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: 98
PDF: 84
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
- Marzia Quattrone, Giovanna Tomaselli, Lara Riguccio, Patrizia Russo, Assessment of the territorial suitability for the creation of the greenways networks: Methodological application in the Sicilian landscape context , Journal of Agricultural Engineering: Vol. 48 No. 4 (2017)
- Pietro Catania, Mariangela Vallone, Felice Pipitone, Gioacchino Francesco Argento, Giuseppe Spartà , Vito Armando Laudicina, Soil management effect on soil penetration resistance in the vineyard , Journal of Agricultural Engineering: Vol. 44 No. s2 (2013): Proceedings of the 10th Conference of the Italian Society of Agricultural Engineering
- Daniele Torreggiani, Alberto Barbaresi, Francesca Dallacasa, Patrizia Tassinari, Effects of different architectural solutions on the thermal behaviour in an unconditioned rural building. The case of an Italian winery , Journal of Agricultural Engineering: Vol. 49 No. 1 (2018)
- Francesco Maria Tangorra, Stefania Leonardi, Valerio Bronzo, Nicola Rota, Paolo Moroni, Pre-milking mechanical teat stimulation and milking performance of dairy buffaloes in early lactation , Journal of Agricultural Engineering: Vol. 48 No. 1 (2017)
- Zhi He, Xinting Ding, Wei Hao, Kai Li, Weixin Gong, Zixu Li, Yongjie Cui, Calibration and experiments of discrete element flexible model parameters for kiwifruit stalk , Journal of Agricultural Engineering: Vol. 55 No. 4 (2024)
- Yubing Sun, Yutong Zheng, Application of MOS gas sensors for detecting mechanical damage of tea plants , Journal of Agricultural Engineering: Vol. 55 No. 4 (2024)
- Alessandro Benelli, Chiara Cevoli, Angelo Fabbri, Luigi Ragni, Hyperspectral imaging to measure apricot attributes during storage , Journal of Agricultural Engineering: Vol. 53 No. 2 (2022)
- Yiming Xiao, Jianhua Wang, Hongyi Xiong, Fangjun Xiao, Renhuan Huang, Licong Hong, Bofei Wu, Jinfeng Zhou, Yongbin Long, Yubin Lan, Lychee cultivar fine-grained image classification method based on improved ResNet-34 residual network , Journal of Agricultural Engineering: Vol. 55 No. 3 (2024)
- Chiara Cevoli, Angelo Fabbri, Simone Virginio Marai, Enrico Ferrari, Adriano Guarnieri, Estimation of thermal conductivity of short pastry biscuit at different baking stages , Journal of Agricultural Engineering: Vol. 45 No. 2 (2014)
- Francesca Piazzolla, Maria Luisa Amodio, Giancarlo Colelli, The use of hyperspectral imaging in the visible and near infrared region to discriminate between table grapes harvested at different times , Journal of Agricultural Engineering: Vol. 44 No. 2 (2013)
<< < 5 6 7 8 9 10 11 12 13 14 > >>
You may also start an advanced similarity search for this article.
