ItemArticle

A hybrid multi-optimizer approach using CNN and GB for accurate prediction of citrus fruit diseases

Title
A hybrid multi-optimizer approach using CNN and GB for accurate prediction of citrus fruit diseases
Creator
Kujur, Lawrence; Gupta, Varuna; Singhal, Abhinav
Description
Efficient prediction of citrus fruit diseases is essential for maintaining orchard health and productivity. Traditional diagnostic methods, often relying on manual inspection, are labor-intensive and prone to inaccuracies. Deep learning techniques, especially Convolutional Neural Networks (CNNs), offer an automated and accurate alternative. This study introduces a novel model integrating CNN with Gradient Boosting (GB) and optimized using the Nesterov-Accelerated Adaptive Moment Estimation (Nadam) optimizer to enhance prediction accuracy. The model employs a custom CNN architecture combined with GB, leveraging Nadam for faster convergence and improved performance. Trained on a dataset of 3,000 citrus fruit images sourced from Kaggle, the model follows a structured process of preprocessing, feature extraction, integration of GB with CNN, and optimal prediction. Comparative analysis using metrics such as accuracy, precision, F1 score, and recall demonstrates the model's effectiveness, achieving an accuracy of 98.03% and precision of 98.04%. This robust approach addresses limitations of traditional methods by enabling automated feature extraction and reliable disease prediction. The proposed CNN-GB-Nadam model significantly enhances efficiency and reliability, providing a valuable tool for protecting citrus fruit health and improving orchard management practices. The Author(s) 2025.
Source
Discover Applied Sciences;Volume;7;Issue;3;Article No.;151;
Date
01-01-2025
Publisher
Springer Nature
Subject
CNN-GB-Nadam; Convolutional Neural Networks (CNN); Disease protection; Forecasting; Gradient boost (GB); NADAM
Coverage
Kujur L., Christ University, Bengaluru, 560029, India; Gupta V., Christ University, Bengaluru, 560029, India; Singhal A., Christ University, Bengaluru, 560029, India
Rights
All Open Access; Gold Open Access
Relation
ISSN: 30049261;
Format
online
Language
English
Type
Article

Citation

Kujur, Lawrence; Gupta, Varuna; Singhal, Abhinav, “A hybrid multi-optimizer approach using CNN and GB for accurate prediction of citrus fruit diseases,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/22123.

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