ENHANCING HERBAL PLANT LEAF IMAGE DETECTION ACCURACY THROUGH MOBILENET ARCHITECTURE OPTIMIZATION IN CNN

Authors

  • Anan Wibowo STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Rahmat Zulpani STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Agus Perdana Windarto STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Anjar Wanto STIKOM Tunas Bangsa, Pematangsiantar, Indonesia
  • Sundari Retno Andani STIKOM Tunas Bangsa, Pematangsiantar, Indonesia

DOI:

https://doi.org/10.33480/jitk.v10i4.6498

Keywords:

classification, CNN, deep learning, herbal plant, mobilenetv2

Abstract

Herbal plants have various health benefits, but their type identification remains challenging for the general public. This study aims to improve the accuracy of herbal plant leaf classification using Convolutional Neural Network (CNN) based on MobileNetV2 architecture. To enhance model performance, various optimization techniques including fine-tuning, batch normalization, dropout, and learning rate scheduling were implemented. The experimental results showed that the proposed optimized model achieved an accuracy of 100%, significantly outperforming previous studies that used standard MobileNet with an accuracy of 86.7%. While these perfect results warrant additional validation with more diverse datasets to confirm generalizability, this study contributes to the development of a more accurate herbal plant classification system that is readily accessible to the general public. Future work should explore model performance under varying environmental conditions and with expanded plant species datasets.

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References

A. K. Gupta et al., “A trans-disciplinary agro-ecology strategy to grow medicinal plants,” J. Ayurveda Integr. Med., vol. 16, no. 1, p. 100985, 2025.

H. El-Ramady et al., “Plant Nutrition for Human Health: A Pictorial Review on Plant Bioactive Compounds for Sustainable Agriculture,” Sustain., vol. 14, no. 14, 2022.

H. S. Elshafie, I. Camele, and A. A. Mohamed, “A Comprehensive Review on the Biological, Agricultural and Pharmaceutical Properties of Secondary Metabolites Based-Plant Origin,” Int. J. Mol. Sci., vol. 24, no. 4, 2023.

N. K. Kumar and R. Pandey, “Traditional Medicine Review The Anthropological Study of Traditional Medicine in South Africa: Unveiling Complexities, Nurturing Traditions Rajendran Govender Role of Standardization and Quality Control in Manufacturing of Herbal Medicines Book rEviEw Acc,” vol. 3, no. 2, 2023.

M. Amir, M. Vohra, R. G. Raj, I. Osoro, and A. Sharma, “Adaptogenic herbs: A natural way to improve athletic performance,” Heal. Sci. Rev., vol. 7, no. March, p. 100092, 2023.

A. A. Elkordy, R. R. Haj-Ahmad, A. S. Awaad, and R. M. Zaki, “An overview on natural product drug formulations from conventional medicines to nanomedicines: past, present and future,” J. Drug Deliv. Sci. Technol., vol. 63, no. 1, pp. 1–14, 2021.

P. Bolouri et al., “Applications of Essential Oils and Plant Extracts in Different Industries,” Moleculas, vol. 27, no. 24, pp. 1–17, 2022.

M. Wang, H. Lin, H. Lin, P. Du, and S. Zhang, “From Species to Varieties: How Modern Sequencing Technologies Are Shaping Medicinal Plant Identification,” Genes (Basel)., vol. 16, no. 1, pp. 1–18, 2025.

O. A. Malik, N. Ismail, B. R. Hussein, and U. Yahya, “Automated Real-Time Identification of Medicinal Plants Species in Natural Environment Using Deep Learning Models—A Case Study from Borneo Region,” Plants, vol. 11, no. 15, 2022.

H. Wang, Y. Chen, L. Wang, Q. Liu, S. Yang, and C. Wang, “Advancing herbal medicine: enhancing product quality and safety through robust quality control practices,” Front. Pharmacol., vol. 14, no. September, pp. 1–16, 2023.

R. Cahyaningsih, “GENETIC CONSERVATION AND SUSTAINABLE USE OF INDONESIAN MEDICINAL PLANTS,” Univ. Birmingham, vol. 75, no. 17, pp. 399–405, 2021.

M. S. Ikrar Musyaffa, N. Yudistira, M. A. Rahman, A. H. Basori, A. B. Firdausiah Mansur, and J. Batoro, “IndoHerb: Indonesia medicinal plants recognition using transfer learning and deep learning,” Heliyon, vol. 10, no. 23, p. e40606, 2024.

A. Ahmad, D. Saraswat, and A. El Gamal, “A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools,” Smart Agric. Technol., vol. 3, no. June 2022, p. 100083, 2023.

S. Saggar et al., “Traditional and Herbal Medicines: Opportunities and Challenges,” Pharmacognosy Res., vol. 14, no. 2, pp. 107–114, 2022.

Z. Ahmad, S. Rahim, M. Zubair, and J. Abdul-Ghafar, “Artificial intelligence (AI) in medicine, current applications and future role with special emphasis on its potential and promise in pathology: present and future impact, obstacles including costs and acceptance among pathologists, practical and philosoph,” Diagn. Pathol., vol. 16, no. 1, pp. 1–16, 2021.

X. Zhao, L. Wang, Y. Zhang, X. Han, M. Deveci, and M. Parmar, A review of convolutional neural networks in computer vision, vol. 57, no. 4. Springer Netherlands, 2024.

N. Sindhwani, R. Anand, S. Meivel, R. Shukla, M. P. Yadav, and V. Yadav, “Performance Analysis of Deep Neural Networks Using Computer Vision,” EAI Endorsed Trans. Ind. Networks Intell. Syst., vol. 8, no. 29, pp. 1–11, 2021.

Zewen Li, Fan Liu, Wenjie Yang, Shouheng Peng, and Jun Zhou, “A survey of convolutional neural networks: analysis, applications, and prospects,” IEEE Trans. neural networks Learn. Syst., vol. 33, no. 12, pp. 6999–7019, 2021.

G. Li et al., “Practices and applications of convolutional neural network-based computer vision systems in animal farming: A review,” Sensors, vol. 21, no. 4, pp. 1–42, 2021.

T. Turay and T. Vladimirova, “Toward Performing Image Classification and Object Detection with Convolutional Neural Networks in Autonomous Driving Systems: A Survey,” IEEE Access, vol. 10, pp. 14076–14119, 2022.

F. Modu, R. Prasad, and F. Aliyu, “Lightweight CNN for Resource-constrained BCD System using Knowledge Distillation,” IEEE Access, vol. 13, no. March, pp. 57504–57529, 2025.

M. Maaz et al., “EdgeNeXt: Efficiently Amalgamated CNN-Transformer Architecture for Mobile Vision Applications,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 13807 LNCS, pp. 3–20, 2023.

E. Prasetyo, R. Purbaningtyas, R. D. Adityo, N. Suciati, and C. Fatichah, “Combining MobileNetV1 and Depthwise Separable convolution bottleneck with Expansion for classifying the freshness of fish eyes,” Inf. Process. Agric., vol. 9, no. 4, pp. 485–496, 2022.

Y. Gulzar, “Fruit Image Classification Model Based on MobileNetV2 with Deep Transfer Learning Technique,” Sustainability, vol. 15, no. 3, 2023.

S. K. Bharadwaj, R. Jha, J. Kumar, D. K. Mishra, V. Shinde, and V. K. Jadon, “COMPARATIVE STUDY OF MOBILENETV2, SIMPLE CNN AND VGG19 FOR IMAGE CLASSIFICATION,” J. Data Acquis. Process., vol. 15, no. 1, pp. 37–48, 2024.

G. Gondhalekar et al., “Enhancing Image Classification Performance through Transfer Learning and Adaptive Augmentation : A MobileNetV2 Approach,” 3rd Int. Conf. Optim. Tech. F. Eng., no. January, p. 11, 2024.

I. N. Purnama, “Herbal Plant Detection Based on Leaves Image Using Convolutional Neural Network With Mobile Net Architecture,” JITK (Jurnal Ilmu Pengetah. dan Teknol. Komputer), vol. 6, no. 1, pp. 27–32, 2020.

H. R. Maier et al., “On how data are partitioned in model development and evaluation: Confronting the elephant in the room to enhance model generalization,” Environ. Model. Softw., vol. 167, no. June, p. 105779, 2023.

A. Lal, A. Sharan, K. Sharma, A. Ram, D. K. Roy, and B. Datta, “Scrutinizing different predictive modeling validation methodologies and data-partitioning strategies: new insights using groundwater modeling case study,” Environ. Monit. Assess., vol. 196, no. 7, 2024.

E. Lopez, J. Etxebarria-Elezgarai, J. M. Amigo, and A. Seifert, “The importance of choosing a proper validation strategy in predictive models. A tutorial with real examples,” Anal. Chim. Acta, vol. 1275, no. June, p. 341532, 2023.

J. Sadaiyandi, P. Arumugam, A. K. Sangaiah, and C. Zhang, “Stratified Sampling-Based Deep Learning Approach to Increase Prediction Accuracy of Unbalanced Dataset,” Electron., vol. 12, no. 21, pp. 1–16, 2023.

K. Maharana, S. Mondal, and B. Nemade, “A review: Data pre-processing and data augmentation techniques,” Glob. Transitions Proc., vol. 3, no. 1, pp. 91–99, 2022.

T. Kumar, A. Mileo, R. Brennan, and M. Bendechache, “Image Data Augmentation Approaches: A Comprehensive Survey and Future directions,” vol. 12, no. September, 2023.

C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” J. Big Data, vol. 6, no. 1, 2019.

K. Medvedieva, T. Tosi, E. Barbierato, and A. Gatti, “Balancing the Scale: Data Augmentation Techniques for Improved Supervised Learning in Cyberattack Detection,” Eng, vol. 5, no. 3, pp. 2170–2205, 2024.

R. Indraswari, R. Rokhana, and W. Herulambang, “Melanoma image classification based on MobileNetV2 network,” Procedia Comput. Sci., vol. 197, pp. 198–207, 2021.

A. Tripathi, T. Singh, R. R. Nair, and P. Duraisamy, “Improving Early Detection and Classification of Lung Diseases With Innovative MobileNetV2 Framework,” IEEE Access, vol. 12, no. June, pp. 116202–116217, 2024.

R. Rashid, W. Aslam, R. Aziz, and G. Aldehim, “A Modified MobileNetv3 Coupled With Inverted Residual and Channel Attention Mechanisms for Detection of Tomato Leaf Diseases,” IEEE Access, vol. 13, no. March, pp. 52683–52696, 2025.

M. J. Adamu et al., “Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2–Support Vector Machine for Magnetic Resonance Imaging Diagnostics in Neoplasms,” Brain Sci., vol. 14, no. 12, 2024.

B. A. Kumar and M. Bansal, “Face Mask Detection on Photo and Real-Time Video Images Using Caffe-MobileNetV2 Transfer Learning,” Appl. Sci., vol. 13, no. 2, 2023.

B. J. Bipin Nair, B. Arjun, S. Abhishek, N. M. Abhinav, and V. Madhavan, “Classification of Indian Medicinal Flowers using MobileNetV2,” Proc. 18th INDIAcom; 2024 11th Int. Conf. Comput. Sustain. Glob. Dev. INDIACom 2024, no. February, pp. 1512–1518, 2024.

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Published

2025-05-30

How to Cite

[1]
A. Wibowo, R. Zulpani, A. P. Windarto, A. Wanto, and S. R. Andani, “ENHANCING HERBAL PLANT LEAF IMAGE DETECTION ACCURACY THROUGH MOBILENET ARCHITECTURE OPTIMIZATION IN CNN”, jitk, vol. 10, no. 4, pp. 859–867, May 2025.