Model Accuracy Analysis: Comparing Weed Detection in Soybean Crops with EfficientNet-B0, B1, and B2
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Dec 30, 2023
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EfficientNet B0, EfficientNet B1, EfficientNet B2 Models, Soybean Crops, Precision, Recall
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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%.
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Hindarto, D. (2023). Model Accuracy Analysis: Comparing Weed Detection in Soybean Crops with EfficientNet-B0, B1, and B2. Jurnal JTIK (Jurnal Teknologi Informasi Dan Komunikasi), 7(4), 734–744. https://doi.org/10.35870/jtik.v7i4.1825
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Computer & Communication Science
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
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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.
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.