Automated grapevine cultivar classification based on machine learning using leaf morpho-colorimetry, fractal dimension and near-infrared spectroscopy parameters.
Sigfredo FuentesEsther Hernández-MontesJ. M. EscalonaJosefina BotaClaudia Gonzalez ViejoCarlos Poblete-EcheverríaEden Jane TongsonH. MedranoPublished in: Comput. Electron. Agric. (2018)
Keyphrases
- fractal dimension
- machine learning
- fractal analysis
- pattern recognition
- machine learning methods
- fractal dimensions
- automated classification
- supervised machine learning
- decision trees
- machine learning algorithms
- supervised learning
- feature selection
- natural textures
- text classification
- box counting
- feature vectors
- classification accuracy
- quantitative analysis
- texture analysis
- computer vision
- model selection
- learning algorithm
- parameter estimation
- neural network
- support vector machine
- object recognition
- image processing
- logistic regression
- computer aided
- support vector machine svm
- expectation maximization
- gray level images
- training set
- feature space
- tissue classification
- multiscale
- feature extraction