ItemArticle

Optimizing citrus disease detection: a transferable convolutional neural network model enhanced with the fruitfly optimization algorithm

Title
Optimizing citrus disease detection: a transferable convolutional neural network model enhanced with the fruitfly optimization algorithm
Creator
Lingaraju, Anoop Ganadalu; Shankaregowda, Asha Mangala; Sathiyamurthy, Babu Kumar; Channegowda, Santhrupth Budanoor; Jalapur, Shruti; Chennakeshava, Chaitra Palahalli
Description
Fungal, bacterial, and viral diseases significantly threaten citrus production and quality worldwide, prompting producers to explore technological solutions to mitigate the financial impact of these diseases. Image analysis techniques have emerged as powerful tools for detecting citrus diseases by differentiating between healthy and diseased specimens through the extraction of discriminative features from input images. This paper introduces a valuable dataset comprising 953 color images of orange leaves from the species Citrus sinensis (L.) Osbeck, which serves to train, evaluate, and compare various algorithms aimed at identifying abnormalities in citrus fruits. The development of automated detection systems is crucial for reducing economic losses in citrus production, with this research focusing on twelve specific diseases and nutrient deficiencies. We propose a novel approach to citrus plant disease detection utilizing a hyper-parameter tuned transferrable convolutional neural network (TCNN) model, referred to as the enhanced fruitfly optimization algorithm (EFOA)-TCNN model. This model optimizes the parameters of TCNN using the EFOA and enhances architectural design by incorporating three convolutional layers alongside an energy layer instead of a traditional pooling layer. Experimental results demonstrate that the proposed EFOA-TCNN model outperforms existing state-of-the-art methods, achieving a sensitivity of 0.975 and an accuracy of 0.995. 2025, Institute of Advanced Engineering and Science. All rights reserved.
Source
IAES International Journal of Artificial Intelligence;Volume;14;Issue;4;pp.3201-3213
Date
01-01-2025
Publisher
Institute of Advanced Engineering and Science
Subject
Bacteria; Citrus fruit disease detection; Enhanced fruitfly optimization algorithm; Image analysis; Transferrable convolutional neural network
Coverage
Lingaraju A.G., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India; Shankaregowda A.M., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India; Sathiyamurthy B.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India; Channegowda S.B., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India; Jalapur S., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India; Chennakeshava C.P., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Bengaluru, India
Rights
All Open Access; Gold Open Access
Relation
ISSN: 20894872;
Format
online
Language
English
Type
Article

Citation

Lingaraju, Anoop Ganadalu; Shankaregowda, Asha Mangala; Sathiyamurthy, Babu Kumar; Channegowda, Santhrupth Budanoor; Jalapur, Shruti; Chennakeshava, Chaitra Palahalli, “Optimizing citrus disease detection: a transferable convolutional neural network model enhanced with the fruitfly optimization algorithm,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/23076.

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