Article Open Access

Deep Learning-Based Mobile Application for Ornamental Plant Classification

Muhamad Nur Gunawan, Syopiansyah Jaya Putra, Maulana Rifan Haditama

Abstract


This study aims to develop and evaluate a deep learning-based mobile application for the automated classification of ornamental plants, addressing challenges associated with visual similarity among species and environmental variability during image acquisition. The proposed system utilizes a Convolutional Neural Network (CNN) based on the MobileNetV2 architecture, selected for its lightweight structure and deployment efficiency on resource-constrained mobile devices. The dataset comprises approximately 600 images representing 10 ornamental plant classes, collected from real-world environments, and processed through a standardized preprocessing pipeline. Model training was conducted using the Teachable Machine platform over 100 epochs, with a batch size of 16 and a learning rate of 0.001, allocating 90% of the dataset for training and 10% for testing. Experimental results indicate that the proposed model achieves a classification accuracy of 96.3%, corroborated by evaluation metrics including accuracy curves, loss convergence, and class-wise performance analysis. The trained model was successfully converted into a lightweight format and integrated into an Android-based mobile application developed using the Flutter framework. Functional testing demonstrates that the application performs effectively in real-time classification scenarios, maintaining high accuracy and responsive on-device inference without relying on cloud computing. In conclusion, this study confirms that lightweight deep learning architectures, such as MobileNetV2, can be effectively implemented in mobile environments for ornamental plant classification. The proposed application enhances accessibility and usability, enabling rapid and accurate plant identification. Furthermore, this approach contributes to practical applications in horticulture, education, and biodiversity awareness, while demonstrating the feasibility of deploying efficient deep learning models on mobile platforms.


Keywords


Deep Learning, Convolutional Neural Network, Mobile Application, Plant Classification

References


P. Alit et al., “Labeling of an intra-class variation object in deep learning classification,” IAES Int. J. Artif. Intell. IJ-AI, 2022, [Online]. Available: https://api.semanticscholar.org/CorpusId:267803597

L. Peng, J. Zhang, Y. Li, and G. Du, “A novel percussion-based approach for pipeline leakage detection with improved MobileNetV2,” Eng. Appl. Artif. Intell., May 2024.

P. Sajitha, A. D. Andrushia, N. Anand, and M. Z. Naser, “A review on machine learning and deep learning image-based plant disease classification for industrial farming systems,” J. Ind. Inf. Integr., vol. 38, p. 100572, Mar. 2024, doi: 10.1016/j.jii.2024.100572.

M. Reda, R. Suwwan, S. Alkafri, Y. Rashed, and T. Shanableh, “AgroAId: A Mobile App System for Visual Classification of Plant Species and Diseases Using Deep Learning and TensorFlow Lite,” Informatics, vol. 9, p. 55, 2022.

M. F. Adak, “Identification of Plant Species by Deep Learning and Providing as A Mobile Application,” Sak. Univ. J. Comput. Inf. Sci., 2020, [Online]. Available: https://api.semanticscholar.org/CorpusId:230647152

Mr. P. S. Prasad and Dr. A. Senthilrajan, “Leaf Features Extraction for Plant Classification using CNN,” Int. J. Adv. Res. Sci. Commun. Technol., pp. 148–154, Feb. 2021, doi: 10.48175/ijarsct-807.

C. Lee, K. Lim, Y. Song, and A. Alqahtani, “Plant-CNN-ViT: Plant Classification with Ensemble of Convolutional Neural Networks and Vision Transformer,” Plants, vol. 12, 2023, [Online]. Available: https://api.semanticscholar.org/CorpusId:259911794

S. M. Omer, R. M. Hasan, and B. N. Anwer, “An Image Dataset Construction for Flower Recognition Using Convolutional Neural Network,” Sci. J. Univ. Zakho, 2020, [Online]. Available: https://api.semanticscholar.org/CorpusId:222508860

S. Dahiya, T. Gulati, and D. Gupta, “Performance analysis of deep learning architectures for plant leaves disease detection,” Meas. Sens., vol. 24, p. 100581, Dec. 2022, doi: 10.1016/j.measen.2022.100581.

M. Badiger and J. A. Mathew, “Tomato plant leaf disease segmentation and multiclass disease detection using hybrid optimization enabled deep learning,” J. Biotechnol., vol. 374, pp. 101–113, Sep. 2023, doi: 10.1016/j.jbiotec.2023.07.011.

I. Anjani, Y. Pratiwi, and N. Bagas, “Implementation of Deep Learning Using Convolutional Neural Network Algorithm for Classification Rose Flower,” J. Phys. Conf. Ser., vol. 1842, 2021, [Online]. Available: https://api.semanticscholar.org/CorpusId:233830566

J. Zhao and S. Chu, “Research on Flower Image Classification Algorithm Based on Convolutional Neural Network,” J. Phys. Conf. Ser., vol. 1994, 2021, [Online]. Available: https://api.semanticscholar.org/CorpusId:236974117

Z. Huang, X. Jiang, S. Huang, S. Qin, and S. Yang, “An efficient convolutional neural network-based diagnosis system for citrus fruit diseases,” Front. Genet., vol. 14, Aug. 2023, doi: 10.3389/fgene.2023.1253934.

R. Azadnia, M. M. Al-Amidi, H. Mohammadi, M. A. Cifci, A. Daryab, and E. Cavallo, “An AI Based Approach for Medicinal Plant Identification Using Deep CNN Based on Global Average Pooling,” Agronomy, vol. 12, no. 11, p. 2723, Nov. 2022, doi: 10.3390/agronomy12112723.

A. D. Sawarkar, D. D. Shrimankar, S. Ali, A. Agrahari, and L. Singh, “Bamboo Plant Classification Using Deep Transfer Learning with a Majority Multiclass Voting Algorithm,” Appl. Sci., vol. 14, no. 3, p. 1023, Jan. 2024, doi: 10.3390/app14031023.

T. Nguyen, L. Dang, T.-D. do, and J. Lim, “The First Study of White Rust Disease Recognition by Using Deep Neural Networks and Raspberry Pi Module Application in Chrysanthemum,” Inventions, 2023, [Online]. Available: https://api.semanticscholar.org/CorpusId:259005373

N. A. Ideastari, C. A. Sari, E. Faisal, Z. Arifin, A. D. Krismawan, and M. Muslih, “An optimum hyperparameters of restnet-50 for orchid classification based on convolutional neural network,” J. Soft Comput. Explor., 2024, [Online]. Available: https://api.semanticscholar.org/CorpusId:268842034

J. Wang, Y. Tian, R. Zhang, Z. Liu, Y. Tian, and S. Dai, “Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification,” Front. Plant Sci., vol. 13, 2022, [Online]. Available: https://api.semanticscholar.org/CorpusId:249446572

A. Shelke and N. Mehendale, “A CNN-based android application for plant leaf classification at remote locations,” Neural Comput. Appl., vol. 35, no. 3, pp. 2601–2607, Sep. 2022, doi: 10.1007/s00521-022-07740-1.

Y. Kaya and E. Gürsoy, “A novel multi-head CNN design to identify plant diseases using the fusion of RGB images,” Ecol. Inform., vol. 75, p. 101998, Jul. 2023, doi: 10.1016/j.ecoinf.2023.101998.

P. Nasra et al., “Optimized ReXNet variants with spatial pyramid pooling, CoordAttention, and convolutional block attention module for money plant disease detection,” Discov. Sustain., vol. 6, 2025, [Online]. Available: https://api.semanticscholar.org/CorpusId:278541360




DOI: https://doi.org/10.52088/ijesty.v6i2.1808

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International Journal of Engineering, Science, and Information Technology (IJESTY) eISSN 2775-2674