Rapid detection of Penaeus vannamei diseases via an improved LeNet

Shrimp disease is a greatly important factor in the culture of Penaeus vannamei , the shrimp species with the highest yield in the world aquaculture industry. Hepatopancreatic necrosis disease (HPND, 37%), red body disease (RBD, 26%), and whitish muscle disease (WMD, 18%) were the most common Penaeus vannamei diseases, all of which are usually classified and identified by two kinds of detection (manual detection and germs purifying method). Most of these detections suffer from the class low accuracy, too complex, or too costly. In this study, we tackle this situation with an improved LeNet, which includes modifying model parameters and computational methods. More particularly, this study proposes a convolutional neural networks (CNN) model that is based on LeNet network framework and can reduce parameters and accelerate calculation. To offer improvements in classification and identification, we increase the number of feature maps. Meanwhile, to firstly take denoise and then strengthen characteristic in pretreatment, HSV color space conversion and Gaussian noise with a level of 20 are led into. We conclude that the model generates the precision at about 96.1 percent when the weight parameter learning rate is 0.002 and the number of iterations is 120 after being trained and validated. The study has made tremendous progress in the rapid detection of Penaeus vannamei diseases by providing an effective technological path and suggesting the possibility of realizing early disease warnings in future works.