Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images
Artículo
Materias > Ingeniería
Materias > Alimentación
Universidad Europea del Atlántico > Investigación > Artículos y libros
Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica
Universidad Internacional Iberoamericana México > Investigación > Producción Científica
Universidad Internacional do Cuanza > Investigación > Producción Científica
Universidad de La Romana > Investigación > Producción Científica
Abierto
Inglés
Maize is a staple crop worldwide, essential for food security, livestock feed, and industrial uses. Its health directly impacts agricultural productivity and economic stability. Effective detection of maize crop health is crucial for preventing disease spread and ensuring high yields. This study presents VG-GNBNet, an innovative transfer learning model that accurately detects healthy and infected maize crops through a two-step feature extraction process. The proposed model begins by leveraging the visual geometry group (VGG-16) network to extract initial pixel-based spatial features from the crop images. These features are then further refined using the Gaussian Naive Bayes (GNB) model and feature decomposition-based matrix factorization mechanism, which generates more informative features for classification purposes. This study incorporates machine learning models to ensure a comprehensive evaluation. By comparing VG-GNBNet's performance against these models, we validate its robustness and accuracy. Integrating deep learning and machine learning techniques allows VG-GNBNet to capitalize on the strengths of both approaches, leading to superior performance. Extensive experiments demonstrate that the proposed VG-GNBNet+GNB model significantly outperforms other models, achieving an impressive accuracy score of 99.85%. This high accuracy highlights the model's potential for practical application in the agricultural sector, where the precise detection of crop health is crucial for effective disease management and yield optimization.
metadata
Tanveer, Muhammad Usama; Munir, Kashif; Raza, Ali; Abualigah, Laith; Garay, Helena; Prado González, Luis Eduardo y Ashraf, Imran
mail
SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, helena.garay@uneatlantico.es, uis.prado@uneatlantico.es, SIN ESPECIFICAR
(2025)
Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images.
Food Science & Nutrition, 13 (1).
ISSN 2048-7177
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Texto
Food Science Nutrition - 2025 - Tanveer - Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop.pdf Available under License Creative Commons Attribution. Descargar (866kB) |
Resumen
Maize is a staple crop worldwide, essential for food security, livestock feed, and industrial uses. Its health directly impacts agricultural productivity and economic stability. Effective detection of maize crop health is crucial for preventing disease spread and ensuring high yields. This study presents VG-GNBNet, an innovative transfer learning model that accurately detects healthy and infected maize crops through a two-step feature extraction process. The proposed model begins by leveraging the visual geometry group (VGG-16) network to extract initial pixel-based spatial features from the crop images. These features are then further refined using the Gaussian Naive Bayes (GNB) model and feature decomposition-based matrix factorization mechanism, which generates more informative features for classification purposes. This study incorporates machine learning models to ensure a comprehensive evaluation. By comparing VG-GNBNet's performance against these models, we validate its robustness and accuracy. Integrating deep learning and machine learning techniques allows VG-GNBNet to capitalize on the strengths of both approaches, leading to superior performance. Extensive experiments demonstrate that the proposed VG-GNBNet+GNB model significantly outperforms other models, achieving an impressive accuracy score of 99.85%. This high accuracy highlights the model's potential for practical application in the agricultural sector, where the precise detection of crop health is crucial for effective disease management and yield optimization.
| Tipo de Documento: | Artículo |
|---|---|
| Palabras Clave: | feature extraction ; plant disease detection ; plant leaf detection ; precision agriculture ; transfer learning |
| Clasificación temática: | Materias > Ingeniería Materias > Alimentación |
| Divisiones: | Universidad Europea del Atlántico > Investigación > Artículos y libros Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Universidad de La Romana > Investigación > Producción Científica |
| Depositado: | 07 Ene 2025 23:30 |
| Ultima Modificación: | 07 Ene 2025 23:30 |
| URI: | https://repositorio.uneatlantico.es/id/eprint/15983 |
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