using the red, green and blue (RGB) spectral values of the captured images as inputs, we trained a neural network-based tomato maturity classifier to indicate the degree of maturity within each stage and to provide a continuous index over the complete maturity range. We trained a 3-layer neural network via the feed-forward, back propagation training algorithm using 70% of the captured images as the training set (4,200 images) and 10% as the test set (600 images), equally representing each maturity stage. The test set was used during training to avoid model over-fitting. Validation results agreed with manual grading in 97% of the remaining tomatoes (1,200 images), while the remaining 3% were classified wrongly but within one maturity stage difference. With this result, an automatic vision system for tomato grading could be a potent alternative to manual grading.

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