eprintid: 17595 rev_number: 8 eprint_status: archive userid: 2 dir: disk0/00/01/75/95 datestamp: 2025-04-10 23:30:13 lastmod: 2025-04-10 23:30:14 status_changed: 2025-04-10 23:30:13 type: article metadata_visibility: show creators_name: Faisal, Hafiz Muhammad creators_name: Aqib, Muhammad creators_name: Rehman, Saif Ur creators_name: Mahmood, Khalid creators_name: Aparicio Obregón, Silvia creators_name: Calderón Iglesias, Rubén creators_name: Ashraf, Imran creators_id: creators_id: creators_id: creators_id: creators_id: silvia.aparicio@uneatlantico.es creators_id: ruben.calderon@uneatlantico.es creators_id: title: Detection of cotton crops diseases using customized deep learning model ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: uninipr_produccion_cientifica divisions: unic_produccion_cientifica divisions: uniromana_produccion_cientifica full_text_status: public keywords: Agricultural economics, Deep learning, Cotton crop disease, Precision agriculture abstract: The agricultural industry is experiencing revolutionary changes through the latest advances in artificial intelligence and deep learning-based technologies. These powerful tools are being used for a variety of tasks including crop yield estimation, crop maturity assessment, and disease detection. The cotton crop is an essential source of revenue for many countries highlighting the need to protect it from deadly diseases that can drastically reduce yields. Early and accurate disease detection is quite crucial for preventing economic losses in the agricultural sector. Thanks to deep learning algorithms, researchers have developed innovative disease detection approaches that can help safeguard the cotton crop and promote economic growth. This study presents dissimilar state-of-the-art deep learning models for disease recognition including VGG16, DenseNet, EfficientNet, InceptionV3, MobileNet, NasNet, and ResNet models. For this purpose, real cotton disease data is collected from fields and preprocessed using different well-known techniques before using as input to deep learning models. Experimental analysis reveals that the ResNet152 model outperforms all other deep learning models, making it a practical and efficient approach for cotton disease recognition. By harnessing the power of deep learning and artificial intelligence, we can help protect the cotton crop and ensure a prosperous future for the agricultural sector. date: 2025-03 publication: Scientific Reports volume: 15 number: 1 id_number: doi:10.1038/s41598-025-94636-4 refereed: TRUE issn: 2045-2322 official_url: http://doi.org/10.1038/s41598-025-94636-4 access: open language: en citation: Artículo Materias > Ingeniería <http://repositorio.unini.edu.mx/view/subjects/uneat=5Feng.html> Universidad Europea del Atlántico > Investigación > Producción Científica <http://repositorio.unini.edu.mx/view/divisions/uneatlantico=5Fproduccion=5Fcientifica.html> Universidad Internacional Iberoamericana México > Investigación > Artículos y libros <http://repositorio.unini.edu.mx/view/divisions/uninimx=5Fproduccion=5Fcientifica.html> Universidad Internacional Iberoamericana Puerto Rico > Investigación > Producción Científica <http://repositorio.unini.edu.mx/view/divisions/uninipr=5Fproduccion=5Fcientifica.html> Universidad Internacional do Cuanza > Investigación > Producción Científica <http://repositorio.unini.edu.mx/view/divisions/unic=5Fproduccion=5Fcientifica.html> Universidad de La Romana > Investigación > Producción Científica <http://repositorio.unini.edu.mx/view/divisions/uniromana=5Fproduccion=5Fcientifica.html> Abierto Inglés The agricultural industry is experiencing revolutionary changes through the latest advances in artificial intelligence and deep learning-based technologies. These powerful tools are being used for a variety of tasks including crop yield estimation, crop maturity assessment, and disease detection. The cotton crop is an essential source of revenue for many countries highlighting the need to protect it from deadly diseases that can drastically reduce yields. Early and accurate disease detection is quite crucial for preventing economic losses in the agricultural sector. Thanks to deep learning algorithms, researchers have developed innovative disease detection approaches that can help safeguard the cotton crop and promote economic growth. This study presents dissimilar state-of-the-art deep learning models for disease recognition including VGG16, DenseNet, EfficientNet, InceptionV3, MobileNet, NasNet, and ResNet models. For this purpose, real cotton disease data is collected from fields and preprocessed using different well-known techniques before using as input to deep learning models. Experimental analysis reveals that the ResNet152 model outperforms all other deep learning models, making it a practical and efficient approach for cotton disease recognition. By harnessing the power of deep learning and artificial intelligence, we can help protect the cotton crop and ensure a prosperous future for the agricultural sector. metadata Faisal, Hafiz Muhammad; Aqib, Muhammad; Rehman, Saif Ur; Mahmood, Khalid; Aparicio Obregón, Silvia; Calderón Iglesias, Rubén y Ashraf, Imran mail SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, SIN ESPECIFICAR, silvia.aparicio@uneatlantico.es, ruben.calderon@uneatlantico.es, SIN ESPECIFICAR <http://repositorio.unini.edu.mx/id/eprint/17595/1/s41598-025-94636-4.pdf> (2025) Detection of cotton crops diseases using customized deep learning model. Scientific Reports, 15 (1). ISSN 2045-2322 document_url: http://repositorio.unini.edu.mx/id/eprint/17595/1/s41598-025-94636-4.pdf