Abstract
Shrimp productivity has greatly increased and has high-economical values along with its impact on the GDP of our country. Considering these impact on the farmers, this paper proposes a convolutional neural network model, which assists and the farmer in understanding the symptoms that the shrimp exhibits during critical conditions of any virus effects or decrease in the dissolved oxygen levels in the water. The proposed system also synthetizes a set of image by uses of image data augmentation techniques for obtaining a considerable set of images. These images are used as the training images for the model. The custom shrimp detection systems use faster_rcnn_inception_v2_coco model, which effectively detects the shrimp and represents them through boundary boxes. Whenever surfacing kind of symptoms are exhibited, thus triggering the farmer to identify the risk factor and take the counter measures.
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Tej Varma, G., Adusumalli, S.K. (2023). Shrimp Surfacing Recognition System in the Pond Using Deep Computer Vision. In: Rao, B.N.K., Balasubramanian, R., Wang, SJ., Nayak, R. (eds) Intelligent Computing and Applications. Smart Innovation, Systems and Technologies, vol 315. Springer, Singapore. https://doi.org/10.1007/978-981-19-4162-7_21
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