https://doi.org/10.4081/jae.2024.1595
Residual attention based multi-label learning for apple leaf disease identification
Published: 20 August 2024
Abstract Views: 130
PDF: 108
HTML: 0
HTML: 0
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Authors
Recent studies suggest that plant disease identification via machine learning approach is vital for preventing the spread of diseases. Identifying multiple diseases simultaneous on a single leaf is one of the most irritating issues in agricultural production. However, the existing approaches are difficult to meet the requirements of production practice in accuracy or interpretability. Here, we present residual attention based multi-label learning framework (RAMDI), a method for predicting apple leaf diseases in natural environment. Built upon an attention based multi-label learning framework, the channel and spatial attention mechanisms are investigated and embedded in residual network for multi-label disease prediction, which takes advantage of channel-wise and spatial-wise attention weights. Experimental results indicate that the RAMDI achieves 0.976 accuracy, 0.986 F-score, and 0.979 mAPs, outperforms the existing state-of-the-art apple leaf disease identification models. RAMDI not only predicts multi-disease on a single leaf simultaneously, but also reveals the interpretability among positive predictions that contribute most to identify the key features that are significant for the leaf diseases. This method achieves the following two achievements. Firstly, it provides a solution for detecting multiple diseases on a single leaf. Secondly, this approach gains an interpretable understanding for apple leaf disease identification.
Agarwal, M., Kaliyar, R., Singal, G., Gupta, S., 2019. FCNN-LDA: A Faster Convolution Neural Network model for Leaf Disease identification on Apple's leaf dataset. 12th International Conference on Information & Communication Technology and System (ICTS), 246-251, https://doi.org/10.1109/ICTS.2019.8850964.
DOI: https://doi.org/10.1109/ICTS.2019.8850964
Alice W., 2021. World apple, grape, and pear production forecast to rise in 2021/22. https://www.mintecglobal.com/top-stories/world-apple-grape-and-pear-production-forecast-to-rise-in-2021/22.
Ayyub, S., Manjramkar, A., 2019. Fruit Disease Classification and Identification using Image Processing. 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC), Erode, India, 2019, 754-758, https://doi.org/10.1109/ICCMC.2019.8819789.
DOI: https://doi.org/10.1109/ICCMC.2019.8819789
Chakraborty, S., Paul, S., Rahat-uz-Zaman, M., 2021. Prediction of Apple Leaf Diseases Using Multiclass Support Vector Machine. 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), 147-151, https://10.1109/ICREST51555.2021.9331132.
DOI: https://doi.org/10.1109/ICREST51555.2021.9331132
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N., 2021. An image is worth 16x16 words: Transformers for image recognition at scale. The Ninth International Conference on Learning Representations (LCLR).
Gao, Z., Xie J., Wang Q., Li, P., 2019. Global Second-Order Pooling Convolutional Networks. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 3019-3028. https://doi.org/10.1109/CVPR.2019.00314.
DOI: https://doi.org/10.1109/CVPR.2019.00314
Hu, J., Shen, L., Albanie, S., Sun, G., Wu, E., 2020. Squeeze-and-Excitation Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 8, 2011-2023. https://doi.org/10.1109/TPAMI.2019.2913372.
DOI: https://doi.org/10.1109/TPAMI.2019.2913372
Huang, R., Wu, Z., 2021.Multi-label feature selection via manifold regularization and dependence maximization, Pattern Recognition,120. https://doi.org/10.1016/j.patcog.2021.108149.
DOI: https://doi.org/10.1016/j.patcog.2021.108149
James, G., Sujatha,S., 2021.Categorising Apple Fruit Diseases Employing Hybrid Neural Clustering Classifier. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.12.139.
DOI: https://doi.org/10.1016/j.matpr.2020.12.139
Lee, C., Fang, W., Yeh, C., Wang, Y., 2018. Multi-label Zero-Shot Learning with Structured Knowledge Graphs, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 1576-1585, https://doi.org/10.1109/CVPR.2018.00170.
DOI: https://doi.org/10.1109/CVPR.2018.00170
Lee, H., Kim, H., Nam, H., 2019. Srm: A style-based recalibration module for convolutional neural networks. Preprint at: https://arxiv.org/abs/ 1903.10829.
DOI: https://doi.org/10.1109/ICCV.2019.00194
Li, C., Peng, J., Zhang, S., 2016. Apple leaf disease identification method based on feature fusion and local discriminant mapping. Guangdong Agric. Sci. 43 (10), 134–139. https://doi.org/10.16768/j.issn.1004-874X.2016.10.024.
Li, L., Zhang, S., Wang, B., 2022. Apple Leaf Disease Identification with a Small and Imbalanced Dataset Based on Lightweight Convolutional Networks. SENSORS, 22(1), https://doi.org/10.3390/s22010173.
DOI: https://doi.org/10.3390/s22010173
Liu, Z., Lin, Y., Cao, Y., Hu, H., Zhang, Z., Lin, S., Guo, B., Swin Transformer: Hierarchical Vision Transformer using Shifted Windows, 2021. [Online], https://arxiv.org/abs/2103.14030.
DOI: https://doi.org/10.1109/ICCV48922.2021.00986
Pandiyan, S., Ashwin, M., Manikandan, R., Karthick, R, Anantah, R., 2020. Heterogeneous Internet of Things organization predictive analysis platform for apple leaf diseases recognition. Computer Communications, 154(12), 99-110, https://doi.org/10.1016/j.comcom.2020.02.054.
DOI: https://doi.org/10.1016/j.comcom.2020.02.054
Qin, Z., Zhang, P., Wu, F., Li, X., 2021. Fcanet: Frequency channel attention networks. Preprint at: https://arxiv.org/abs/ 2012.11879.
DOI: https://doi.org/10.1109/ICCV48922.2021.00082
Ramachandran, P., Parmar, N., Vaswani, A., Bello, I., Levskaya, A., Shlens, J., 2019. Stand-alone self-attention in vision models. Advances in Neural Information Processing Systems, 32. https://proceedings.neurips.cc/paper/2019/hash/3416a75f4cea9109507cacd8e2f2aefc-Abstract.html.
Shi Y., Huang, W., Zhang, S., 2017. Apple disease recognition based on two-dimensionality subspace learning. Comput. Eng. Appl. 53 (22), 180–184. ISSN 1002-8331, https://doi.org/10.3778/j.issn.1002-8331.1605-0073.
Sun, H., Xu, H., Liu, B., He, D., He, J., Zhang, H., Geng, N., 2021. MEAN-SSD: A novel real-time detector for apple leaf diseases using improved light-weight convolutional neural networks, Computers and Electronics in Agriculture. 189, https://doi.org/10.1016/j.compag.2021.106379.
DOI: https://doi.org/10.1016/j.compag.2021.106379
Thapa, R., Zhang, K., Snavely, N., Belongie, S., Belongie, S., Khan, A., 2020. The Plant Pathology Challenge 2020 data set to classify foliar disease of apples, Applications in Plant Sciences, 8,9. https://doi.org/10.1002/aps3.11390.
DOI: https://doi.org/10.1002/aps3.11390
Wang, P., Niu, T., Mao, YR., Zhang, Z., Liu, B., He, DJ., 2021. Identification of Apple Leaf Diseases by Improved Deep Convolutional Neural Networks with an Attention Mechanism. Frontiers in Plant Science, 12, https://doi.org/10.3389/fpls.2021.723294.
DOI: https://doi.org/10.3389/fpls.2021.723294
Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q., 2020. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 11531-11539. https://doi.org/10.1109/CVPR42600.2020.01155.
DOI: https://doi.org/10.1109/CVPR42600.2020.01155
Wang, X., Girshick, R., Gupta A., He, K., 2018. Non-local Neural Networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7794-7803, https://doi.org/10.1109/CVPR.2018.00813.
DOI: https://doi.org/10.1109/CVPR.2018.00813
Woo, S., Park, J., Lee, Y., Kweon, I., CBAM: Convolutional Block Attention Module, 2018, [online], http://arxiv.org/abs/1807.06521.
DOI: https://doi.org/10.1007/978-3-030-01234-2_1
Yang, B., Bender, G., Le, Q. V., & Ngiam, J.,2019. Condconv: Conditionally parameterized convolutions for efficient inference. Advances in Neural Information Processing Systems, 32.
Yang, Z., Zhu, L., Wu, Y., Yang, Y., 2020. Gated channel transformation for visual recognition. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 11 794–11 803. https://doi.org/10.1109/CVPR42600.2020.01181.
DOI: https://doi.org/10.1109/CVPR42600.2020.01181
Yu, H., Son, C., 2020. Leaf Spot Attention Network for Apple Leaf Disease Identification. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 229-237, https://doi.org/10.1109/CVPRW50498.2020.00034.
DOI: https://doi.org/10.1109/CVPRW50498.2020.00034
Zhong, Y., Zhao, M., 2020. Research on deep learning in apple leaf disease recognition, Computers and Electronics in Agriculture,168, https://doi.org/10.1016/j.compag.2019.105146.
DOI: https://doi.org/10.1016/j.compag.2019.105146
Zhou, C., Zhou, S., Xing, J., Song, J., 2021. Tomato Leaf Disease Identification by Restructured Deep Residual Dense Network. IEEE Access, vol. 9, pp. 28822-28831, https://doi.org/10.1109/ACCESS.2021.3058947.
DOI: https://doi.org/10.1109/ACCESS.2021.3058947
How to Cite
Zhou, C. (2024) “Residual attention based multi-label learning for apple leaf disease identification”, Journal of Agricultural Engineering, 55(4). doi: 10.4081/jae.2024.1595.
Copyright (c) 2024 the Author(s)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
PAGEPress has chosen to apply the Creative Commons Attribution NonCommercial 4.0 International License (CC BY-NC 4.0) to all manuscripts to be published.
Similar Articles
- Fuhao Zhu, Jin Chen, Zhuohuai Guan, Yahui Zhu, Hao Shi, Kai Cheng, Development of a combined harvester navigation control system based on visual simultaneous localization and mapping-inertial guidance fusion , Journal of Agricultural Engineering: Vol. 55 No. 3 (2024)
- José F. Reyes, Elías Contreras, Christian Correa, Pedro Melin, Image analysis of real-time classification of cherry fruit from colour features , Journal of Agricultural Engineering: Vol. 52 No. 4 (2021)
- Roberto Romaniello, Alessandro Leone, Giorgio Peri, Measurement of food colour in L*a*b* units from RGB digital image using least squares support vector machine regression , Journal of Agricultural Engineering: Vol. 46 No. 4 (2015)
- Samuel Kojo Ahorsu, Hayford Ofori, Jonathan Ampah, Ernest Kodzo Kumah, Maxwell Budu, Effect of variable chipping clearance and operational speed on the cassava chip geometry , Journal of Agricultural Engineering: Vol. 53 No. 2 (2022)
- Alina Evelyn Badillo-Márquez, Jonathan J. Cid-Galiot, Rubén Posada-Gómez, Alberto Alfonso Aguilar-Lasserre, Automated system for the detection of risk in agricultural sugarcane harvesting using digital image processing and deep learning , Journal of Agricultural Engineering: Vol. 55 No. 3 (2024)
- Alessandro Parenti, Piernicola Masella, Lorenzo Guerrini, Antonio Guiso, Paolo Spugnoli, Energetic and economic viability of olive stone recovery as a renewable energy source: a Southern Italy case study , Journal of Agricultural Engineering: Vol. 45 No. 2 (2014)
- Senanur Durgut Malçok, Azime Özkan Karabacak, Ertürk Bekar, Cüneyt Tunçkal, Canan Ece Tamer, Influence of a hybrid drying combined with infrared and heat pump dryer on drying characteristics, colour, thermal imaging and bioaccessibility of phenolics and antioxidant capacity of mushroom slices , Journal of Agricultural Engineering: Vol. 54 No. 3 (2023)
- Stanimir Stoilov, Salvatore F. Papandrea, Georgi Angelov, Delyan Oslekov, Giuseppe Zimbalatti, Andrea R. Proto, Productivity analysis and costs of wheel cable skidder during salvage logging in European beech stand , Journal of Agricultural Engineering: Vol. 54 No. 2 (2023)
- Enrica Santolini, Marco Bovo, Alberto Barbaresi, Daniele Torreggiani, Patrizia Tassinari, Evaluation of microclimate in dairy farms using different model typologies in computational fluid dynamics analyses , Journal of Agricultural Engineering: Vol. 55 No. 3 (2024)
- Ildar Gabitov, Samat Insafuddinov, Denis Kharisov, Elmir Gaysin, Timur Farhutdinov, Diagnostic method and device for evaluating and forecasting the technical condition of farm machinery in operation , Journal of Agricultural Engineering: Vol. 52 No. 4 (2021)
<< < 1 2 3 4 5 6 7 8 9 10 > >>
You may also start an advanced similarity search for this article.
