Journal ArticleDOI
Fruit classification by biogeography-based optimization and feedforward neural network
TLDR
The proposed BBO‐FNN was effective in fruit‐classification in terms of classification accuracy and computation time, which indicated that it can be applied in credible use.Abstract:
Accurate fruit classification is difficult to accomplish because of the similarities among the various categories. In this paper, we proposed a novel fruit-classification system, with the goal of recognizing fruits in a more efficient way. Our methodology included the following steps. First, a four-step pre-processing was employed. Second, the features colour, shape, and texture were extracted. Third, we utilized principal component analysis to remove excessive features. Fourth, a novel fruit-classification system based on biogeography-based optimization BBO and feedforward neural network FNN was proposed, with the short name of BBO-FNN. The experiment employed over 1653 chromatic fruit images 18 categories by fivefold stratified cross-validation. The results showed that the proposed BBO-FNN yielded an overall accuracy of 89.11%, which was higher than the five state-of-the-art methods: genetic algorithm-FNN, artificial bee colony-FNN, particle swarm optimization-FNN, kernel support vector machine, and ant colony optimization-FNN. Also, the BBO-FNN achieved the same accuracy as fitness-scaling chaotic artificial bee colony-FNN, but it performed much faster than the latter. The proposed BBO-FNN was effective in fruit-classification in terms of classification accuracy and computation time. This indicated that it can be applied in credible use.read more
Citations
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Journal ArticleDOI
Apple detection during different growth stages in orchards using the improved YOLO-V3 model
TL;DR: The test results show that the proposed YOLOV3-dense model is superior to the original YOLO-V3 model and the Faster R-CNN with VGG16 net model, which is the state-of-art fruit detection model.
Journal ArticleDOI
Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation
TL;DR: This study designed and validated a 13-layer convolutional neural network (CNN) that is effective in image-based fruit classification and observed using data augmentation can increase the overall accuracy.
Journal ArticleDOI
Fruits and vegetables quality evaluation using computer vision: A review
Anuja Bhargava,Atul Bansal +1 more
TL;DR: A critical comparison of different algorithm proposed by researchers for quality inspection of fruits and vegetables has been carried out and a detailed overview of various methods i.e. preprocessing, segmentation, feature extraction, classification which addressed fruit and vegetables quality based on color, texture, size, shape and defects is presented.
Journal ArticleDOI
Intelligent facial emotion recognition based on stationary wavelet entropy and Jaya algorithm
TL;DR: This team proposed a novel intelligent emotion recognition system that used stationary wavelet entropy to extract features, and employed a single hidden layer feedforward neural network as the classifier, and introduced the Jaya algorithm.
Journal ArticleDOI
A Review of Convolutional Neural Network Applied to Fruit Image Processing
José Naranjo-Torres,Marco Mora,Ruber Hernández-García,Ricardo J. Barrientos,Claudio Fredes,Andres Valenzuela +5 more
TL;DR: This article presents a review of the use of CNN applied to different automatic processing tasks of fruit images: classification, quality control, and detection, and observes that in the last two years (2019–2020), theUse of CNN for fruit recognition has greatly increased obtaining excellent results.
References
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Journal ArticleDOI
Grey Wolf Optimizer
TL;DR: The results of the classical engineering design problems and real application prove that the proposed GWO algorithm is applicable to challenging problems with unknown search spaces.
Journal ArticleDOI
Biogeography-Based Optimization
TL;DR: This paper discusses natural biogeography and its mathematics, and then discusses how it can be used to solve optimization problems, and sees that BBO has features in common with other biology-based optimization methods, such as GAs and particle swarm optimization (PSO).
Journal ArticleDOI
Texture classification and segmentation using wavelet frames
TL;DR: In this paper, a new approach to the characterization of texture properties at multiple scales using the wavelet transform is described, which uses an overcomplete wavelet decomposition, which yields a description that is translation invariant.
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A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications
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Journal ArticleDOI
Binary PSO with mutation operator for feature selection using decision tree applied to spam detection
TL;DR: A novel spam detection method that focused on reducing the false positive error of mislabeling nonspam as spam, which demonstrated the MBPSO is superior to GA, RSA, PSO, and BPSO in terms of classification performance and wrappers are more effective than filters with regard to classification performance indexes.
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