Taiwan's fishery and aquaculture development welcomes new prospects, but the technical threshold for fish farming remains to be overcome
Taiwan entered an aging society in ROC year 105, and with the compounding effect of a declining birth rate, the problem of insufficient agricultural labor has become imminent. Therefore, in ROC year 106 the Council of Agriculture actively promoted Smart Agriculture 4.0, hoping that through innovations in wireless communication and sensors, farmers could be helped to face the management challenges brought by climate change even with limited manpower. The team from the Fisheries Research Institute recently joined hands with the Highlight Team to demonstrate research and development results, including: indoor facility aquaculture systems, marine Vibrio detection systems, and unmanned aerial vehicle water quality monitoring vehicles, exchanging new concepts and new technologies with the aquaculture industry.
Tseng Chen-te, Chief Secretary of the Fisheries Research Institute, stated that in addition to facing population problems, the traditional aquaculture industry, under the open-air farming environment that most operations in the country use, must also face the risks of fish diseases and medication caused by climate change. Therefore, it is hoped that through the "big data, artificial intelligence, Internet of Things" technologies of big data, AI, and IoT, fishermen can be assisted in implementing effective and precise production models under limited land and water resources.
Wang Yu-chun, Assistant Research Fellow of the Planning and Information Division of the Fisheries Research Institute, explained the institute's development achievements over the past 3 years: the smart grouper aquaculture IoT system, the marine Vibrio detection system, and the unmanned aerial vehicle water quality monitoring vehicle. Among these, in the smart grouper aquaculture IoT system, the Fisheries Research Institute established an indoor grouper aquaculture model plant in Qigu, installing water quality monitoring equipment in the aquaculture ponds. The system will activate aeration equipment and shade nets in response to specific information, responding immediately to conditions in the pond; at the same time, through an underwater image recognition system, it records the activity level of the fish school and controls the feeding frequency of the automatic feeder. When it finds that the fish school no longer actively moves because feed has been dispersed, it determines that the feed is sufficient and stops supplying it, effectively controlling feed costs and reducing water quality pollution. The relevant data will ultimately return to the "aquaculture decision visualization subsystem." Managers can grasp the fishery conditions in the pond or the operating information of equipment through the monitoring platform, so they do not need to go to the fish pond to make aquaculture decisions with the assistance of the IoT.
However, Tseng Chen-te also admitted that although technology can assist decision-making conveniently, in practice there is still a gap to work on in promoting the concept and implementing machine decision-making, because older aquaculture operators rely on personal experience and are relatively conservative toward new technology and techniques. Therefore, the Fisheries Research Institute will try to hold more technical briefings to promote them to younger aquaculture farmers; in addition, machine decision-making involves data collection, interpretation, and writing parameters before machines can execute effectively. Interpreting useful data and establishing a parameter system are the keys to making "big data, AI, and IoT" technologies work. Therefore, the data collected by the Fisheries Research Institute in grouper aquaculture areas and the parameter system established will also be opened in the future for aquaculture operators to reference and apply, allowing the data to play a role.
