AIoT Precision Feeding Management System

Abstract

Different fish species and different growth stages require different amounts of fish pellets. Excessive fish pellets increase the cost of aquaculture, and the leftover fish pellets sink to the bottom of the fish farm. This causes water pollution in the fish farm. Weather changes and providing too many or too little fish pellets affect the growth of the fish. In light of the abovementioned factors, this article uses the artificial intelligence of things (AIoT) precision feeding management system to improve an existing fish feeder. The AIoT precision feeding management system is placed on the water surface of the breeding pond to measure the water surface fluctuations in the area of fish pellet application. The buoy, with s built-in three-axis accelerometer, senses the water surface fluctuations when the fish are foraging. Then, through the wireless transmission module, the data are sent back to the receiver and control device of the fish feeder. When the fish feeder receives the signal, it judges the returned value to adjust the feeding time. Through this system, the intelligent feeding of fish can be achieved by adjusting the amount of fish pellets in order to reduce the cost of aquaculture.

Artificial Intelligence-Based Aquaculture System for Optimizing the Quality of Water: A Systematic Analysis

“…Intelligent equipment based on IoT, 5G connections, and AI, with its algorithms and computing, will aim to find and solve aquaculture problems. Therefore, optimized module designs that will serve as construction for a smart fish farm [26][27][28][29][30][31] are proposed. Figure 8 shows a typical aquaculture system with actuators, sensors (turbidity, pH, salinity, dissolved oxygen, etc.…”

“…Fishpond Wireless X X Cloud [29] Fish farm Lora Wan X X Cloud [30] Fish farm Wi-Fi/LoRa/5G X X X X Cloud X = Apply.…”

LPAI—A Complete AIoT Framework Based on LPWAN Applicable to Acoustic Scene Classification Scenarios

“…They support the uploading of all target domain data regardless of resources. Chiu et al [ 19 ] propose an AIoT precision feeding management system based on LoRa network to improve the existing automatic feeding system in the mark. Chang et al [ 20 ] proposed an intelligent assistive system based on wearable smart glasses and smart cane.…”

“…Huawei Technologies Co. et al [ 16 , 17 , 18 ] proposed mobile device frameworks designed for high-dimensional data such as images, which are not suitable for the more restrictive LPWAN. Other frameworks, including Chiu et al [ 19 ], Zualkernan et al [ 9 ] and Chang et al [ 20 ] are based on LPWAN; however, these studies do not consider updating the intelligence model according to the environment. They have still not implemented edge intelligence for LPWAN in this sense.…”