ItemArticle

Machine Learning and Deep Learning Approaches for Guava Disease Detection

Title
Machine Learning and Deep Learning Approaches for Guava Disease Detection
Creator
Paramesha, K.; Jalapur, Shruti; Hanok, Shalini; Puttegowda, Kiran; Manjunatha, G.; Kumara, Bharath
Description
A larger proportion of crops face disease outbreaks, making agricultural output difficult. Detecting and predicting diseases at an early stage can enhance productivity. Guava, a tropical and subtropical fruit, is cultivated in various countries. In regions such as Bangladesh, Pakistan, India, and South America, guava cultivation faces significant challenges due to diseases like Canker, Dot, Mummification, Phytophthora, Scab, and Styler and Root. Traditional diagnosis methods based on visual observation are often unreliable and time-consuming. To address this, we developed an automated system leveraging deep learning techniques. Our study utilized a dataset comprising 4046 guava leaf images categorized into these seven disease classes. We compared the performance of traditional methods with deep learning approaches using vision transformers and transfer learning. The results demonstrate the superiority of deep learning methods over traditional approaches, where traditional machine learning model SVM gave accuracy near 78% and deep learning methods gave over 90%. The transfer learning method gave an accuracy of nearly 97% and on the other hand, the vision transformer gave accuracy of 98%. This offers a promising solution for early disease detection in guava crops. The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.
Source
SN Computer Science;Volume;6;Issue;4;Article No.;361;
Date
01-01-2025
Publisher
Springer
Subject
Accuracy; Deep learning; Guava disease; Leaf images; Machine learning
Coverage
Paramesha K., Department of Computer Science and Engineering, Vidyavardhaka College of Engineering, Karnataka, Mysuru, India; Jalapur S., Department of Computer Science and Engineering, Christ University, Karnataka, Bengaluru, India; Hanok S., Department of Electronics and Communication Engineering, ATME College of Engineering, Karnataka, Mysuru, India; Puttegowda K., Department of Electronics and Communication Engineering, Vidyavardhaka College of Engineering, Karnataka, Mysuru, India; Manjunatha G., Department of CSD, PES Institute of Technology and Management, Karnataka, Shivamogga, India; Kumara B., Department of ECE, MS Ramaiah University of Applied Science (MSRUAS), Karnataka, Bangalore, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISSN: 2662995X;
Format
online
Language
English
Type
Article

Citation

Paramesha, K.; Jalapur, Shruti; Hanok, Shalini; Puttegowda, Kiran; Manjunatha, G.; Kumara, Bharath, “Machine Learning and Deep Learning Approaches for Guava Disease Detection,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22140.

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