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A New Data Preprocessing Framework to Enhance the Accuracy of Herbal Plants Classification Using Deep Learning
Abstract
This research proposes to solve the problem of herbal plant classification, which plays a key role in Thai pharmacy and traditional medicine. Moreover, there are limitations due to similar physical characteristics of plants and the reliance on specialists to classify herbal plants, which hinder the utilization of herbal plants by the general public at the local level. To solve this problem, this research presents a new preprocessing framework called P4, which integrates 7 techniques as follow: Image Cropping, Resizing, Normalization (0–1), Data Augmentation, Label Noise, Label Cleaning, and Dataset Quality Score (DQS). The prominent point of P4 technique is the combination of intentional mislabeling and label cleaning process, as well as, quantitative data quality assessment and additional expert review in order to filter out potentially inaccurate data before inputting to Deep Learning model. In the experiment, a dataset of 4,211 herbal images covering 30 herbal plant species is used and compared with 3 proposed techniques in previous research (P1–P3) with 5 deep learning architectures, namely DenseNet201, EfficientNetB7, ViT, Swin Transformer, and ConvNeXt. The experimental results showed that the P4 technique combined with DenseNet201 model provided the highest performance in herbal plant classification, with an Accuracy of 92%, Precision of 92%, Recall of 91%, and a training time of merely 22.92 minutes. This was a result of combining the good data quality from the P4 technique, which enhanced to increase efficiency in producing higher quality and more balanced data. When combined with the structural capability of DenseNet201 that supported feature reuse from previous layers, it increased the robustness to mislabeled data and was able to accurately distinguish plants with similar characteristics. The results of this experiment are able be applied as a guideline for future application in Thai traditional medicine support system and herbal plant learning system.
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J. Claude and N. Kitana, On the edge of the sixth mass extinction in biodiversity hotspots: Facts, needs, solutions and opportunities in Thailand and adjacent countries. 2024.
C. Nangsue, P. Srathong, K. Choowongkomon, K. Promnikorn, and C. Tocharus, “Anticancer Activity of the Thai Herbal Formula ‘Kerra’ Versus Afatinib and Doxorubicin In Vitro,” WSEAS Transactions on Biology and Biomedicine, vol. 21, pp. 155–161, 2024, doi: 10.37394/23208.2024.21.16.
S. Pinmongkholgul, “Cultivation and production for enhanced commercial and pharmaceutical applications of Kariyat (Andrographis paniculata) products, Phayao, Thailand,” Pakistan Journal of Life and Social Sciences, vol. 22, no. 2, pp. 3530–3538, 2024, doi: 10.57239/PJLSS-2024-22.2.00260.
A. Singh and P. S. Negi, “Biotechnological application of health-promising bioactive compounds,” in Biotechnological intervention in production of bioactive compounds, J. Devi, Ed., in Sustainable Landscape Planning and Natural Resources Management. , Cham: Springer, 2025, pp. 73–94. doi: 10.1007/978-3-031-76859-0_5.
R. Azadnia, F. Noei-Khodabadi, A. Moloudzadeh, and A. Jahanbakhshi, “Medicinal and poisonous plants classification from visual characteristics of leaves using computer vision and deep neural networks,” Ecological Informatics, vol. 82, p. 102683, 2024, doi: 10.1016/j.ecoinf.2024.102683.
D. Vinod et al., “An Android App for Ayurvedic Herb Identification and Classification Using Machine Vision,” in Proceedings of the 1st International Conference on Trends in Engineering Systems and Technologies (ICTEST), Kochi, India, 2024, pp. 1–6. doi: 10.1109/ICTEST60614.2024.10576106.
A. Garcia-Perez, M. J. Gomez-Silva, and A. de la Escalera-Hueso, “A generative AI approach to improve in-situ vision tool wear monitoring with scarce data,” Journal of Intelligent Manufacturing, 2024, doi: 10.1007/s10845-024-02379-2.
X. Zhao, L. Wang, Y. Zhang, and et al, “A review of convolutional neural networks in computer vision,” Artificial Intelligence Review, vol. 57, p. 99, 2024, doi: 10.1007/s10462-024-10721-6.
G. Cicceri, S. Di Bella, S. Di Franco, G. Stassi, M. Todaro, and S. Vitabile, “Deep learning approaches for morphological classification of intestinal organoids,” IEEE Access, 2025, doi: 10.1109/ACCESS.2025.3558621.
L. Baloch, A. Sajid, C. Dewi, H. Christanto, and A. Zafar, “Deep Learning-Based Medical Image Reconstruction: Overview, Analysis, and Challenges,” Revue d’Intelligence Artificielle, vol. 38, pp. 417–429, Apr. 2024, doi: 10.18280/ria.380205.
T. H. Kim, R. Chinthaginjala, A. Srinivasulu, and others, “COVID-19 health data prediction: a critical evaluation of CNN-based approaches,” Scientific Reports, vol. 15, p. 9121, 2025, doi: 10.1038/s41598-025-92464-0.
K. Nawaz, A. Zanib, I. Shabir, and et al, “Skin cancer detection using dermoscopic images with convolutional neural network,” Scientific Reports, vol. 15, p. 7252, 2025, doi: 10.1038/s41598-025-91446-6.
R. Roslidar, Y. Yunida, and M. Jurej, “Improved lung sound classification model using combined residual attention network and Vision Transformer for limited dataset,” Indonesian Journal of Electrical Engineering and Informatics, vol. 12, no. 1, pp. 102–113, 2024.
H. Rabie and M. A. Akhloufi, “A review of machine learning and deep learning for Parkinson’s disease detection,” Discovery Artificial Intelligence, vol. 5, p. 24, 2025, doi: 10.1007/s44163-025-00241-9.
J. Lu, K. Wang, H. Ding, and et al, “MSCA-Sp R-CNN: a segmentation algorithm for pneumonia small lesions integrating multi-scale channel attention and sub-pixel upsampling,” Multimedia Systems, vol. 31, p. 149, 2025, doi: 10.1007/s00530-025-01726-4.
T. Burgert, M. Ravanbakhsh, and B. Demir, “On the Effects of Different Types of Label Noise in Multi-Label Remote Sensing Image Classification,” IEEE Transactions on Geoscience and Remote Sensing, vol. 60, p. 5413713, 2022.
A. Majeed and S. O. Hwang, “CTGAN-MOS: Conditional Generative Adversarial Network Based Minority-Class-Augmented Oversampling Scheme for Imbalanced Problems,” IEEE Access, vol. 11, pp. 37420–37435, 2023, doi: 10.1109/ACCESS.2023.3303509.
A. Mouri Zadeh Khaki and A. Choi, “Optimizing Deep Learning Acceleration on FPGA for Real-Time and Resource-Efficient Image Classification,” Applied Sciences, vol. 15, p. 422, 2025, doi: 10.3390/app15010422.
R. Sujatha, J. M. Chatterjee, N. Z. Jhanjhi, and S. N. Brohi, “Performance of Deep Learning vs Machine Learning in Plant Leaf Disease Detection,” Microprocessors and Microsystems, vol. 80, p. 103615, 2021, doi: 10.1016/j.micpro.2020.103615.
C. Zhou, Y. Xie, W. Wang, and Y. Zheng, “Machine Learning Driven Post-Impact Damage State Prediction for Performance-Based Crashworthiness Design of Bridge Piers,” Engineering Structures, vol. 292, p. 116539, 2023, doi: 10.1016/j.engstruct.2023.116539.
A. R. Palakayala and P. Kuppusamy, “A qualitative and quantitative approach using machine learning and non-motor symptoms for Parkinson’s disease classification: A hierarchical study,” Applied Computer Science, vol. 20, no. 3, pp. 171–191, 2024, doi: 10.35784/acs-2024-35.
C. Mo, K. Huang, H. Ji, W. Li, and K. Xu, “Efficient intelligent fault diagnosis method and graphical user interface development based on fusion of convolutional networks and vision transformers characteristics,” Scientific Reports, vol. 15, p. 7110, 2025, doi: 10.1038/s41598-025-88668-z.
R. Bandla, S. K. M. Hattaraki, J. S. Priyanka, and Y. C. Dumpali, “Plant Identification and Analysis of Medicinal Properties Using Image Processing and CNN with MobileNetV2 and AlexNet,” in IEEE INNOVA 2024 Conference, 2024. doi: 10.1109/INNOVA63080.2024.10847039.
P. B. Vikhe and B. L. Gunjal, “Capsicum Leaf Disease Detection Using Different Learning Techniques,” in Proceedings of the International Conference on IoT Based Control Networks and Intelligent Systems (ICICNIS-2024), IEEE, 2024. doi: 10.1109/ICICNIS64247.2024.10823333.
H. Tao, “Smoke Recognition in Satellite Imagery via an Attention Pyramid Network With Bidirectional Multilevel Multigranularity Feature Aggregation and Gated Fusion,” IEEE Internet of Things Journal, vol. 11, no. 8, pp. 5821–5837, 2024, doi: 10.1109/JIOT.2023.3339476.
S. Islam et al., “BDMediLeaves: A Leaf Images Dataset for Bangladeshi Medicinal Plants Identification,” Data in Brief, vol. 50, p. 109488, 2023, doi: 10.1016/j.dib.2023.109488.
H. M. Balaha, E. M. El-Gendy, and M. M. Saafan, “DMDRDF: Diabetes Mellitus and Retinopathy Detection Framework Using Artificial Intelligence and Feature Selection,” Soft Computing, vol. 28, pp. 11393–11420, 2024, doi: 10.1007/s00500-024-09873-y.
D. Herzalla, W. T. Lunardi, and M. Andreoni, “TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection,” IEEE Access, vol. 11, pp. 42562–42578, 2023, doi: 10.1109/ACCESS.2023.3319213.
M. Z. Degu and G. L. Simegn, “Smartphone-Based Detection and Classification of Poultry Diseases from Chicken Fecal Images Using Deep Learning Techniques,” Smart Agricultural Technology, vol. 3, pp. 51–63, 2023, doi: 10.1016/j.engappai.2024.109646.
Z. Zhang et al., “Identification of tea plant cultivars based on canopy images using deep learning methods,” Scientia Horticulturae, vol. 339, p. 113908, 2025, doi: 10.1016/j.scienta.2024.113908.
B. Dey and others, “Deep learning-based automatic classification of medicinal plants using leaf images: A comparative study of seven models,” Journal of Artificial Intelligence and Computational Science, vol. 15, no. 3, pp. 210–225, 2024, doi: 10.1016/j.heliyon.2023.e23655.
J. Mendes, J. Lima, L. Costa, N. Rodrigues, and A. I. Pereira, “Deep learning networks for olive cultivar identification: A comprehensive analysis of convolutional neural networks,” Smart Agricultural Technology, vol. 8, 2024, doi: 10.1016/j.atech.2024.100470.
S. Poornima, R. Divya, and R. S. Krishnan, “Advancing Rose Disease Diagnosis: A Deep Learning Framework with EfficientNet-B7,” in IEEE Conference on Intelligent Systems, 2024. doi: 10.1109/IS-2024.10714594.
E.-T. Baek, “Attention Score-Based Multi-Vision Transformer Technique for Plant Disease Classification,” Sensors, vol. 25, no. 1, p. 270, 2025, doi: 10.3390/s25010270.
Q. Wang, Y. Dong, N. Xu, F. Xu, C. Mou, and F. Chen, “Image Classification of Tree Species in Relatives Based on Dual-Branch Vision Transformer,” Forests, vol. 15, no. 12, p. 2243, 2024.
K. Ma et al., “Research on Soybean Seedling Stage Recognition Based on Swin Transformer,” Agronomy, vol. 14, no. 2614, 2024, doi: 10.3390/agronomy14112614.
Z. Liu et al., “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows,” in IEEE/CVF International Conference on Computer Vision (ICCV), 2021. doi: 10.1109/ICCV48922.2021.00986.
D. Li, C. Zhang, J. Li, M. Li, M. Huang, and Y. Tang, “MCCM: Multi-scale feature extraction network for disease classification and recognition of chili leaves,” Frontiers in Plant Science, vol. 15, p. 1367738, 2024, doi: 10.3389/fpls.2024.1367738.
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,” Journal of Industrial Information Integration, vol. 38, p. 100572, 2024, doi: 10.1016/j.jii.2024.100572.
H. El Akhal, A. Ben Yahya, and A. El Belrhiti El Alaoui, “Positive discrimination of minority classes through data generation and distribution: A case study in olive disease classification,” Engineering Applications of Artificial Intelligence, vol. 139, p. 109646, 2025, doi: 10.1016/j.engappai.2024.109646.
M. Rezaei, S. Gupta, D. Diepeveen, H. Laga, M. G. K. Jones, and F. Sohel, “Barley disease recognition using deep neural networks,” European Journal of Agronomy, vol. 161, p. 127359, 2024, doi: 10.1016/j.eja.2024.127359.
F. Nguleni, D. Nyambo, J. Lisuma, and S. Kaijage, “Dataset of Virginia Flue-cured Tobacco Leaf images based on stalk leaf position for classification tasks: A case of Tanzania,” Data in Brief, vol. 56, p. 110817, 2024, doi: 10.1016/j.dib.2024.110817.
E. Chamseddine, N. Mansouri, M. Soui, and M. Abed, “Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss,” Applied Soft Computing, vol. 129, p. 109588, 2022, doi: 10.1016/j.asoc.2022.109588.
A. F. A. Alshamrani and F. S. Z. Alshomrani, “Optimizing Breast Cancer Mammogram Classification Through a Dual Approach: A Deep Learning Framework Combining ResNet50, SMOTE, and Fully Connected Layers for Balanced and Imbalanced Data,” IEEE Access, vol. 13, pp. 4815–4826, 2025, doi: 10.1109/ACCESS.2024.3524633.
B. G. Deepa and S. Senthil, “Predicting invasive ductal carcinoma tissues in whole slide images of breast Cancer by using convolutional neural network model and multiple classifiers,” Multimedia Tools and Applications, vol. 81, pp. 8575–8596, 2022, doi: 10.1007/s11042-022-12114-9.
C. Shorten and T. M. Khoshgoftaar, “A survey on Image Data Augmentation for Deep Learning,” Journal of Big Data, vol. 6, p. 60, 2019, doi: 10.1186/s40537-019-0197-0.
M. A. Ilani, D. Shi, and Y. M. Banad, “T1-weighted MRI-based brain tumor classification using hybrid deep learning models,” Scientific Reports, vol. 15, p. 7010, 2025, doi: 10.1038/s41598-025-92020-w.
G. Imeraj and H. Iyatomi, “Waveshift Augmentation: A Physics-Driven Strategy in Fine-Grained Plant Disease Classification,” IEEE Access, vol. 13, pp. 31303–31317, 2025, doi: 10.1109/ACCESS.2025.3541780.
G. H. Dagnaw, M. El Mouhtadi, and M. Mustapha, “Skin cancer classification using vision transformers and explainable artificial intelligence,” Journal of Medical Artificial Intelligence, vol. 7, Jun. 2024.
M. Alsaidi, M. T. Jan, and A. Altaher, “Tackling the class imbalanced dermoscopic image classification using data augmentation and GAN,” Multimedia Tools and Applications, vol. 83, pp. 49121–49147, 2024, doi: 10.1007/s11042-023-17067-1.
D. R. Raman, S. Nishanthi, and P. Babysha, “Diagnosis of Diabetic Retinopathy by using EfficientNet-B7 CNN Architecture in Deep Learning,” in Proceedings of the 2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS), Coimbatore, India, 2023, pp. 430–435. doi: 10.1109/ICSCSS57650.2023.10169453.
P. Gupta, J. Nirmal, and N. Mehendale, “Custom CNN architectures for skin disease classification: binary and multi-class performance,” Multimedia Tools and Applications, 2024, doi: 10.1007/s11042-024-20503-5.
A. Alzahrani, “Digital Image Forensics: An Improved DenseNet Architecture for Forged Image Detection,” Engineering, Technology & Applied Science Research (ETASR), vol. 14, no. 2, pp. 13671–13680, 2024, doi: 10.48084/etasr.7029.
J. Zhou et al., “Intelligent classification of maize straw types from UAV remote sensing images using DenseNet201 deep transfer learning algorithm,” Ecological Indicators, 2024, doi: 10.1016/j.ecolind.2024.0788X.
C. González-Santoyo, D. Renza, and E. Moya-Albor, “Identifying and Mitigating Label Noise in Deep Learning for Image Classification,” Technologies, vol. 13, no. 4, p. 132, 2025, doi: 10.3390/technologies13040132.
S. S. Alam, A. Ahad, S. Ahmed, J. Dudley, and T. H. Farook, “Using deep learning to segment impacted molar teeth from panoramic radiographs,” Digital Dentistry Journal, vol. 1, 2025, [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2950643325000078
A. Kuppusamy, S. K. Sundaresan, and R. Cingaram, “Enhancing sugarcane leaf disease classification through a novel hybrid shifted-vision transformer approach: technical insights and methodological advancements,” Environmental Monitoring and Assessment, vol. 197, no. 37, 2025, [Online]. Available: https://link.springer.com/article/10.1007/s10661-024-13468-3
B. Zheng, J. Liu, Y. Li, Y. Li, and Z. Qin, “MAS-PD: Transferable Adversarial Attack against Vision-Transformers-Based SAR Image Classification Task,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, doi: 10.1109/10906523.
Q. Wu et al., “A classification method for soybean leaf diseases based on an improved ConvNeXt model,” Scientific Reports, vol. 13, p. 19141, 2023, doi: 10.1038/s41598-023-46492-3.
D. Reyes and J. Sánchez, “Performance of convolutional neural networks for the classification of brain tumors using magnetic resonance imaging,” Heliyon, vol. 10, no. 3, p. e25468, 2024, doi: 10.1016/j.heliyon.2024.e25468.
DOI: https://doi.org/10.47738/jads.v6i3.733
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