ItemConference Paper

Early Detection and Analysis of Potato Leaf Diseases Using Deep Learning based CNN Models

Title
Early Detection and Analysis of Potato Leaf Diseases Using Deep Learning based CNN Models
Creator
Kumar, Prince; Anuj; Ragavendra, T.S.; Chinnaiyan, R.; Sabarmathi, G.; Khanna, Vimal Kumar
Description
Potato diseases pose a significant threat to global agricultural productivity, leading to severe economic losses. Early and accurate disease detection is crucial for effective disease management and improved crop yield. This research explores deep learning techniques for automated potato disease prediction using convolutional neural networks (CNNs). A large dataset of potato leaf images is used to train and validate the model, ensuring robustness and accuracy. The proposed deep learning model efficiently classifies common potato diseases, such as late blight and early blight, with high precision. Performance evaluation metrics, include accuracy, The integration of deep learning in disease prediction minimizes the reliance on manual inspection, providing farmers with a cost-effective and scalable solution. Additionally, we analyze the impact of transfer learning and data augmentation on model performance. The results highlight the potential of AI-driven approaches in precision agriculture, offering real-time disease diagnosis and early intervention strategies. This research contributes to the advancement of smart farming technologies, ensuring sustainable crop protection and food security. Future work will focus on optimizing the model for real-world deployment through mobile applications and IoT-based systems. 2025 IEEE.
Source
2025 2nd International Conference on New Frontiers in Communication, Automation, Management and Security, ICCAMS 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Accuracy; Convolutional Neural Networks (CNNs); Deep learning; Early blight; Late blight
Coverage
Kumar P., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Anuj, Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Ragavendra T.S., S-Vyasa School of Advanced Studies, Department of Computer Science and Engineering, Bengaluru, India; Chinnaiyan R., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India; Sabarmathi G., Christ University, School of Computer Science, Bangalore, India; Khanna V.K., Lingaya's Vidyapeeth, AI/ML Lab, Centre of Excellence, Department of Computer Science and Engineering, Haryana, Faridabad, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833159610-1;
Format
online
Language
English
Type
Conference paper

Citation

Kumar, Prince; Anuj; Ragavendra, T.S.; Chinnaiyan, R.; Sabarmathi, G.; Khanna, Vimal Kumar, “Early Detection and Analysis of Potato Leaf Diseases Using Deep Learning based CNN Models,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25920.

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