ItemConference Paper

Automated Leaf Disease Detection using a Hybrid CNN-BiLSTM Model for Smart Agriculture

Title
Automated Leaf Disease Detection using a Hybrid CNN-BiLSTM Model for Smart Agriculture
Creator
Deepakraj, Jamuna; John, Tegil J; Sathiyanathan, S.; Josephson, P Joel; Priyanka, H.D.; Suganthi, D.
Description
The mitigation of crop losses and the sustainability of agriculture rely on the prompt identification of foliar diseases. In large-scale agriculture, conventional identification methods such as expert eye inspections are inefficient, susceptible to errors, and labour-intensive. A growing number of individuals are seeking automated methods to monitor plant health, given that the majority of Indians are employed in agriculture. This study presents a hybrid DL strategy for leaf disease detection, encompassing preprocessing, segmentation, feature extraction, and model training. Initially, images are processed to enhance their quality and uniformity. The impacted regions of the leaf are subsequently categorised by K-Means clustering. The classification accuracy is improved by utilising several feature extraction methods. The proposed model, CNBiLS, integrates bidirectional LSTM layers with convolutional layers to leverage the spatial and sequential information in image data. When evaluated against contemporary state-of-the-art models, CNBiLS exhibited superior performance, achieving an exceptional 99.84% classification accuracy. This result underscores the model's accuracy in identifying various leaf diseases. Ultimately, CNBiLS offers a precise, scalable, and robust automated system for detecting leaf diseases, equipping farmers with timely information to manage illnesses effectively, so enhancing both the quality and yield of their crops. 2025 IEEE.
Source
Proceedings of 8th International Conference on Computing Methodologies and Communication, ICCMC 2025;pp.1993-1998
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Back Propagation Neural Network (BPNN); Graphical User Interface (GUI); Hue Saturation Value (HSV); Leaf Disease Detection (LDD); Local Binary Patterns (LBP); Markov Random Fields (MRFs)
Coverage
Deepakraj J., Erode Sengunthar Engineering College, Department of Artificial Intelligence and Data Science, Tamil Nadu, Perundurai, India; John T.J., Christ University, Department of Computer Science, Karnataka, Bangalore, India; Sathiyanathan S., M.Kumarasamy College of Engineering, Department of It, Tamilnadu, Karur, India; Josephson P.J., Malla Reddy (MR) Deemed to Be University, Dept of Ece, Telangana, Hyderabad, India; Priyanka H.D., Bgs Institute of Technology, Faculty of Engineering, Management and Technology, Department of Chemistry, Mandya, India; Suganthi D., Chennai Institute of Technology, Department of Computer Science and Engineering, Chennai, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833151211-8;
Format
online
Language
English
Type
Conference paper

Citation

Deepakraj, Jamuna; John, Tegil J; Sathiyanathan, S.; Josephson, P Joel; Priyanka, H.D.; Suganthi, D., “Automated Leaf Disease Detection using a Hybrid CNN-BiLSTM Model for Smart Agriculture,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25944.

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