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• W1963939054 abstract "Fruit classification is a difficult challenge due to the numerous types of fruits. In order to recognize fruits more accurately, we proposed a hybrid classification method based on fitness-scaled chaotic artificial bee colony (FSCABC) algorithm and feedforward neural network (FNN). First, fruits images were acquired by a digital camera, and then the background of each image were removed by split-and-merge algorithm. We used a square window to capture the fruits, and download the square images to 256 × 256. Second, the color histogram, texture and shape features of each fruit image were extracted to compose a feature space. Third, principal component analysis was used to reduce the dimensions of the feature space. Finally, the reduced features were sent to the FNN, the weights/biases of which were trained by the FSCABC algorithm. We also used a stratified K-fold cross validation technique to enhance the generation ability of FNN. The experimental results of the 1653 color fruit images from the 18 categories demonstrated that the FSCABC–FNN achieved a classification accuracy of 89.1%. The classification accuracy was higher than Genetic Algorithm–FNN (GA–FNN) with 84.8%, Particle Swarm Optimization–FNN (PSO–FNN) with 87.9%, ABC–FNN with 85.4%, and kernel support vector machine with 88.2%. Therefore, the FSCABC–FNN was seen to be effective in classifying fruits." @default.
• W1963939054 created "2016-06-24" @default.
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• W1963939054 date "2014-12-01" @default.
• W1963939054 modified "2023-10-05" @default.
• W1963939054 title "Fruit classification using computer vision and feedforward neural network" @default.
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• W1963939054 doi "https://doi.org/10.1016/j.jfoodeng.2014.07.001" @default.
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