Combination of Morphology, Wavelet and Convex Hull Features in Classification of Patchouli Varieties with Imbalance Data using Artificial Neural Network
نویسندگان
چکیده
Patchouli plants are main raw materials for essential oils in Indonesia. leaves have a very varied physical form based on the area planted, making it difficult to recognize variety. This condition makes farmers these varieties and they need experts’ advice. As there few experts this field, technology identifying types of patchouli is required. In study, identification model constructed using combination leaf morphological features, texture features extracted with Wavelet shape convex hull. The results feature extraction used as input data training classification algorithms. effectiveness tested three methods class artificial neural network algorithms: (1) feedforward networks backpropagation algorithm training, (2) learning vector quantization (LVQ), (3) extreme machine (ELM). Synthetic minority over-sampling technique (SMOTE) applied solve problem imbalance variety dataset. system by combining indicate level recognition an average accuracy 72.61%, higher when compared only (58.68%) or (59.03 %) both (67.25%). study also showed that use SMOTE increases highest 88.56%.
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ژورنال
عنوان ژورنال: Journal of Applied Research and Technology
سال: 2021
ISSN: ['2448-6736']
DOI: https://doi.org/10.22201/icat.24486736e.2021.19.6.1017