A Neural Network-based Computer Color Vision for Grading Tomatoes (Lycopersicon esculentum)
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CONTRIBUTORS:
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JOURNAL:
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YEAR:
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2007
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PUB TYPE:
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Journal Article
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SUBJECT(S):
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tomato maturity; neural network; image processing; machine vision; machine learning
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DISCIPLINE:
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Computer Science
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HTTP:
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http://www.ics.uplb.edu.ph/node/239
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LANGUAGE:
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English
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PUB ID:
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103-444-119
(Last edited on
2008/07/19 01:44:18 GMT-6)
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SPONSOR(S):
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ABSTRACT:
We developed a computerized color image analysis procedure and a neural network model (NNM) to automate the classification of maturity of fresh tomatoes. Automating the classification procedure will help reduce errors of human graders who compare tomato color with a color chart. The chart is a USDA standard that human graders use to classify fresh tomatoes into six maturity stages: Green, Breakers, Turning, Pink, Light Red, and Red. The repetitive procedure of visually comparing colors is prone to human errors and subjectivity, while the number of wrong classifications increases with time as human graders experience eye stress, boredom and tiredness. Our automated system uses a computer color vision as its artificial “eye” and an NNM as its artificial “brain”. We setup a computer-mounted digital camera that captured 6,000 color images of locally grown and harvested tomatoes equally representing the six maturity stages. We classified each tomato according to the majority grade given by five expert graders from a local commercial farm. We developed a color image analysis procedure to extract the red, green and blue (RGB) spectral values of the captured images. We designed a tomato maturity classifier based on a 3-layer NNM that uses the RGB spectral values as inputs. The NNM was trained via the feed-forward, back propagation algorithm using 4,200 images as training data and 600 images as test data, equally representing each maturity stage. To avoid model over-fitting, we used the NNM errors with the test data as the training’s stopping criteria. We used the remaining 1,200 images to validate the model and results show that the system agreed with manual grading 97% of the time. The remaining 3% were misclassifications but within one maturity stage difference. This result shows that our automatic vision system possesses the same grading accuracy as the human experts but is more efficient than the manual grading.
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