TY  - JOUR
UR  - http://doi.org/10.1016/j.compeleceng.2023.108659
N2  - Given that it provides nourishment for more than half of humanity, rice is regarded as one of the most significant plants in the world in agriculture. The quantity and quality of the product may be impacted by diseases that can damage rice plants which can occasionally cause crop losses ranging from 30 to 60%. This manuscript proposed a Convolutional Neural Network (CNN) and Visual Geometry Group (VGG)19 i.e. CNN-VGG19 model with a transfer learning-based method for the precise identification and classification of rice leaf diseases. This scheme employs a transfer learning technique based on the VGG19 which can identify the brown spot class. The accuracy is 93.0% in the deployment of the dataset of rice leaf disease. The other parameters are sensitivity, specificity, precision and F1-score with 89.9%, 94.7%, 92.4% and 90.5% respectively. The developed technique obtained better results as compared to the existing baseline models.
ID  - uninipr10010
JF  - Computers and Electrical Engineering
TI  - Deep learning model for detection of brown spot rice leaf disease with smart agriculture
Y1  - 2023/07//
VL  - 109
AV  - none
A1  - Dogra, Roopali
A1  - Rani, Shalli
A1  - Singh, Aman
A1  - Albahar, Marwan Ali
A1  - Pascual Barrera, Alina Eugenia
A1  - Alkhayyat, Ahmed
SN  - 00457906
ER  -