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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Budanoor Channegowda, Santhrupth
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Computer Science & IT Engineering

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Optimizing citrus disease detection: a transferrable convolutional neural network model enhanced with the fruitfly optimization algorithm Ganadalu Lingaraju, Anoop; Mangala Shankaregowda, Asha; Sathiyamurthy, Babu Kumar; Budanoor Channegowda, Santhrupth; Jalapur, Shruti; Palahalli Chennakeshava, Chaitra
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i4.pp3201-3213

Abstract

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.
Co-Authors Ganadalu Lingaraju, Anoop Jalapur, Shruti Mangala Shankaregowda, Asha Palahalli Chennakeshava, Chaitra Sathiyamurthy, Babu Kumar