Artificial Neural Network Approach for the Identification of Clove Buds Origin Based on Metabolites Composition
This work addresses a domain-specific problem for agricultural or quality control applications, but it is incremental as it applies existing neural network methods to a new dataset.
This paper tackled the problem of identifying the origin of clove buds using metabolites composition with small datasets, achieving high accuracy, such as 99.91% for training and 99.47% for testing with backpropagation and one hidden layer.
This paper examines the use of artificial neural network approach in identifying the origin of clove buds based on metabolites composition. Generally, large data sets are critical for accurate identification. Machine learning with large data sets lead to precise identification based on origins. However, clove buds uses small data sets due to lack of metabolites composition and their high cost of extraction. The results show that backpropagation and resilient propagation with one and two hidden layers identifies clove buds origin accurately. The backpropagation with one hidden layer offers 99.91% and 99.47% for training and testing data sets, respectively. The resilient propagation with two hidden layers offers 99.96% and 97.89% accuracy for training and testing data sets, respectively.