Abstract
The evolution of Few-Shot Learning (FSL) technologies has significantly enhanced the capacity for accurate plant disease classification. This paper introduces an FSL model that integrates the Performer-attention mechanism, marking a novel exploration in the domain of plant disease detection. The proposed approach is based on the Performer-attention mechanism that significantly enhances the model’s learning efficiency from limited examples and improves disease classification accuracy. Our model is developed through a two-step process: data preprocessing followed by the application of an attention-guided FSL process. This latter step encompasses patch extraction, performer attention, patch embedding, informative patch selection, masked image modeling, and the FSL application. The proposed techniques ensure the capability to address the issue of sample scarcity while ensuring scalability and efficiency. The efficacy of our approach is validated using the PlantVillage dataset and compared with seven state-of-the-art works. Results demonstrate exceptional accuracy rates of 92.15%, 98.12%, and 99.12% across 1-shot, 5-shot, and 10-shot learning scenarios, respectively. These findings depict the potential of our proposed model for more effective crop health monitoring and promoting sustainable agriculture.
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Data Availability
The data used in this study is publicly available at https://www.kaggle.com/datasets/emmarex/plantdisease
References
Afifi A, Alhumam A, Abdelwahab A (2020) Convolutional neural network for automatic identification of plant diseases with limited data. Plants 10(1):28
AIMultiple (2023) What is few-shot learning? methods & applications in 2023. https://research.aimultiple.com/few-shot-learning/
Alzahem A, Boulila W, Koubaa A, Khan Z, Alturki I (2023) Improving satellite image classification accuracy using gan-based data augmentation and vision transformers. Earth Sci Inf 16(4):4169–4186
Argüeso D, Picon A, Irusta U, Medela A, San-Emeterio MG, Bereciartua A, Alvarez-Gila A (2020) Few-shot learning approach for plant disease classification using images taken in the field. Comput Electron Agric 175:105542
Atila Ü, Uçar M, Akyol K, Uçar E (2021) Plant leaf disease classification using efficientnet deep learning model. Eco Inform 61:101182
Bouguettaya A, Zarzour H, Kechida A, Taberkit AM (2022) Deep learning techniques to classify agricultural crops through uav imagery: a review. Neural Comput Appl 34(12):9511–9536
Bouguettaya A, Zarzour H, Kechida A, Taberkit AM (2023) A survey on deep learning-based identification of plant and crop diseases from uav-based aerial images. Clust Comput 26(2):1297–1317
Boulila W, Ayadi Z, Farah IR (2017) Sensitivity analysis approach to model epistemic and aleatory imperfection: application to land cover change prediction model. J Comput Sci 23:58–70
Boulila W, Alzahem A, Koubaa A, Benjdira B, Ammar A (2023) Early detection of red palm weevil infestations using deep learning classification of acoustic signals. Comput Electron Agric 212:108154
Chen L, Cui X, Li W (2021) Meta-learning for few-shot plant disease detection. Foods 10(10):2441
Choromanski K, Likhosherstov V, Dohan D, Song X, Gane A, Sarlos T, Hawkins P, Davis J, Mohiuddin A, Kaiser L et al (2020) Rethinking attention with performers. arXiv:2009.14794
Ferchichi A, Boulila W, Farah IR (2018) Reducing uncertainties in land cover change models using sensitivity analysis. Knowl Inf Syst 55:719–740
Garg S, Singh P (2023) An aggregated loss function based lightweight few shot model for plant leaf disease classification. Multimed Tools Appl 1–19
Golhani K, Balasundram SK, Vadamalai G, Pradhan B (2018) A review of neural networks in plant disease detection using hyperspectral data. Inf Process Agric 5(3):354–371
Gómez-Vargas N, Alonso-Fernández A, Blanquero R, Antelo LT (2023) Re-identification of fish individuals of undulate skate via deep learning within a few-shot context. Eco Inform 75:102036. https://doi.org/10.1016/j.ecoinf.2023.102036. https://www.sciencedirect.com/science/article/pii/S1574954123000651
Hiller M, Ma R, Harandi M, Drummond T (2022) Rethinking generalization in few-shot classification. Adv Neural Inf Process Syst 35:3582–3595
Karthik R, Hussain S, George TT, Mishra R (2023) A dual track deep fusion network for citrus disease classification using group shuffle depthwise feature pyramid and swin transformer. Eco Inform 78:102302
Kaur P, Harnal S, Tiwari R, Upadhyay S, Bhatia S, Mashat A, Alabdali AM (2022) Recognition of leaf disease using hybrid convolutional neural network by applying feature reduction. Sensors 22(2):575
Li Y, Chao X (2021) Semi-supervised few-shot learning approach for plant diseases recognition. Plant Methods 17:1–10
Li Y, Yang J (2020) Few-shot cotton pest recognition and terminal realization. Comput Electron Agric 169:105240
Li Y, Yang J (2021) Meta-learning baselines and database for few-shot classification in agriculture. Comput Electron Agric 182:106055
Li L, Zhang S, Wang B (2021) Plant disease detection and classification by deep learning–a review. IEEE Access 9:56683–56698
Lin H, Tse R, Tang SK, Qiang Z, Pau G (2022) Few-shot learning for plant-disease recognition in the frequency domain. Plants 11(21):2814
Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF international conference on computer vision, pp 10012–10022
Meng X, Tian X, Wu Q, Chen Y, Pan J, Hang Y (2023) Meta-baseline based on deep neuro-fuzzy network for few-shot plant leaf fungal diseases recognition. Imaging Sci J 1–13
Mishra AM, Harnal S, Gautam V, Tiwari R, Upadhyay S (2022) Weed density estimation in soya bean crop using deep convolutional neural networks in smart agriculture. J Plant Dis Prot 129(3):593–604
Pan J, Xia L, Wu Q, Guo Y, Chen Y, Tian X (2022) Automatic strawberry leaf scorch severity estimation via faster r-cnn and few-shot learning. Eco Inform 70:101706
Pandey A, Jain K (2022) A robust deep attention dense convolutional neural network for plant leaf disease identification and classification from smart phone captured real world images. Eco Inform 70:101725
Sharma V, Tripathi AK, Mittal H (2023) Dlmc-net: deeper lightweight multi-class classification model for plant leaf disease detection. Eco Inform 75:102025
Sigurðardóttir AR, Sverrisson Þ, Jónsdóttir A, Gudjónsdóttir M, Þór Elvarsson B, Einarsson H (2023) Otolith age determination with a simple computer vision based few-shot learning method. Eco Inform 76:102046. https://doi.org/10.1016/j.ecoinf.2023.102046. https://www.sciencedirect.com/science/article/pii/S1574954123000754
Sun J, Cao W, Fu X, Ochi S, Yamanaka T (2023) Few-shot learning for plant disease recognition: a review. Agronomy Journal
Sunil C, Jaidhar C, Patil N (2022) Binary class and multi-class plant disease detection using ensemble deep learning-based approach. Int J Sustain Agric Manag Inf 8(4):385–407
Sunil C, Jaidhar C, Patil N (2023) Systematic study on deep learning-based plant disease detection or classification. Artif Intell Rev 56(12):14955–15052
Sunil C, Jaidhar C, Patil N (2023) Tomato plant disease classification using multilevel feature fusion with adaptive channel spatial and pixel attention mechanism. Expert Syst Appl 228:120381
Thakur PS, Chaturvedi S, Khanna P, Sheorey T, Ojha A (2023) Vision transformer meets convolutional neural network for plant disease classification. Eco Inform 77:102245
Tiwari V, Joshi RC, Dutta MK (2021) Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. Eco Inform 63:101289
Varone G, Boulila W, Driss M, Kumari S, Khan MK, Gadekallu TR, Hussain A (2024) Finger pinching and imagination classification: a fusion of cnn architectures for iomt-enabled bci applications. Information Fusion 101:102006
Wang Y, Yao Q, Kwok JT, Ni LM (2020) Generalizing from a few examples: A survey on few-shot learning. ACM Computing Surveys (csur) 53(3):1–34
Wang C, Zhou J, Zhao C, Li J, Teng G, Wu H (2021) Few-shot vegetable disease recognition model based on image text collaborative representation learning. Comput Electron Agric 184:106098
Yang J, Guo X, Li Y, Marinello F, Ercisli S, Zhang Z (2022) A survey of few-shot learning in smart agriculture: developments, applications, and challenges. Plant Methods 18(1):1–12
Zhong F, Chen Z, Zhang Y, Xia F (2020) Zero-and few-shot learning for diseases recognition of citrus aurantium l. using conditional adversarial autoencoders. Comput Electron Agric 179:105828
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The author would like to thank Prince Sultan University for their support.
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The entirety of the work presented in this paper was carried out by Wadii Boulila. This includes the conception and design of the study, development of the methodology, data collection and analysis, interpretation of the results, and drafting of the manuscript.
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Communicated by: Hassan Babaie.
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Boulila, W. An approach based on performer-attention-guided few-shot learning model for plant disease classification. Earth Sci Inform (2024). https://doi.org/10.1007/s12145-024-01339-x
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DOI: https://doi.org/10.1007/s12145-024-01339-x