AIoT-Based Shrimp Larvae Counting System Using Scaled Multilayer Feature Fusion Network

Yi-Kuan Hsieh, Jun-Wei Hsieh, Wu-Chih Hu, Yu-Chee Tseng

Abstract: The Artificial Intelligence of Things (AIoT) plays a crucial role in shrimp farming by enabling automated and real-time monitoring of shrimp counting, especially larvae. With this counting information, proper feeding control can be maintained by equipping IoT devices to capture real-time data on water quality, temperature, and other environmental factors, ensuring healthy shrimp growth and increasing production. Taking advantage of the power of AIoT, this article proposes a scaled multilayer feature fusion network (SMILES-Net) to allow farmers to remotely and automatically manage the counting of shrimps, effectively reducing the need for manual labor, and enabling swift interventions to sustain shrimp well-being. Since shrimp larvae are extremely small, we frame this counting problem as a density prediction problem, where the sum of the constructed density map is the total number of shrimps predicted. The pooling operation used in convolutional neural networks scales each feature map to 1/4 and causes the rich features of small shrimps to disappear dramatically. To tackle this truncation problem, we propose a novel synthetic fusion module (SFM) and an intrablock fusion module (IFM) to create a smoother scale space, generating better heat maps with fine-grained features for shrimp counting. Furthermore, we introduce a lightweight version of SMILES-Net (LW-SMILES-Net) that enables real-time shrimp counting without compromising accuracy. This method is evaluated on different data sets for shrimp counting and outperforms all State-of-The-Art methods. Overall, integrating SMILES-Net with IoT devices can provide a powerful solution for real-time shrimp larvae counting in shrimp farming, contributing to sustainable increases in shrimp production. The data set is available at https://github.com/Naughty725/shrimp.