A Hybrid Deep Learning Approach for Crop Disease Severity Level Prediction

Authors

  • Vishnu C. Khade Research Scholar, SCOE, SPPU Pune, Assistant Professor, RIT Islampur, Maharashtra, India
  • Sanjay B. Patil Principal, SCSOCE, Dhangawadi, Pune, Maharashtra, India

Keywords:

Disease Severity, Maize Crop, CNN Model, Transfer Learning, Attention Layer.

Abstract

The threat of crop diseases and the need for efficient management are critical concerns, particularly in the context of maize cultivation. Early detection and accurate estimation of disease severity play a pivotal role in safeguarding maize crops and ensuring optimal yield. Convolutional Neural Networks (CNNs) have emerged as invaluable tools for this purpose, showcasing their prowess in automatic feature extraction. In the realm of maize disease severity estimation, the distinct characteristics of diseases, such as variations in lesions texture along with variations its color serve as crucial factors for automated assessment through machine learning. In this paper, a CNN model is developed with combination of transfer learning features from ResNet101 and Inception-V3 models. The features obtained from these models are then combined and passed through the attention layer ensures optimal performance.  With tuning of hyper parameters and 5-fold analysis model is set for highest performance of 0.956 of accuracy. The high specificity of 0.985 shows models suitability for primary stage disease detection. This approach reflects a proactive strategy in addressing the challenges associated with disease severity estimation in maize cultivation, utilizing cutting-edge technologies for the benefit of agricultural sustainability.

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References

L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” J. Big Data 2021 81, vol. 8, no. 1, pp. 1–74, Mar. 2021, doi: 10.1186/S40537-021-00444-8.

P. Wspanialy and M. Moussa, “A detection and severity estimation system for generic diseases of tomato greenhouse plants,” Comput. Electron. Agric., vol. 178, p. 105701, Nov. 2020, doi: 10.1016/J.COMPAG.2020.105701.

M. M. Taye, “Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions,” Comput. 2023, Vol. 11, Page 52, vol. 11, no. 3, p. 52, Mar. 2023, doi: 10.3390/COMPUTATION11030052.

H. Yang et al., “Automatic Recognition of Rice Leaf Diseases Using Transfer Learning,” Agron. 2023, Vol. 13, Page 961, vol. 13, no. 4, p. 961, Mar. 2023, doi: 10.3390/AGRONOMY13040961.

P. Rawat, A. Pandey, and A. Panaiyappan.k, “Rice Leaf Diseases Classification Using Deep Learning Techniques,” Proc. 1st IEEE Int. Conf. Netw. Commun. 2023, ICNWC 2023, 2023, doi: 10.1109/ICNWC57852.2023.10127315.

M. H. Al-Adhaileh, A. Verma, T. H. H. Aldhyani, and D. Koundal, “Potato Blight Detection Using Fine-Tuned CNN Architecture,” Math. 2023, Vol. 11, Page 1516, vol. 11, no. 6, p. 1516, Mar. 2023, doi: 10.3390/MATH11061516.

G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely Connected Convolutional Networks,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 2261–2269, Aug. 2016, doi: 10.1109/CVPR.2017.243.

W. Li et al., “Fraxin ameliorates lipopolysaccharide-induced acute lung injury in mice by inhibiting the NF-κB and NLRP3 signalling pathways,” Int. Immunopharmacol., vol. 67, pp. 1–12, Feb. 2019, doi: 10.1016/J.INTIMP.2018.12.003.

S. H. Lee, H. Goëau, P. Bonnet, and A. Joly, “New perspectives on plant disease characterization based on deep learning,” Comput. Electron. Agric., vol. 170, p. 105220, Mar. 2020, doi: 10.1016/J.COMPAG.2020.105220.

L. Yang et al., “Stacking-based and improved convolutional neural network: a new approach in rice leaf disease identification,” Front. Plant Sci., vol. 14, p. 1165940, Jun. 2023, doi: 10.3389/FPLS.2023.1165940/BIBTEX.

X. Qian, C. Zhang, L. Chen, and K. Li, “Deep Learning-Based Identification of Maize Leaf Diseases Is Improved by an Attention Mechanism: Self-Attention,” Front. Plant Sci., vol. 13, p. 864486, Apr. 2022, doi: 10.3389/FPLS.2022.864486/BIBTEX.

Z. Ma et al., “Maize leaf disease identification using deep transfer convolutional neural networks,” Int. J. Agric. Biol. Eng., vol. 15, no. 5, pp. 187–195, Nov. 2022, doi: 10.25165/IJABE.V15I5.6658.

G. Li, J. Wang, H. W. Shen, K. Chen, G. Shan, and Z. Lu, “CNNPruner: Pruning Convolutional Neural Networks with Visual Analytics,” IEEE Trans. Vis. Comput. Graph., vol. 27, no. 2, pp. 1364–1373, Sep. 2020, doi: 10.1109/TVCG.2020.3030461.

P. Singh, V. K. Verma, P. Rai, and V. P. Namboodiri, “Acceleration of Deep Convolutional Neural Networks Using Adaptive Filter Pruning,” IEEE J. Sel. Top. Signal Process., vol. 14, no. 4, pp. 838–847, May 2020, doi: 10.1109/JSTSP.2020.2992390.

N. Kundu et al., “Disease detection, severity prediction, and crop loss estimation in MaizeCrop using deep learning,” Artif. Intell. Agric., vol. 6, pp. 276–291, Jan. 2022, doi: 10.1016/J.AIIA.2022.11.002.

Y. Li, S. Sun, C. Zhang, G. Yang, and Q. Ye, “One-Stage Disease Detection Method for Maize Leaf Based on Multi-Scale Feature Fusion,” Appl. Sci. 2022, Vol. 12, Page 7960, vol. 12, no. 16, p. 7960, Aug. 2022, doi: 10.3390/APP12167960.

D. Faye et al., “Plant Disease Severity Assessment Based on Machine Learning and Deep Learning: A Survey,” J. Comput. Commun., vol. 11, no. 9, pp. 57–75, Sep. 2023, doi: 10.4236/JCC.2023.119004.

T. Shi et al., “Recent advances in plant disease severity assessment using convolutional neural networks,” Sci. Reports 2023 131, vol. 13, no. 1, pp. 1–13, Feb. 2023, doi: 10.1038/s41598-023-29230-7.

A. Ahmad, “CD&S Dataset,” 2021, doi: 10.17605/OSF.IO/S6RU5.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep Residual Learning for Image Recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-December, pp. 770–778, Dec. 2015, doi: 10.1109/CVPR.2016.90.

C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the Inception Architecture for Computer Vision,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., vol. 2016-December, pp. 2818–2826, Dec. 2015, doi: 10.1109/CVPR.2016.308.

A. Zafar et al., “A Comparison of Pooling Methods for Convolutional Neural Networks,” Appl. Sci. 2022, Vol. 12, Page 8643, vol. 12, no. 17, p. 8643, Aug. 2022, doi: 10.3390/APP12178643.

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Published

27.12.2023

How to Cite

Khade, V. C. ., & Patil, S. B. . (2023). A Hybrid Deep Learning Approach for Crop Disease Severity Level Prediction. International Journal of Intelligent Systems and Applications in Engineering, 12(9s), 215–224. Retrieved from https://www.ijisae.org/index.php/IJISAE/article/view/4267

Issue

Vol. 12 No. 9s (2024)

Section

Research Article

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