How can image processing and artificial neural network be used to classify durian species?
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Image processing and artificial neural networks can be used to classify durian species by extracting features from durian images and training a model to recognize different species based on these features. Several approaches have been proposed in the literature. Lim and Chuah developed a method using convolutional neural networks (CNN) to classify different cultivars of Durio zibethinus (durian) based on visual features, achieving an accuracy of 82.50% . Mustaffa et al. proposed a content-based recognition framework using shape and texture features extracted from durian images, achieving 100% accuracy with Logistic Regression, Gaussian Naive Bayesian, and Linear Discriminant Analysis classifiers . Nyon et al. also used shape descriptors and K-Nearest Neighbors algorithm to achieve 100% recognition rate for durian species . Amlekar and Gaikwad presented a method using leaf shape features and neural networks for plant classification, achieving up to 99% accuracy . Pacifico et al. used an artificial neural network trained with Backpropagation algorithm to classify plants, showing potential for unbiased plant classification .
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| Papers (3) | Insight |
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01 Aug 2018 15 Citations | The research work uses image processing and convolutional neural network (CNN) to classify different cultivars of durian based on visual features, achieving an accuracy of 82.50%. |
26 Mar 2018 5 Citations | The paper proposes a durian species recognition system using global shape representations and the K-Nearest Neighbors algorithm. |
7 Citations | The paper does not mention the use of image processing or artificial neural networks for durian species classification. |
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