GreenEye: Plant Classification Using MobileNet V2
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Abstract
Biodiversity in Indonesia includes more than 30,000 species of plantsand mushrooms, but public knowledge about these plants is still limited. The research aims to develop a mobile application called GreenEye that uses machine learning to detect and classify plants based on images. The model used is based on the MobileNet V2 architecture, a type of Convolutional Neural Network (CNN) designed for high-efficiency image classification tasks. Research data collected from PlantNet and Google Images, consisting of 2800 images covering seven plant species: Ananas comosus, Artocarpus heterophyllus, Carica papaya, Cocos nucifera, Musa spp, Nephelium lappaceum, and Salacca zalacca. Each species is categorized into four plant parts: fruit, flower, leaf, and habit. (habitus). This data is then processed through various preprocessing stages such as data cleaning, format conversion, resizing, cropping, and image augmentation. The results showed that the MobileNet V2 model was able to classify parts of plants with high accuracy, especially on fruits and leaves with accurations above 90%. However, the accuration was slightly lower for flowers and habits, which is about 70%. Classification errors occurred mainly in species with high visual similarities. To improve the performance of the model, it is recommended that further research increase the quantity and diversity of datasets.
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