Garlic Field Classification Using Machine Learning and Statistic Approaches

Abstract

The level of garlic consumption in Indonesia increases as the population grows. This is because most of the ingredients of Indonesian food recipes contain garlic. However, local garlic production is not sufficient to fulfil the demand. Therefore, the Indonesian government imported garlic from other countries to fulfil the demand. To reduce the import capacity of garlic, the government made a regulation to increase the potential area for garlic cultivation in several priority locations in Indonesia, one of which is Sembalun District, East Lombok. To support government regulation, this study presents an application of machine learning and a statistic approach for the garlic field mapping method in Sembalun, Indonesia. This study comprises several steps including the Sentinel-1A images data acquisition, image preprocessing, machine learning and statistic model training, and model evaluation. k-nearest neighbor (k-NN) and maximum likelihood classification (MLC) methods are selected in this study. The performance of k-NN and MLC are compared to other garlic field classification results developed in previous studies using pixel-based and image-based classifications. The comparison results show that the k-NN classification is slightly better than the SVM classification and also that it outperformed the MLC method. In addition, MLC works faster than k-NN in learning the dataset and testing the models. The classification results can be used to estimate garlic production in the study area. The study concludes that the proposed methods are better than other classification models and the statistic approach. The future study will improve dataset quality to increase the model’s accuracy.