eprintid: 15983 rev_number: 9 eprint_status: archive userid: 2 dir: disk0/00/01/59/83 datestamp: 2025-01-07 23:30:11 lastmod: 2025-01-07 23:30:11 status_changed: 2025-01-07 23:30:11 type: article metadata_visibility: show creators_name: Tanveer, Muhammad Usama creators_name: Munir, Kashif creators_name: Raza, Ali creators_name: Abualigah, Laith creators_name: Garay, Helena creators_name: Prado González, Luis Eduardo creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: helena.garay@uneatlantico.es creators_id: uis.prado@uneatlantico.es creators_id: title: Novel Transfer Learning Approach for Detecting Infected and Healthy Maize Crop Using Leaf Images ispublished: pub subjects: uneat_eng subjects: uneat_sn divisions: uneatlantico_produccion_cientifica divisions: unincol_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: unic_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: feature extraction ; plant disease detection ; plant leaf detection ; precision agriculture ; transfer learning abstract: 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. date: 2025-01 publication: Food Science & Nutrition volume: 13 number: 1 id_number: doi:10.1002/fsn3.4655 refereed: TRUE issn: 2048-7177 official_url: http://doi.org/10.1002/fsn3.4655 access: open language: en citation: Artículo Materias > Ingeniería Materias > Alimentación Universidad Europea del Atlántico > Investigación > Producción Científica Fundación Universitaria Internacional de Colombia > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Artículos y libros 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 document_url: http://repositorio.unic.co.ao/id/eprint/15983/1/Food%20Science%20%20%20Nutrition%20-%202025%20-%20Tanveer%20-%20Novel%20Transfer%20Learning%20Approach%20for%20Detecting%20Infected%20and%20Healthy%20Maize%20Crop.pdf