Published: 2023-12-30
Model Accuracy Analysis: Comparing Weed Detection in Soybean Crops with EfficientNet-B0, B1, and B2
DOI: 10.35870/jtik.v7i4.1825
Downloads
Abstract
Comparison of EfficientNet models B0, B1, and B2 for soybean weed detection. This study examines how well these models distinguish weeds from soybeans. Precision, recall, and F1 score metrics evaluate each model through extensive testing and experiments. The method trains these models on datasets with images of soybean fields overrun by various weeds. Results show subtle differences in model accuracy, showing what they're good at and what they can't do to find soybean weeds. EfficientNet-B2 detects and classifies weeds better than B1 and B0 in soybean fields. It could improve weed management systems' accuracy and reliability. This comparison helps us choose the best weed-finding model to improve soybean farming and prevent crop yield losses. Training accuracy measurements on EfficientNet models B0 = 100%, B1 = 100%, and B2 = 100%.
Keywords
EfficientNet B0 ; EfficientNet B1 ; EfficientNet B2 Models ; Soybean Crops ; Precision ; Recall
Article Information
This article has been peer-reviewed and published in the Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi). The content is available under the terms of the Creative Commons Attribution 4.0 International License.
-
Issue: Vol. 7 No. 4 (2023)
-
Section: Computer & Communication Science
-
Published: %750 %e, %2023
-
License: CC BY 4.0
-
Copyright: © 2023 Authors
-
DOI: 10.35870/jtik.v7i4.1825
AI Research Hub
This article is indexed and available through various AI-powered research tools and citation platforms. Our AI Research Hub ensures that scholarly work is discoverable, accessible, and easily integrated into the global research ecosystem. By leveraging artificial intelligence for indexing, recommendation, and citation analysis, we enhance the visibility and impact of published research.
Djarot Hindarto
Informatics Study Program, Faculty of Communication and Information Technology, Universitas Nasional, South Jakarta City, Special Capital Region of Jakarta, Indonesia.
-
Rafay, A. and Hussain, W., 2023. EfficientSkinDis: An EfficientNet-based classification model for a large manually curated dataset of 31 skin diseases. Biomedical Signal Processing and Control, 85, p.104869. DOI: https://doi.org/10.1016/j.bspc.2023.104869.
-
Yadav, P., Menon, N., Ravi, V., Vishvanathan, S. and Pham, T.D., 2022. EfficientNet convolutional neural networks-based Android malware detection. Computers & Security, 115, p.102622. DOI: https://doi.org/10.1016/j.cose.2022.102622.
-
Gessert, N., Nielsen, M., Shaikh, M., Werner, R. and Schlaefer, A., 2020. Skin lesion classification using ensembles of multi-resolution EfficientNets with meta data. MethodsX, 7, p.100864. DOI: https://doi.org/10.1016/j.mex.2020.100864.
-
Raza, R., Zulfiqar, F., Khan, M.O., Arif, M., Alvi, A., Iftikhar, M.A. and Alam, T., 2023. Lung-EffNet: Lung cancer classification using EfficientNet from CT-scan images. Engineering Applications of Artificial Intelligence, 126, p.106902. DOI: https://doi.org/10.1016/j.engappai.2023.106902.
-
Yang, L., Yu, H., Cheng, Y., Mei, S., Duan, Y., Li, D. and Chen, Y., 2021. A dual attention network based on efficientNet-B2 for short-term fish school feeding behavior analysis in aquaculture. Computers and Electronics in Agriculture, 187, p.106316. DOI: https://doi.org/10.1016/j.compag.2021.106316.
-
Ali, K., Shaikh, Z.A., Khan, A.A. and Laghari, A.A., 2022. Multiclass skin cancer classification using EfficientNets–a first step towards preventing skin cancer. Neuroscience Informatics, 2(4), p.100034. DOI: https://doi.org/10.1016/j.neuri.2021.100034.
-
Atila, Ü., Uçar, M., Akyol, K. and Uçar, E., 2021. Plant leaf disease classification using EfficientNet deep learning model. Ecological Informatics, 61, p.101182. DOI: https://doi.org/10.1016/j.ecoinf.2020.101182.
-
Hindarto, D., 2023. Enhancing Road Safety with Convolutional Neural Network Traffic Sign Classification. Sinkron: jurnal dan penelitian teknik informatika, 8(4), pp.2810-2818. DOI: https://doi.org/10.33395/sinkron.v8i4.13124.
-
Hindarto, D., 2023. A comparative study of sentiment classification: traditional nlp vs. Neural network approaches. Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM), 8(2), pp.49-60. DOI: https://doi.org/10.20527/jtiulm.v8i2.178.
-
Nigam, S., Jain, R., Marwaha, S., Arora, A., Haque, M.A., Dheeraj, A. and Singh, V.K., 2023. Deep transfer learning model for disease identification in wheat crop. Ecological Informatics, 75, p.102068. DOI: https://doi.org/10.1016/j.ecoinf.2023.102068.
-
Hindarto, D., 2023. Comparison of Detection with Transfer Learning Architecture RestNet18, RestNet50, RestNet101 on Corn Leaf Disease. Jurnal Teknologi Informasi Universitas Lambung Mangkurat (JTIULM), 8(2), pp.41-48. DOI: https://doi.org/10.20527/jtiulm.v8i2.174.
-
Sze, E., Santoso, H. and Hindarto, D., 2022. Review Star Hotels Using Convolutional Neural Network. Sinkron: jurnal dan penelitian teknik informatika, 7(4), pp.2469-2477. DOI: https://doi.org/10.33395/sinkron.v7i4.11836
-
Iswahyudi, I., Hindarto, D. and Santoso, H., 2023. PyTorch Deep Learning for Food Image Classification with Food Dataset. Sinkron: jurnal dan penelitian teknik informatika, 8(4), pp.2651-2661. DOI: https://doi.org/10.33395/sinkron.v8i4.12987
-
Saputra, A.D., Hindarto, D. and Santoso, H., 2023. Disease Classification on Rice Leaves using DenseNet121, DenseNet169, DenseNet201. Sinkron: jurnal dan penelitian teknik informatika, 8(1), pp.48-55. DOI: https://doi.org/10.33395/sinkron.v8i1.11906
-
Suherman, E., Hindarto, D., Makmur, A. and Santoso, H., 2023. Comparison of Convolutional Neural Network and Artificial Neural Network for Rice Detection. Sinkron: jurnal dan penelitian teknik informatika, 8(1), pp.247-255. DOI: https://doi.org/10.33395/sinkron.v8i1.11944
-
Zulfiqar, F., Bajwa, U.I. and Mehmood, Y., 2023. Multi-class classification of brain tumor types from MR images using EfficientNets. Biomedical Signal Processing and Control, 84, p.104777. DOI: https://doi.org/10.1016/j.bspc.2023.104777.
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright Retention and Open Access License
Authors retain copyright of their work and grant the journal non-exclusive right of first publication under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
This license allows unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
2. Rights Granted Under CC BY 4.0
Under this license, readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, including commercial use
- No additional restrictions — the licensor cannot revoke these freedoms as long as license terms are followed
3. Attribution Requirements
All uses must include:
- Proper citation of the original work
- Link to the Creative Commons license
- Indication if changes were made to the original work
- No suggestion that the licensor endorses the user or their use
4. Additional Distribution Rights
Authors may:
- Deposit the published version in institutional repositories
- Share through academic social networks
- Include in books, monographs, or other publications
- Post on personal or institutional websites
Requirement: All additional distributions must maintain the CC BY 4.0 license and proper attribution.
5. Self-Archiving and Pre-Print Sharing
Authors are encouraged to:
- Share pre-prints and post-prints online
- Deposit in subject-specific repositories (e.g., arXiv, bioRxiv)
- Engage in scholarly communication throughout the publication process
6. Open Access Commitment
This journal provides immediate open access to all content, supporting the global exchange of knowledge without financial, legal, or technical barriers.