A hybrid multi-optimizer approach using CNN and GB for accurate prediction of citrus fruit diseases
Abstract 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 (...
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| Format: | Article |
| Language: | English |
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Springer
2025-02-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06593-2 |
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| author | Lawrence Kujur Varuna Gupta Abhinav Singhal |
| author_facet | Lawrence Kujur Varuna Gupta Abhinav Singhal |
| author_sort | Lawrence Kujur |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-a7f446797f1442a7a7dcf9ed4e05710f |
| institution | DOAJ |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-a7f446797f1442a7a7dcf9ed4e05710f2025-08-20T03:11:09ZengSpringerDiscover Applied Sciences3004-92612025-02-017311510.1007/s42452-025-06593-2A hybrid multi-optimizer approach using CNN and GB for accurate prediction of citrus fruit diseasesLawrence Kujur0Varuna Gupta1Abhinav Singhal2Christ UniversityChrist UniversityChrist UniversityAbstract 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.https://doi.org/10.1007/s42452-025-06593-2Convolutional Neural Networks (CNN)Gradient boost (GB)NADAMForecastingCNN-GB-NadamDisease protection |
| spellingShingle | Lawrence Kujur Varuna Gupta Abhinav Singhal A hybrid multi-optimizer approach using CNN and GB for accurate prediction of citrus fruit diseases Discover Applied Sciences Convolutional Neural Networks (CNN) Gradient boost (GB) NADAM Forecasting CNN-GB-Nadam Disease protection |
| title | A hybrid multi-optimizer approach using CNN and GB for accurate prediction of citrus fruit diseases |
| title_full | A hybrid multi-optimizer approach using CNN and GB for accurate prediction of citrus fruit diseases |
| title_fullStr | A hybrid multi-optimizer approach using CNN and GB for accurate prediction of citrus fruit diseases |
| title_full_unstemmed | A hybrid multi-optimizer approach using CNN and GB for accurate prediction of citrus fruit diseases |
| title_short | A hybrid multi-optimizer approach using CNN and GB for accurate prediction of citrus fruit diseases |
| title_sort | hybrid multi optimizer approach using cnn and gb for accurate prediction of citrus fruit diseases |
| topic | Convolutional Neural Networks (CNN) Gradient boost (GB) NADAM Forecasting CNN-GB-Nadam Disease protection |
| url | https://doi.org/10.1007/s42452-025-06593-2 |
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