A Hybrid Lightweight Deep Neural Network Approach for Plant Disease Classification Using Self-Attention Mechanism and Transfer Learning

Abstract

Classification of plant diseases is crucial for overall food security and agricultural economies in the world. However, classification has long been challenging primarily due to the various diseases it encompasses and the different environmental factors influencing them. One of the main challenges in developing an accurate classification model is obtaining high-quality, multiclass datasets. At the same time, deep learning methods like CNN may be considered state-of-the-art in detecting complex image patterns in various applications for correct diagnoses. Still, they involve poor parameter optimizations and overfitting and have very high resource requirements. This paper introduces a combined model of classifying plant diseases in an imaging approach, which incorporates the Efficient Neural Network (ENN) with a Squeeze and Excitation Network (SEN). The architecture follows high-density feature extraction by the networks, late fusion of features, and using a cross-channel attention mechanism to boost feature representation. This work uses transfer learning to design the hyperparameter optimization scheme and early stopping scheme to avoid overfitting. We tested our model on the Plant Village Dataset and the Leaf Rose Disease Dataset with an accuracy of 96.40% for the Plant Village Dataset and 97.15% accuracy on the Leaf Rose Disease Dataset. Our model achieved higher accuracy than the traditional DNNs VGG16, Inception V3, and RESNET-50 by approximately 21.04%, 9.40%, and 4.33% on the Plant Village Dataset. It improved the classification accuracy compared to VGG16, Inception V3, and RESNET-50 by 15.80%, 11.20%, and 6.02% on the Rose leaf disease dataset, respectively. Moreover, it has the lowest times as well as space complexity: 45 minutes and 150 MB, which are less than VGG16 (50 minutes, 180 MB), Inception Net (55 minutes, 170 MB), and RESNET- 50 (75 minutes, 190 MB). The global results show that our approach is superior, demonstrating enhanced performance and efficiency, which makes it well-suited for real-time applications.

Keywords

• Leaf classification
• convolutional neural network
• Efficient Neural Network
• Squeeze and Excitation Network
• classification accuracy

References

1. Agarwal G, Belhumeur P, Feiner S, Jacobs D, Kress W J, Ramamoorthi R & White S (2006). First steps toward an electronic field guide for plants. Taxon 55(3): 597-610 https://doi.org/10.2307/25065637
2. Ahmad I, Hamid M, Yousaf S, Shah S T & Ahmad M O (2020). Optimizing pretrained convolutional neural networks for tomato leaf disease detection. Complexity 2020(1): 8812019 https://doi.org/10.1155/2020/8812019
3. Akilan T, Wu Q J, Safaei A & Jiang W (2017). A late fusion approach for harnessing multi-CNN model high-level features. In 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC) 566-571. https://doi.org/10.1109/smc.2017.8122666
4. Ashwinkumar S, Rajagopal S, Manimaran V & Jegajothi B (2022). Automated plant leaf disease detection and classification using optimal MobileNet based convolutional neural networks, Materials Today: Proceedings 51(1): 480-487, https://doi.org/10.1016/j.matpr.2021.05.584
5. Bi C, Wang J, Duan Y, Fu B, Kang J R & Shi Y (2022). MobileNet based apple leaf diseases identification. Mobile Networks and Applications, 1-9. https://doi.org/10.1007/s11036-020-01640-1
6. Bock C H, Barbedo J G, Del Ponte E M, Bohnenkamp D & Mahlein A K (2020). From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy. Phytopathology Research, 2, 1-30. https://doi.org/10.1186/s42483-020-00049-8
7. Chai Ali, C A, Li BaoJu L B, Shi YanXia S Y, Cen ZheXin C Z, Huang HaiYang H H & Liu Jun L J (2010). Recognition of tomato foliage disease based on computer vision technology. Acta Horticulturae Sinica, 37(9), 1423-1430
8. Chen G H, Yang L & Xie S L (2006). Gradient-based structural similarity for image quality assessment. In 2006 international conference on image processing 2929-2932. IEEE. http://doi.org/10.1109/ICIP.2006.313132

9. Chen Q & Wu D (2010). Image denoising by bounded block matching and 3D filtering. Signal Processing 90(9): 2778-2783. https://doi.org/10.1016/j.sigpro.2010.03.016
10. Devi R S, Kumar V R & Sivakumar P (2023). EfficientNetV2 Model for Plant Disease Classification and Pest Recognition. Computer Systems Science & Engineering 45(2). https://doi.org/10.32604/csse.2023.032231
11. Ferentinos K P (2018). Deep learning models for plant disease detection and diagnosis. Computers and electronics in agriculture, 145, 311- 318. https://doi.org/10.1016/j.compag.2018.01.009
12. Ghazi M M, Yanikoglu B & Aptoula E (2017). Plant identification using deep neural networks via optimization of transfer learning parameters. Neurocomputing 235: 228-235. https://doi.org/10.1016/j.neucom.2017.01.018
13. Guan Z X, Tang J, Yang B J, Zhou Y F, Fan D Y & Yao Q (2010). Study on recognition method of rice disease based on image. Chinese Journal of Rice Science 24(5): 497
14. He K, Zhang X, Ren S & Sun J (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition 770-778. https://doi.org/10.1109/cvpr.2016.90
15. Hu J, Shen L & Sun G (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7132-7141). https://doi.org/10.1109/cvpr.2018.00745
16. Hu J, Shen L & Sun G (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE conference on computer vision and pattern recognition 7132-7141. https://doi.org/10.1109/cvpr.2018.00745
17. Hughes D & Salathé M (2015). An open access repository of images on plant health to enable the development of mobile disease diagnostics. arXiv preprint arXiv:1511.08060
18. Kamilaris A & Prenafeta-Boldú F X (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147, 70-90. https://doi.org/10.1016/j.compag.2018.02.016
19. Kawasaki Y, Uga H, Kagiwada S & Iyatomi H (2015). Basic study of automated diagnosis of viral plant diseases using convolutional neural networks. In Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part II 11 (pp. 638-645). Springer International Publishing. https://doi.org/10.1007/978-3-319-27863-6_59
20. Koonce B (2021). Convolutional neural networks with swift for tensorflow: Image recognition and dataset categorization 109-123). New York, NY, USA: Apress.
21. Li F, Cong R, Bai H & He Y (2020). Deep interleaved network for image super-resolution with asymmetric co-attention. arXiv preprint arXiv:2004.11814.
22. Li L, Zhang S & Wang B (2021). Plant disease detection and classification by deep learning—a review. IEEE Access, 9, 56683-56698. https://doi.org/10.1109/access.2021.3069646
23. Liu N & Kan J M (2016). Improved deep belief networks and multi-feature fusion for leaf identification. Neurocomputing, 216, 460-467. https://doi.org/10.1016/j.neucom.2016.08.005
24. Ma J, Du K, Zheng F, Zhang L, Gong Z & Sun Z (2018). A recognition method for cucumber diseases using leaf symptom images based on deep convolutional neural network. Computers and electronics in agriculture, 154, 18-24. https://doi.org/10.1016/j.compag.2018.08.048
25. Mohanty S P, Hughes D P & Salathé M (2016). Using deep learning for image-based plant disease detection. Frontiers in plant science, 7, 1419. https://doi.org/10.3389/fpls.2016.01419
26. Pal A & Kumar V (2023). AgriDet: Plant Leaf Disease severity classification using agriculture detection framework. Engineering Applications of Artificial Intelligence 119: 105754. https://doi.org/10.1016/j.engappai.2022.105754
27. Reyes A K, Caicedo J C & Camargo J E (2015). Fine-tuning Deep Convolutional Networks for Plant Recognition. CLEF (Working Notes) 1391: 467-475
28. Ronneberger O, Fischer P & Brox T (2015). U-Net: Convolutional networks for biomedical image segmentation. In Medical image computing and computer-assisted intervention–MICCAI 2015: 18th international conference, Munich, Germany, October 5-9, 2015, proceedings, part III 18 (pp. 234-241). Springer International Publishing. https://doi.org/10.1007/978-3-662-54345-0_3
29. Sai Reddy B & Neeraja S (2022). Plant leaf disease classification and damage detection system using deep learning models. Multimedia tools and applications 81(17): 24021-24040. https://doi.org/10.1007/s11042-022-12147-0
30. Sazzad S, Rajbongshi A, Shakil R, Akter B & Kaiser M S (2022). RoseNet: Rose leave dataset for the development of an automation system to recognize the diseases of rose. Data in Brief, 44, 108497. https://doi.org/10.1016/j.dib.2022.108497
31. Simonyan K & Zisserman A (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
32. Srivastava R K, Greff K & Schmidhuber J (2015). Highway networks. arXiv preprint arXiv:1505.00387.
33. Strange R N & Scott P R (2005). Plant disease: a threat to global food security. Annual Review of Phytopathology. 43: 83-116
34. Sun Y, Liu Y, Wang G & Zhang H (2017). Deep learning for plant identification in natural environment. Computational intelligence and neuroscience, 2017. https://doi.org/10.1155/2017/7361042
35. Szegedy C, Vanhoucke V, Ioffe S, Shlens J & Wojna Z (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2818-2826. https://doi.org/10.1109/cvpr.2016.308
36. Tan M & Le Q (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning 6105-6114. PMLR.
37. Theckedath D & Sedamkar R R (2020). Detecting affect states using VGG16, ResNet50 and SE-ResNet50 networks. SN Computer Science 1(2): 79. https://doi.org/10.1007/s42979-020-0114-9
38. Tiwari A (2023). A hybrid feature selection method using an improved binary butterfly optimization algorithm and adaptive β–hill climbing. IEEE Access 11: 93511-93537. https://doi.org/10.1109/access.2023.3274469
39. Tiwari A & Chaturvedi A (2021). A novel channel selection method for BCI classification using dynamic channel relevance. IEEE Access, 9: 126698-126716. https://doi.org/10.1109/access.2021.3110882
40. Tiwari A & Chaturvedi, A. (2022). A hybrid feature selection approach based on information theory and dynamic butterfly optimization algorithm for data classification. Expert Systems with Applications, 196, 116621. https://doi.org/10.1016/j.eswa.2022.116621
41. Tiwari A & Chaturvedi A (2023). Automatic EEG channel selection for multiclass brain-computer interface classification using multiobjective improved firefly algorithm. Multimedia Tools and Applications, 82(4), 5405-5433. https://doi.org/10.1007/s11042-022-12795-2
42. Tiwari V, Joshi R C & Dutta M K (2021). Dense convolutional neural networks based multiclass plant disease detection and classification using leaf images. Ecological Informatics 63: 101289. https://doi.org/10.1016/j.ecoinf.2021.101289
43. Victoria A H & Maragatham G (2021). Automatic tuning of hyperparameters using Bayesian optimization. Evolving Systems, 12(1), 217-223. https://doi.org/10.1007/s12530-020-09345-2
44. Weiss K, Khoshgoftaar T M & Wang D (2016). A survey of transfer learning. Journal of Big data 3: 1-40 Wong T T, & Yang N Y (2017). Dependency analysis of accuracy estimates in k-fold cross validation. IEEE Transactions on Knowledge and Data Engineering 29(11): 2417-2427. https://doi.org/10.1109/tkde.2017.2740926
45. Yanikoglu B, Aptoula E & Tirkaz C. (2014). Automatic plant identification from photographs. Machine vision and applications 25: 1369- 1383. https://doi.org/10.1007/s00138-014-0612-7
46. Zhang Y, Tian Y, Kong Y, Zhong B & Fu Y (2018). Residual dense network for image super-resolution. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 2472-2481).
47. Zhao H, Jia J & Koltun V (2020). Exploring self-attention for image recognition. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition 10076-10085. https://doi.org/10.1109/cvpr42600.2020.01009
48. Zhao Z Q, Zheng P, Xu S T & Wu X (2019). Object detection with deep Learning: A review. IEEE transactions on neural networks and learning systems 30(11): 3212-3232. https://doi.org/10.1109/tnnls.2018.2876865