Detection of cotton crops diseases using customized deep learning model

Article Subjects > Engineering <http://repositorio.unib.org/view/subjects/uneat=5Feng.html> Europe University of Atlantic > Research > Scientific Production <http://repositorio.unib.org/view/divisions/uneatlantico=5Fproduccion=5Fcientifica.html>
Ibero-american International University > Research > Scientific Production <http://repositorio.unib.org/view/divisions/uninimx=5Fproduccion=5Fcientifica.html>
Ibero-american International University > Research > Articles and Books <http://repositorio.unib.org/view/divisions/uninipr=5Fproduccion=5Fcientifica.html>
Universidad Internacional do Cuanza > Research > Scientific Production <http://repositorio.unib.org/view/divisions/unic=5Fproduccion=5Fcientifica.html>
University of La Romana > Research > Scientific Production <http://repositorio.unib.org/view/divisions/uniromana=5Fproduccion=5Fcientifica.html> Open English 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 and Ashraf, Imran mail UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, UNSPECIFIED, silvia.aparicio@uneatlantico.es, ruben.calderon@uneatlantico.es, UNSPECIFIED <http://repositorio.unib.org/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