Classification of Wild Edible Plants Using InceptionV3 with Transfer Learning and Metadata Integration as a Decision Support System

Ridho Nur Fauzi; Julius Panda Putra Naibaho; Alex De Kweldju

doi:10.24014/ijaidm.v9i1.39091

Classification of Wild Edible Plants Using InceptionV3 with Transfer Learning and Metadata Integration as a Decision Support System

Ridho Nur Fauzi, Julius Panda Putra Naibaho, Alex De Kweldju

Abstract

Deep learning has advanced intelligent systems for plant identification; however, distinguishing edible wild plants remains challenging due to limited datasets and the need for contextual information beyond visual classification. This study develops a Convolutional Neural Network (CNN) framework that integrates metadata as a decision support system to enhance food safety and strengthen community-based food security. A dataset of 16,076 images across 34 classes of edible wild plants was collected and enriched with metadata containing plant descriptions, consumption status, and nutritional values. The dataset was split into 75% training, 20% validation, and 5% testing to ensure reliable evaluation. The proposed solution employs InceptionV3 with transfer learning as the primary model, chosen for its ability to capture complex visual features in limited datasets, while MobileNetV3-Large serves as a lightweight comparative architecture. Results show that InceptionV3 achieved superior performance with a test accuracy of 0.87 and F1-score of 0.88, whereas MobileNetV3-Large obtained only 0.03 accuracy, indicating poor generalization. This highlights the importance of selecting architectures with sufficient depth for domains characterized by high visual variability. Metadata integration enhanced the system’s role as a decision support tool, providing contextual information such as edibility status and nutritional content. The novelty of this research lies in combining CNN-based classification with metadata integration, transforming the system into a practical framework for safe consumption decisions. Limitations include the dataset containing only edible plants. Future work should incorporate non-edible classes, evaluate performance under real-world conditions, and explore advanced architectures and explainable AI techniques to improve robustness, transparency, and accessibility.

Keywords

Convolutional Neural Network; Decision Support System; Edible Wild Plants; Image Classification; Metadata Integration; Transfer Learning

References

J. Fanzo, L. Haddad, K. R. Schneider, C. Béné, N. Covic, et al., “A research vision for food systems in the 2020s: Defying the status quo,” Global Food Security, vol. 26, 2020, doi: https://doi.org/10.1016/j.gfs.2020.100397

FAO, IFAD, UNICEF, WFP, WHO, “The State of Food Security and Nutrition in the World 2023”, Rome: FAO, 2023. doi: https://doi.org/10.4060/cc3017en

K. Kartarina, N. Islamiah, D. Supatmiwati, M. Zulfiqri, B. K. Triwijoyo, and R. Amrullah, “Image Classification of Medicinal Plants Using Inception V3 and CNN: A Novel Implementation,” Int. J. Electron. Commun. Syst., vol. 5, no. 2, pp. 143–158, 2025, doi: https://doi.org/10.24042//ijecs.v5i2.27930

M. Sholihin, M. R. Zamroni, L. Anifah, M. F. Md Fudzee, and M. N. Ismail, “Fine-Tuned Transfer Learning with Inception V3 for Automated Detection of Grapevine Leaf Diseases,” J. Tek. Inform. (JUTIF), vol. 6, no. 5, pp. 3683–3696, Oct. 2025, doi: https://doi.org/10.52436/1.jutif.2025.6.5.4717

R. Faurina, S. Rahma, A. Vatresia, and A. Susanto, “Comparison of Convolutional Neural Networks Transfer Learning Models for Disease Classification of Food Crops,” JOIV: Int. J. Inform. Visualization, vol. 8, no. 4, pp. 2020–2032, Dec. 2024, doi: http://dx.doi.org/10.62527/joiv.8.4.1936

F. I. Tanesab, C. Y. Laatrehe, and M. A. Wuarlela, “Penerapan Metode CNN Dalam Mengklasifikasi Jenis Penyakit Pada Daun Mangga Menggunakan Arsitektur Inception V3,” J. Penelit. Nusantara, vol. 1, no. 8, pp. 265–275, Aug. 2025, doi: https://doi.org/10.59435/menulis.v1i8.583

A. J. Rozaqi, A. Sunyoto, and M. R. Arief, “Implementasi Transfer Learning pada Algoritma Convolutional Neural Network Untuk Identifikasi Penyakit Daun Kentang,” Proc. Eng. Life Sci., vol. 1, no. 1, pp. 1–7, Mar. 2021, doi: https://doi.org/10.21070/pels.v1i1.820.

S. R. Shah, S. Qadri, H. Bibi, S. M. W. Shah, M. I. Sharif, and F. Marinello, “Comparing Inception V3, VGG 16, VGG 19, CNN, and ResNet 50: A Case Study on Early Detection of a Rice Disease,” Agronomy, vol. 13, no. 1633, pp. 1–13, Jun. 2023, doi: https://doi.org/10.3390/agronomy13061633

V. Maeda-Gutiérrez, J. J. Oropeza-Valdez, L. C. Reveles-Gómez, C. Padron-Manrique, O. Resendis-Antonio, L. O. Solís-Sanchez, H. A. Guerrero-Osuna, and C. A. Olvera, “AI-Driven Plant Health Assessment: A Comparative Analysis of Inception V3, ResNet-50 and ViT with SHAP for Accurate Disease Identification in Taro,” Agronomy, vol. 15, no. 77, pp. 1–18, Dec. 2024, doi: https://doi.org/10.3390/agronomy15010077

S. Aboelenin, F. A. Elbasheer, M. M. Eltoukhy, W. M. El-Hady, and K. M. Hosny, “A hybrid Framework for plant leaf disease detection and classification using convolutional neural networks and vision transformer,” Complex & Intelligent Systems, vol. 11, p. 142, Jan. 2025, doi: https://doi.org/10.1007/s40747-024-01764-x

B. Hu, W. Jiang, J. Zeng, C. Cheng, and L. He, “FOTCA: hybrid transformer-CNN architecture using AFNO for accurate plant leaf disease image recognition,” Front. Plant Sci., vol. 14, Sep. 2023, doi: https://doi.org/10.3389/fpls.2023.1231903

K. A. Sahib, B. K. Oleiwi, and A. R. Nasser, “Medicinal Plants Recognition Using Deep Transfer Learning Models,” Int. J. Design & Nature and Ecodynamics, vol. 19, no. 5, pp. 1501–1510, Oct. 2024, doi: https://doi.org/10.18280/ijdne.190504.

N. S. Bhelkar and A. Sharma, “Identification and classification of medicinal plants using leaf with deep convolutional neural networks,” Int. J. Health Sci., vol. 6, no. S6, pp. 11596–11605, 2022, doi: https://doi.org/10.53730/ijhs.v6nS6.13233.

S. Faisal, K. Javed, S. Ali, A. Alasiry, M. Marzougui, M. A. Khan, and J.-H. Cha, “Deep Transfer Learning Based Detection and Classification of Citrus Plant Diseases,” Computers, Materials & Continua, vol. 76, no. 1, pp. 896–906, Jun. 2023, doi: https://doi.org/10.32604/cmc.2023.039781.

B. Bharadwaj, A. Mishra, and S. Bharadwaj, “Transfer Learning-Based CNN Models for Plant Species Identification Using Leaf Venation Patterns,” arXiv preprint arXiv:2509.03729, Sep. 2025, doi: https://doi.org/10.48550/arXiv.2509.03729

D. A. Pusparani, M. W. A. Kesiman, and K. Y. E. Aryanto, “Identification of Little Tuna Species Using Convolutional Neural Networks (CNN) Method and ResNet-50 Architecture,” Indonesian Journal of Artificial Intelligence and Data Mining (IJAIDM), vol. 8, no. 1, pp. 86–93, Mar. 2025, doi: https://doi.org/10.24014/ijaidm.v8i1.31620.

A. Dosovitskiy, et al., “An image is worth 16x16 words: Transformers for image recognition at scale,” arXiv preprint arXiv:2010.11929, 2020, doi: https://doi.org/10.48550/arXiv.2010.11929.

I. Suhardin, A. Patombongi, and I. A. Muhammad, “Mengidentifikasi Jenis Tanaman Berdasarkan Citra Daun Menggunakan Algoritma Convolutional Neural Network,” vol. 6, no. 2, 2021, doi: https://doi.org/10.51876/simtek.v6i2.101.

B. Falahkhi, E. F. Achmal, M. Rizaldi, R. Rizki, and N. Yudistira, “Perbandingan Model AlexNet dan ResNet dalam Klasifikasi Citra Bunga Memanfaatkan Transfer Learning,” Ilmu Komputer Agri-Informatika, vol. 9, pp. 70–78, 2022, doi: https://doi.org/10.29244/jika.9.1.70-78

R. Wightman, H. Touvron, and H. Jégou, “ResNet strikes back: An improved training procedure in timm,” arXiv preprint arXiv:2110.00476, 2021, doi: https://doi.org/10.48550/arXiv.2110.00476.

T. Abdullah, K. S. Saputra, H. Syahputra, Z. Indra, and D. Kartika, “Comparative Analysis of Model Architectures using Transfer Learning Approach in Convolutional Neural Networks for Traditional Ulos Fabric Classification,” Journal of Artificial Intelligence and Engineering Applications, vol. 4, no. 2, pp. 629–634, Feb. 2025, doi: https://doi.org/10.59934/jaiea.v4i2.719

F. K. N. Nadiyah, N. N. Alifah, S. Nurdiati, E. Khatizah, and M. K. Najib, “Performance Comparison of VGG16, MobileNetV2, and InceptionV3 Convolutional Neural Networks in Classifying Facial Dermatological Conditions,” Jambura Journal of Mathematics, vol. 7, no. 2, pp. 144–150, Aug. 2025, doi: https://doi.org/10.37905/jjom.v7i2.33082.

M. K. Senapaty, A. Ray, and N. Padhy, “A Decision Support System for Crop Recommendation Using Machine Learning Classification Algorithms,” Agriculture, vol. 14, no. 8, Aug. 2024, doi: https://doi.org/10.3390/agriculture14081256.

F. A. Cahyanti, R. D. Eskundari, and A. Purwanto, "Wild Edible Plants as an Alternative Food Source for The Community of Pakis Baru Village, Nawangan, Pacitan," Jurnal Biologi Tropis, vol. 24, no. 2, pp. 352–367, May 2024, doi: https://doi.org/10.29303/jbt.v24i2.6779.

A. R. Dianto, F. T. Anggraeny, and H. Maulana, “Analisis Efektivitas Algoritma MobileNetV3-Large dan EfficientNet-B0 untuk Klasifikasi Citra Penyakit Daun Jeruk,” JITET (Jurnal Informatika dan Teknik Elektro Terapan), vol. 13, no. 3, pp. 697–699, 2024, doi: https://doi.org/10.23960/jitet.v13i3.6956.

N. Huda, A. Mahiruna, W. Sulistijanti, and R. C. N. Santi, “Analisis Performa InceptionV3 Convolutional Network pada Klasifikasi Varietas Daun Grapevine,” Jurnal Sains Komputer dan Teknologi Informasi, vol. 5, no. 2, pp. 48–54, May 2023, doi: https://doi.org/10.33084/jsakti.v5i2.5022

DOI: http://dx.doi.org/10.24014/ijaidm.v9i1.39091

Refbacks

There are currently no refbacks.

Office and Secretariat:

Big Data Research Centre
Puzzle Research Data Technology (Predatech)
Laboratory Building 1st Floor of Faculty of Science and Technology
UIN Sultan Syarif Kasim Riau

Jl. HR. Soebrantas KM. 18.5 No. 155 Pekanbaru Riau – 28293
Website: http://predatech.uin-suska.ac.id/ijaidm
Email: ijaidm@uin-suska.ac.id
e-Journal: http://ejournal.uin-suska.ac.id/index.php/ijaidm
Phone: 085275359942

Journal Indexing:

IJAIDM Stats