Plant Leaf Disease Detection using Deep Learning and Image Processing: A Systematic Review
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Abstract
Plant leaf diseases significantly affect agricultural productivity and crop quality worldwide, leading to major economic losses and food security challenges. Early and accurate detection of plant diseases is essential for improving crop management and reducing the excessive use of pesticides. In recent years, deep learning and image processing techniques have emerged as effective approaches for automated plant leaf disease detection due to their high accuracy and efficiency. This systematic review presents a comprehensive analysis of existing research on plant leaf disease detection using deep learning and image processing methods. The review discusses various stages involved in disease detection, including image acquisition, preprocessing, segmentation, feature extraction, classification, and performance evaluation. Different machine learning and deep learning models such as Convolutional Neural Networks (CNN), ResNet, VGG, AlexNet, MobileNet, and hybrid optimization techniques are examined and compared based on accuracy, computational efficiency, and dataset utilization. The study also highlights commonly used datasets, challenges associated with real-time field conditions, image quality, class imbalance, and limitations of traditional disease identification methods. Furthermore, recent advancements in transfer learning, data augmentation, IoT-based monitoring systems, and smartphone-assisted disease diagnosis are discussed. The review concludes that deep learning-based approaches provide superior performance compared to conventional image processing methods and have strong potential for developing intelligent, real-time, and cost-effective agricultural disease management systems.
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References
R. Kumar et al., “Hybrid Approach of Cotton Disease Detection for Enhanced Crop Health and Yield”, in IEEE Access, vol. 12, pp. 132495-132507, 2024.
I. Ahmed, G. Habib and P. K. Yadav, “An approach to identify and classify agricultural crop diseases using machine learning and deep learning techniques”, Proc. Int. Conf. Emerg. Smart Comput. Informat. (ESCI), pp. 1-6, Mar. 2023.
S. Bondre and D. Patil, “Recent advances in agricultural disease image recognition technologies: A review”, Concurrency Computation Pract. Exper., vol. 35, no. 9, Apr. 2023.
M. M. Islam, M. A. Talukder, M. R. A. Sarker, M. A. Uddin, A. Akhter, S. Sharmin, et al., "A deep learning model for cotton disease prediction using fine-tuning with smart web application in agriculture", Intell. Syst. Appl., vol. 20, Nov. 2023.
R. Mahum, H. Munir, Z.-U.-N. Mughal, M. Awais, F. S. Khan, M. Saqlain, et al., "A novel framework for potato leaf disease detection using an efficient deep learning model", Hum. Ecol. Risk Assessment Int. J., vol. 29, no. 2, pp. 303-326, Feb. 2023.
L. Goel and J. Nagpal, "A systematic review of recent machine learning techniques for plant disease identification and classification", IETE Tech. Rev., vol. 40, no. 3, pp. 423-439, May 2023.
C. Sarkar, D. Gupta, U. Gupta and B. B. Hazarika, "Leaf disease detection using machine learning and deep learning: Review and challenges", Appl. Soft Comput., vol. 145, Sep. 2023.
Y. Yuan, L. Chen, H.Wu, and L. Li, ‘‘Advanced agricultural disease image recognition technologies: A review,’’ Inf. Process. Agricult., vol. 9, no. 1, pp. 48–59, Mar. 2022.
V. Kathole and M. Munot, ‘‘Deep learning models for tomato plant disease detection,’’ in Advanced Machine Intelligence and Signal Processing. Singapore: Springer, pp. 679–686, 2022.
Q. Pan, J.-F. Qiao, R. Wang, H.-L. Yu, C. Wang, K. Taylor, and H.-Y. Pan, ‘‘Intelligent diagnosis of northern corn leaf blight with deep learning model,’’ J. Integrative Agricult., vol. 21, no. 4, pp. 1094–1105, 2022.
U. Chauhan, ‘‘ResTS: Residual deep interpretable architecture for plant disease detection,’’ Inf. Process. Agricult., vol. 9, no. 2, pp. 212–223, Jun. 2022.
Vallabhajosyula, V. Sistla, and V. K. K. Kolli, ‘‘Transfer learning-based deep ensemble neural network for plant leaf disease detection,’’ J. Plant Diseases Protection, vol. 129, no. 3, pp. 545–558, Jun. 2022.
Chen, Z. Yuan, S. Chen, and X. Zou, ‘‘Plant disease recognition model based on improved YOLOv5,’’ Agronomy, vol. 12, no. 2, p. 365, Jan. 2022.
Wang, X. He, K. Feng, and H. Zhu, ‘‘Plant disease detection and classification method based on the optimized lightweight YOLOv5 model”, Agriculture, vol. 12, no. 7, p. 931, Jun. 2022.
J. Karthika, M. Santhose and T. Sharan, "Disease detection in cotton leaf spot using image processing", J. Phys. Conf. Ser., vol. 1916, no. 1, 2021.
Aditi Singh and Harjeet Kaur, “Potato Plant Leaves Disease Detection and Classification using Machine Learning Methodologies”, IOP Conf. Series: Materials Science and Engineering, pp. 01-09, ICCRDA 2021.
K Anandhan, Ajay Shanker Singh, K Thirunavukkarasu and Raju Shanmugam, “Comprehensive Study: Machine Learning & Deep Learning Algorithms for Paddy Crops”, 9th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO), IEEE 2021.