Advancements in Deep Learning Techniques for Potato Leaf Disease Identification Using SAM-CNNet Classification
- Title
- Advancements in Deep Learning Techniques for Potato Leaf Disease Identification Using SAM-CNNet Classification
- Creator
- Patil S.; Satya A.D.V.; Bajjuri U.R.; Damarapati P.K.; Manur M.; Thirumalraj A.; Vatambeti R.
- Description
- Potato leaf diseases like Late Blight and Early Blight significantly challenge potato cultivation, impacting crop yield and quality worldwide. Potatoes are a staple for over a billion people and crucial for food security, especially in developing countries. The economic impact is substantial, with Late Blight alone causing annual damages over $6 billion globally. Effective detection and management are essential to mitigate these effects on agricultural productivity and economic stability. This paper presents a novel approach to potato leaf disease detection using advanced deep learning and optimization techniques. Key components include data normalization to eliminate noise, feature extraction using GoogLeNet, and hyperparameter tuning through the Elk Herd Optimizer (EHO). Additionally, a Spatial Attention Mechanism and Convolutional Neural Network (SAM-CNNet) are employed for robust classification. The method is validated using the Plant Village dataset, yielding an accuracy of 98.58%, with precision of 97.68%, recall of 98.42%, and F1-Score of 98.21%, demonstrating exceptional performance and reliability. This study highlights the proposed approach's efficacy in accurately identifying and classifying potato leaf diseases, offering a promising solution for precision agriculture and crop management. Copyright: 2024 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license.
- Source
- Ingenierie des Systemes d'Information, Vol-29, No. 5, pp. 2021-2030.
- Date
- 2024-01-01
- Publisher
- International Information and Engineering Technology Association
- Subject
- convolutional neural network; data normalization; GoogLeNet; potato leaf diseases; Spatial Attention Mechanism
- Coverage
- Patil S., Department of Information Technology, MLR Institute of Technology, Hyderabad, 500043, India; Satya A.D.V., Department of Chemistry, New Horizon College of Engineering, Bengaluru, 560103, India; Bajjuri U.R., Department of Computer Science and Engineering, Lakireddy Bali Reddy College of Engineering, Mylavaram, 521230, India; Damarapati P.K., Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Guntur, 522302, India; Manur M., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, 560074, India; Thirumalraj A., Department of Computer Science and Engineering, K. Ramakrishna College of Technology, Trichy, 621112, India; Vatambeti R., School of Computer Science and Engineering, VIT-AP University, Vijayawada, 522237, India
- Rights
- All Open Access; Hybrid Gold Open Access
- Relation
- ISSN: 16331311
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Patil S.; Satya A.D.V.; Bajjuri U.R.; Damarapati P.K.; Manur M.; Thirumalraj A.; Vatambeti R., “Advancements in Deep Learning Techniques for Potato Leaf Disease Identification Using SAM-CNNet Classification,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/12809.