Affiliations: [a]
Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India
| [b]
Department of Electrical and Electronics Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu, India
| [c]
Srinivasa Ramanujan Centre, SASTRA Deemed University, Kumbakonam, Tamilnadu, India
Correspondence:
[*]
Corresponding author. L. Ramachandran, Department of Electronics and Communication Engineering, E.G.S. Pillay Engineering College, Nagapattinam, Tamilnadu 611002, India. E-mail: [email protected].
Abstract: White Spot Syndrome Virus (WSSV) is a major virus found in shrimp that causes huge economic loss in shrimp farms. A selective diagnostic approach for WSSV is required for the early diagnosis and protection of farms. This work proposes a novel recognition method based on improved Convolutional Neural Network (CNN) namely Dense Inception Convolutional Neural Network (DICNN) for diagnoses of WSSV disease. Initially, the process of data acquisition and data augmentation is carried out. The Inception structure is then used to improve the performance of multi-dimensional feature extraction. As a result, the proposed work has the highest accuracy of 97.22% when compared to other traditional models. The proposed work is targeted to Litopenaeus Vannamei (LV), and Penaeus Monodon (PM) diversities for major threats detection of White Spot Syndrome (WSS). Performance metrics related to accuracy have been compared with other traditional models, which demonstrate that our model will efficiently recognize shrimp WSSV disease.
