Identification of Cactus Species Using the MobileNet Convolutional Neural Network Model
DOI:
https://doi.org/10.53697/jkomitek.v4i2.1933Keywords:
Cactus Classification, Convolutional Neural Network (CNN),, MobileNet ArchitectureAbstract
The objective of this design is to create a system capable of accurately and precisely classifying different types of succulent cacti. The system design aims to create a succulent cactus classification application using a Convolutional Neural Network (CNN) with the MobileNet architecture. The process involves collecting cactus images, dividing them into training and test datasets, and developing a CNN model to recognize patterns in succulent cactus species. The training data is used to train the CNN model, and the test data evaluates the model's accuracy. The trained model, stored in Tf.lite format, successfully classifies 15 cactus types, achieving high accuracy by employing preprocessing steps like resizing, normalization, and background removal. Over 1,200 cactus images were taken with a smartphone, categorized into 15 classes, and prepared to ensure optimal lighting, angle, background, and resolution (224x224 pixels). The MobileNet model was chosen for its high accuracy and efficiency. Hardware used includes a Samsung A54 smartphone and an Intel i7 laptop, with software such as Python, Kotlin, and Android Studio facilitating development. This design ensures the application’s accessibility, making it a valuable tool for cactus enthusiasts and the general public to easily identify different succulent cactus types. Testing of the cactus species classification program using the Convolutional Neural Network (CNN) method with the MobileNetV2 architecture yielded strong results, achieving over 90% accuracy in classifying 15 cactus species. The highest training accuracy of 0.9837 was achieved at 150 epochs without early stopping, outperforming other epoch configurations. The model successfully classified species across five main genera—Kalanchoe, Crassula, Echeveria, Haworthia, and Euphorbia. This high accuracy highlights the model's effectiveness, making it a useful tool for cactus enthusiasts and the public to accurately identify and distinguish cactus species.
References
Astriani, L., Bahfen, M., Mulyanto, T. Y., & Istikomah, I. (2019). Pemberdayaan Masyarakat melalui Budidaya Tanaman Hias Sukulen dalam Pot. Prosiding Seminar Nasional Pengabdian Masyarakat LPPM UMJ, 1(1), Article 1. https://jurnal.umj.ac.id/index.php/semnaskat/article/view/8856
Kaur, S., Rakhra, M., Singh, D., Singh, A., & Aggarwal, S. (2022). Disease Detection in Cactus (Beles) via the Use of Machine Learning: A Proposed Technique. 2022 2nd International Conference on Technological Advancements in Computational Sciences (ICTACS), 772–778. https://doi.org/10.1109/ICTACS56270.2022.9988580
Kurnia, D., & Wibowo, A. T. (2021). Klasifikasi Spesies Tanaman Kaktus Grafting Berdasarkan Citra Scion Menggunakan Metode Convolutional Neural Network (cnn). eProceedings of Engineering, 8(4). https://openlibrarypublications.telkomuniversity.ac.id/index.php/engineering/article/view/15244
Naufal, M. F. (2021). Analisis Perbandingan Algoritma Svm, Knn, Dan Cnn untuk Klasifikasi Citra Cuaca. Jurnal Teknologi Informasi Dan Ilmu Komputer, 8(2), Article 2.
Pradiatiningtyas, D. (2022). Edukasi Budidaya Tanaman Hias Kaktus Dan Pemasaran Melalui Media Digital Pada Komunitas Nirlaba Cactus And Succulent Society Of Indonesia (CSSI). SPEED - Sentra Penelitian Engineering Dan Edukasi, 14(1), Article 1. https://doi.org/10.55181/speed.v14i1.750
Supirman, S., Lubis, C., Yuliarto, D., & Perdana, N. J. (2023). Klasifikasi Penyakit Kulit Menggunakan Convolutional Neural Network (Cnn) Dengan Arsitektur VGG16. Simtek : Jurnal Sistem Informasi Dan Teknik Komputer, 8(1), Article 1. https://doi.org/10.51876/simtek.v8i1.217
Wakhidaturrohmah, N., Murtaqib, M., & Kushariyadic, K. (2022). Meningkatkan Usaha Budidaya Tanaman Hias Suculen Dan Kaktus Dengan Inovasi Packaging “Sucubox” Serta Pemasaran Media Sosial Didesa Kebonsari, Kecamatan Sumbersari, Kabupaten Jember. PROSIDING SEMINAR NASIONAL PENGABDIAN KEPADA MASYARAKAT, 2(1), Article 1. https://doi.org/10.33086/snpm.v2i1.953
Alshehhi, R. (2021). Detection of Coronal Mass Ejections Using Unsupervised Deep Clustering. Solar Physics, 296(6). https://doi.org/10.1007/s11207-021-01854-w
Atitallah, S. B. (2021). An Enhanced Randomly Initialized Convolutional Neural Network for columnar cactus recognition in unmanned aerial vehicle imagery. Procedia Computer Science, 192, 573–581. https://doi.org/10.1016/j.procs.2021.08.059
Benyahia, S. (2022). Multi-features extraction based on deep learning for skin lesion classification. Tissue and Cell, 74. https://doi.org/10.1016/j.tice.2021.101701
Berka, A. (2023). CactiViT: Image-based smartphone application and transformer network for diagnosis of cactus cochineal. Artificial Intelligence in Agriculture, 9, 12–21. https://doi.org/10.1016/j.aiia.2023.07.002
Kassania, S. H. (2021). Automatic Detection of Coronavirus Disease (COVID-19) in X-ray and CT Images: A Machine Learning Based Approach. Biocybernetics and Biomedical Engineering, 41(3), 867–879. https://doi.org/10.1016/j.bbe.2021.05.013
Kogilavani, S. V. (2022). COVID-19 Detection Based on Lung Ct Scan Using Deep Learning Techniques. Computational and Mathematical Methods in Medicine, 2022. https://doi.org/10.1155/2022/7672196
Li, Y. (2022). EfficientFormer: Vision Transformers at MobileNet Speed. Advances in Neural Information Processing Systems, 35.
Maji, D. (2022). Attention Res-UNet with Guided Decoder for semantic segmentation of brain tumors. Biomedical Signal Processing and Control, 71. https://doi.org/10.1016/j.bspc.2021.103077
Ohata, E. F. (2021). Automatic detection of COVID-19 infection using chest X-ray images through transfer learning. IEEE/CAA Journal of Automatica Sinica, 8(1), 239–248. https://doi.org/10.1109/JAS.2020.1003393
Perez, M. F. (2022). Coalescent-based species delimitation meets deep learning: Insights from a highly fragmented cactus system. Molecular Ecology Resources, 22(3), 1016–1028. https://doi.org/10.1111/1755-0998.13534
Srinivasu, P. N. (2021). Classification of skin disease using deep learning neural networks with mobilenet v2 and lstm. Sensors, 21(8). https://doi.org/10.3390/s21082852
Yao, H. (2017). An end-to-end method of Coronal Mass Ejections detection. Kexue Tongbao/Chinese Science Bulletin, 62(23), 2680–2690. https://doi.org/10.1360/N972016-00382
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