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AWUNet: leaf area segmentation based on attention gate and wavelet pooling mechanism

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A Correction to this article was published on 28 February 2023

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Abstract

Accurate segmentation and detection of leaf area is of great significance for automatic plant growth monitoring and is one of the most effective strategies to monitor the sustainability of plant growth, yield, width, and height of plants for agricultural production. The leaf segmentation in carrot plants is a challenging task because of secondary leaflets, which introduces complex variability in leaf shape. In this paper, a novel AWUNet (Attention-gated Wavelet pooled UNet) integrating the concepts of wavelet pooling to reduce the size of the feature map and attention gate module which focuses the semantic content in the feature map is proposed. The skip connections in existing UNet (U-shaped network) model are remodeled using attention gate mechanism for saliency improvement, and pooling layers utilize wavelet function for compression. The effectiveness of hyper parameters is investigated to enhance the proposed AWUNet model’s accuracy. The proposed model was evaluated and compared with existing methods such as UNet +  + , Inception UNet, SAUNet, and INCSA UNet, as well as pre-trained deep learning architectures like Visual Geometry Group models such as VGG16-UNet, VGG19-UNet, and residual neural network (ResNet) UNet. The highest segmentation IoU (intersection of union) score of 94.81% was achieved by the proposed AWUNet model, whereas the state-of-the-art methods UNet, UNet +  + , Inception UNet, SAUNet, INCSA UNet,VGG16 UNet,VGG19 UNet, and ResNet UNet achieved IoU score of 86.29%, 63.37%, 45.26%, 72.39%, 89.30%, 82.99%, 85.16%, and 75.50%, respectively. The findings showed that the proposed AWUNet model outperforms than other models in segmenting leaf area in plants and can be implemented in the agriculture sector for crop monitoring.

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Data availability

The datasets used in the study are publically available in the repository: https://github.com/cwfid/dataset

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References

  1. Pieruschka, R., Schurr, U.: Plant phenotyping: past, present, and future. Plant Phenomics. (2019). https://doi.org/10.1155/2019/7507131

    Article  Google Scholar 

  2. Singh, V., Misra, A.K.: Detection of plant leaf diseases using image segmentation and soft computing techniques. Inf. Process. Agricult. (2017). https://doi.org/10.1016/j.inpa.2016.10.005

    Article  Google Scholar 

  3. Minervini, M., Giuffrida, M.V., Perata, P., Tsaftaris, S.A.: Phenotiki: an open software and hardware platform for affordable and easy image-based phenotyping of rosette-shaped plants. Plant J. (2017). https://doi.org/10.1111/tpj.13472

    Article  Google Scholar 

  4. Castillo-Martínez, M., Funes, F., Carvajal-Gámez, B., Urriolagoitia-Sosa, G., Rosales, A.: Color index based thresholding method for background and foreground segmentation of plant images. Comput. Electron. Agric. (2020). https://doi.org/10.1016/j.compag.2020.105783

    Article  Google Scholar 

  5. Sengar, N., Dutta, M., Travieso, C.: Computer vision based technique for identification and quantification of powdery mildew disease in cherry leaves. Computing (2018). https://doi.org/10.5555/3288338.3288342

    Article  MathSciNet  Google Scholar 

  6. Riehle, D., Reiser, D., Griepentrog, H.W.: Robust index-based semantic plant/background segmentation for RGB- images. Comput. Electron. Agric. 169, 1–12 (2020). https://doi.org/10.1016/j.compag.2019.105201

    Article  Google Scholar 

  7. Kumar, J.P., Domnic, S.: Image based Leaf segmentation and counting in Rosette plants. Inf. Process. Agricul (2019). https://doi.org/10.1016/j.inpa.2018.09.005

    Article  Google Scholar 

  8. Wang, Z., Wang, K., Yang, F., Pan, S., Han, H.: Image segmentation of overlapping leaves based on Chan-vese model and sobel operator. Information Processing in Agriculture (2018). https://doi.org/10.1016/j.inpa.2017.09.005

    Article  Google Scholar 

  9. Ye, M., Cao, Z.-G., Yu, Z., Bai, X.D.: Crop feature extraction from images with probabilistic superpixel Markov random field. Comput. Electron. Agric. (2015). https://doi.org/10.1016/j.compag.2015.04.010

    Article  Google Scholar 

  10. Zhou, J., Zhou, J.: Automated segmentation of soybean plants from 3D point cloud using machine learning. Comput. Electron. Agric. (2019). https://doi.org/10.1016/j.compag.2019.04.014

    Article  Google Scholar 

  11. Pravin Kumar, S.K., Sumithra, M.G., Saranya, N.: Artificial bee colony-based fuzzy c means (ABC-FCM) segmentation algorithm and dimensionality reduction for leaf disease detection in bioinformatics. J Supercomput (2019). https://doi.org/10.1007/s11227-019-02999-z

    Article  Google Scholar 

  12. Smith, A.G., Petersen, J., Selvan, R., et al.: Segmentation of roots in soil with UNet. Plant Methods 16, 1–15 (2020). https://doi.org/10.1186/s13007-020-0563-0

    Article  Google Scholar 

  13. Minaee, S., Boykov, Y.Y., Porikli, F., Plaza, A.J., Kehtarnavaz, N., Terzopoulos, D.: Image segmentation using deep learning: a survey. IEEE Trans. Pattern Anal. Mach. Intell. (2021). https://doi.org/10.1109/TPAMI.2021.3059968

    Article  Google Scholar 

  14. Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N. and Liang, J.: UNet++: a nested unet architecture for medical image segmentation. In: Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. Lecture Notes in Computer Science, Springer Cham, pp. 3-11 (2018). doi:https://doi.org/10.1007/978-3-030-00889-5_1

  15. Punn, N., Agarwal, S.: Inception U-net architecture for semantic segmentation to identify nuclei in microscopy cell images. ACM Trans. Multimed. Comput. Commun. Appl. 16, 1–15 (2020). https://doi.org/10.1145/3376922

    Article  Google Scholar 

  16. Guo, C., Szemenyei, M., Pei, Y., Yi, Y., Zhou, W.: SD-Unet: A Structured Dropout U-Net for Retinal Vessel Segmentation, pp 439–444 (2019). https://doi.org/10.1109/BIBE.2019.00085.

  17. Delibasoğlu, İ: INCSA-UNET: Spatial attention inception UNET for aerial images segmentation. Comput. Inf. 40(6), 1244–1262 (2022). https://doi.org/10.31577/cai_2021_6_1244

    Article  Google Scholar 

  18. Agarwal, M., Gupta, S.K., Biswas, K.K.: Plant leaf disease segmentation using compressed UNet architecture. In: Gupta, M., Ramakrishnan, G. (eds.) Trends and Applications in Knowledge Discovery and Data Mining PAKDD. Lecture Notes in Computer Science, pp. 9–14. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75015-2_2

    Chapter  Google Scholar 

  19. Williams, T., Li, R.: Advanced Image Classification Using Wavelets and Convolutional Neural Networks. https://doi.org/10.1109/ICMLA. (2016)

  20. Allison, R., Wenjin Z.: Improving classification with CNNs using wavelet pooling with Nesterov-accelerated Adam, In: Proceedings of 11th International Conference on Bioinformatics and Computational Biology, EPiC Series in Computing, pp. 84–93 (2019), https://doi.org/10.29007/9c5j

  21. Ozan, O., Jo, S., Bernhard, K., Ben, G.: Attention UNet: Learning Where to Look for the Pancreas, (2018). https://doi.org/10.48550/arXiv.1804.03999.

  22. Kang, J., Liu, L., Zhang, F., Shen, C., Wang, N., Shao, L.: Semantic segmentation model of cotton roots in-situ image based on attention mechanism. Comput. Electron. Agric. (2021). https://doi.org/10.1016/j.compag.2021.106370

    Article  Google Scholar 

  23. Jo, S., Ozan, O., Michiel, S., Mattias, H., Bernhard, K., Ben, G., Daniel, R.: Attention gated networks: Learning to leverage salient regions in medical images. Med. Image Anal. (2019). https://doi.org/10.1016/j.media.2019.01.012

    Article  Google Scholar 

  24. Sambasivam, G., Opiyo, G.D.: A predictive machine learning application in agriculture: Cassava disease detection and classification with imbalanced dataset using Convolutional neural networks. Egyptian Inf. J. (2021). https://doi.org/10.1016/j.eij.2020.02.007

    Article  Google Scholar 

  25. Jalal Uddin, Md., Yubin Li, Md., Sattar, A., Most, Z., Nasrin, C.L.: Effects of learning rates and optimization algorithms on forecasting accuracy of hourly typhoon rainfall: experiments with convolutional neural network. Earth Space Sci. (2022). https://doi.org/10.1029/2021EA002168

    Article  Google Scholar 

  26. Ronneberger, O., Fischer, P., Brox, T.: UNet: Convolutional Networks for Biomedical Image Segmentation, pp.234–241 (2015). https://doi.org/10.1007/978-3-319-24574-4_28.

  27. Bai, Y., Guo, Y., Zhang, Q., Cao, B., Zhang, B.: Multi-network fusion algorithm with transfer learning for green cucumber segmentation and recognition under complex natural environment. Comput. Electron. Agricult. (2022). https://doi.org/10.1016/j.compag.2022.106789

    Article  Google Scholar 

  28. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. (2014) http://arxiv.org/abs/1409.1556.

  29. Ghosh, S., Chaki, A., Santosh, K.: Improved UNet architecture with VGG-16 for brain tumor segmentation. Phys. Eng. Sci. Med. 44(703–712), 919–995 (2021). https://doi.org/10.1007/s13246-021-01019-w

    Article  Google Scholar 

  30. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition, pp. 770–778 (2016). https://doi.org/10.1109/CVPR.2016.90.

  31. Mushtaq, M., Akram, M.U., Alghamdi, N.S., Fatima, J., Masood, R.F.: Localization and edge-based segmentation of lumbar spine vertebrae to identify the deformities using deep learning models. Sensors (Basel) 22(4), 1547 (2022). https://doi.org/10.3390/s22041547

    Article  Google Scholar 

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This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Research Scholar, Department of ECE, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India

    A. Shamim Banu

  2. Government Polytechnic College, Thuvakudi, Tiruchirappalli, Tamil Nadu, India

    A. Shamim Banu

  3. Department of ECE, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India

    S. Deivalakshmi

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All authors carried out the literature review. Ms A. Shamim Banu conducted the experiments. Dr. S. Deivalakshmi reviewed the manuscript and approved the final version of manuscript after peer review. All the authors approved to submit the manuscript in the Journal tilted “Signal, Image and Video Processing.”

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Correspondence to S. Deivalakshmi.

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Banu, A.S., Deivalakshmi, S. AWUNet: leaf area segmentation based on attention gate and wavelet pooling mechanism. SIViP 17, 1915–1924 (2023). https://doi.org/10.1007/s11760-022-02403-z

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