Loading article...
Loading article...
Stream/Area:
-
Subject:
-
Paper ID:
IJSREM62371
Volume:
10 |
5 |
May 2026
Pages:
1-4
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Computer Science and Engineering
KMM Institute Of Post Graduate Studies
Computer Science and Engineering
Bapatla Engineering College
Computer Science and Engineering
KMM Institute Of Post Graduate Studies
Agriculture remains a critical sector for global food security and economic stability. Crop diseases significantly reduce agricultural productivity, causing substantial economic losses worldwide. Traditional disease identification methods rely on manual inspection by experts, which is time-consuming, subjective, and often inaccurate. Recent advancements in Deep Learning (DL) and Computer Vision provide efficient automated solutions for plant disease detection. This paper proposes a deep learning-based crop disease detection system using transfer learning techniques. Pre-trained convolutional neural network (CNN) models such as ResNet50 and MobileNetV2 are fine-tuned using the PlantVillage dataset to classify healthy and diseased plant leaves. The proposed approach leverages transfer learning to reduce computational complexity and improve classification performance. Experimental results demonstrate that the ResNet50-based model achieves superior accuracy compared to baseline CNN models. The proposed system can assist farmers and agricultural stakeholders in early disease detection, thereby improving productivity and sustainability in smart agriculture systems.
KEYWORDS
This paper presented a deep learning-based crop disease detection system using transfer learning techniques. The comparative analysis of ResNet50 and MobileNetV2 demonstrated that transfer learning significantly enhances classification accuracy. The proposed model achieved an accuracy of 98.2%, making it suitable for practical smart agriculture applications. The system can assist farmers in early disease detection and improve agricultural productivity.
[1] S. Mohanty, D. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Frontiers in Plant Science, vol. 7, pp. 1–10, 2016. [2] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 770–778. [3] M. Sandler, A. Howard, M. Zhu, A. Zhmoginov, and L. Chen, “MobileNetV2: Inverted residuals and linear bottlenecks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4510–4520. [4] A. Krizhevsky, I. Sutskever, and G. Hinton, “ImageNet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems (NeurIPS), 2012, pp. 1097–1105. [5] J. Deng et al., “ImageNet: A large-scale hierarchical image database,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009, pp. 248–255.
Publication Date
5/3/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
Documents
Article Views
1
Total views since publication
PDF Downloads
7
Total downloads since publication
Geographic Reach
5
Digital Preservation: This article is permanently archived on Zenodo and complies with all data preservation standards across major indexing platforms.
Plagiarism Screening
This manuscript has been checked for plagiarism using TurnitinPlagiarismChecker software.
Conflict of Interest
The authors declare no conflict of interest related to this research work.
Funding Information
No funding information available for this research.
Related research in the same subject area
Loading similar articles...