Özet:

Abstract The study looks at how traditional Bangus (chanos chanos) farming methods in the Philippine province of Pangasinan can be adapted to incorporate cutting-edge technology and predictive analytics. The study's objective is to boost the local aquaculture sector's production, efficiency, and sustainability in order to promote economic development and food security in the long run. The study starts off by thoroughly reviewing the body of prior research on SMART farming, predictive analysis, and the situation of Bangus farming in the area. This literature review lays the groundwork for the study by highlighting the possible advantages and difficulties of implementing modern technologies in aquaculture. Then, primary data is gathered through document analysis from the reliable source of the internet. These measure if there is a need to adapt SMART farming ideas for improvements. The study also gathers crucial historical information on past climatic trends, water quality, and other pertinent environmental aspects that affect Bangus cultivation. This data serves as the foundation for predictive modeling, which projects future outcomes under various scenarios utilizing cutting-edge analytical tools. Predictive analysis tries to improve Bangus farming enterprises' overall efficiency by optimizing feeding practices, reducing disease outbreaks, and anticipating market demand. The study's findings provided insight into the Bangus farmers of Pangasinan's existing level of knowledge and readiness about SMART farming techniques. Additionally, the predictive models provide useful insights into the potential advantages and difficulties of deploying SMART agricultural technologies in this particular area. The study's conclusions have important results for agricultural extension agencies, legislators, and technology providers because they can guide targeted interventions and support systems to encourage the adoption of SMART farming in the neighborhood aquaculture industry. In the context of food production and rural development, the research also contributes to a larger conversation about sustainable agriculture, technological integration, and predictive analytics.

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