eprintid: 5631 rev_number: 11 eprint_status: archive userid: 2 dir: disk0/00/00/56/31 datestamp: 2023-01-31 23:30:09 lastmod: 2023-07-11 23:30:33 status_changed: 2023-01-31 23:30:09 type: article metadata_visibility: show creators_name: Gautam, Vinay creators_name: Trivedi, Naresh K. creators_name: Singh, Aman creators_name: Mohamed, Heba G. creators_name: Delgado Noya, Irene creators_name: Kaur, Preet creators_name: Goyal, Nitin creators_id: creators_id: creators_id: aman.singh@uneatlantico.es creators_id: creators_id: irene.delgado@uneatlantico.es creators_id: creators_id: title: A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment ispublished: pub subjects: uneat_eng divisions: uneatlantico_produccion_cientifica divisions: uninimx_produccion_cientifica divisions: unic_produccion_cientifica full_text_status: public keywords: artificial intelligence; transfer learning; paddy leaf disease detection; crop disease classification abstract: The paddy crop is the most essential and consumable agricultural produce. Leaf disease impacts the quality and productivity of paddy crops. Therefore, tackling this issue as early as possible is mandatory to reduce its impact. Consequently, in recent years, deep learning methods have been essential in identifying and classifying leaf disease. Deep learning is used to observe patterns in disease in crop leaves. For instance, organizing a crop’s leaf according to its shape, size, and color is significant. To facilitate farmers, this study proposed a Convolutional Neural Networks-based Deep Learning (CNN-based DL) architecture, including transfer learning (TL) for agricultural research. In this study, different TL architectures, viz. InceptionV3, VGG16, ResNet, SqueezeNet, and VGG19, were considered to carry out disease detection in paddy plants. The approach started with preprocessing the leaf image; afterward, semantic segmentation was used to extract a region of interest. Consequently, TL architectures were tuned with segmented images. Finally, the extra, fully connected layers of the Deep Neural Network (DNN) are used to classify and identify leaf disease. The proposed model was concerned with the biotic diseases of paddy leaves due to fungi and bacteria. The proposed model showed an accuracy rate of 96.4%, better than state-of-the-art models with different variants of TL architectures. After analysis of the outcomes, the study concluded that the anticipated model outperforms other existing models date: 2022-10 publication: Sustainability volume: 14 number: 20 pagerange: 13610 id_number: doi:10.3390/su142013610 refereed: TRUE issn: 2071-1050 official_url: http://doi.org/10.3390/su142013610 access: open language: en citation: Artículo Materias > Ingeniería Universidad Europea del Atlántico > Investigación > Producción Científica Universidad Internacional Iberoamericana México > Investigación > Producción Científica Universidad Internacional do Cuanza > Investigación > Producción Científica Abierto Inglés The paddy crop is the most essential and consumable agricultural produce. Leaf disease impacts the quality and productivity of paddy crops. Therefore, tackling this issue as early as possible is mandatory to reduce its impact. Consequently, in recent years, deep learning methods have been essential in identifying and classifying leaf disease. Deep learning is used to observe patterns in disease in crop leaves. For instance, organizing a crop’s leaf according to its shape, size, and color is significant. To facilitate farmers, this study proposed a Convolutional Neural Networks-based Deep Learning (CNN-based DL) architecture, including transfer learning (TL) for agricultural research. In this study, different TL architectures, viz. InceptionV3, VGG16, ResNet, SqueezeNet, and VGG19, were considered to carry out disease detection in paddy plants. The approach started with preprocessing the leaf image; afterward, semantic segmentation was used to extract a region of interest. Consequently, TL architectures were tuned with segmented images. Finally, the extra, fully connected layers of the Deep Neural Network (DNN) are used to classify and identify leaf disease. The proposed model was concerned with the biotic diseases of paddy leaves due to fungi and bacteria. The proposed model showed an accuracy rate of 96.4%, better than state-of-the-art models with different variants of TL architectures. After analysis of the outcomes, the study concluded that the anticipated model outperforms other existing models metadata Gautam, Vinay; Trivedi, Naresh K.; Singh, Aman; Mohamed, Heba G.; Delgado Noya, Irene; Kaur, Preet y Goyal, Nitin mail SIN ESPECIFICAR, SIN ESPECIFICAR, aman.singh@uneatlantico.es, SIN ESPECIFICAR, irene.delgado@uneatlantico.es, SIN ESPECIFICAR, SIN ESPECIFICAR (2022) A Transfer Learning-Based Artificial Intelligence Model for Leaf Disease Assessment. Sustainability, 14 (20). p. 13610. ISSN 2071-1050 document_url: http://repositorio.unic.co.ao/id/eprint/5631/1/sustainability-14-13610-v3.pdf