Big Data Technology Architecture Proposal for Smart Agriculture for Moroccan Fish Farming

Abstract

As the global population increases rapidly, so does the need for fishing products. Aquaculture is well-developed in Asian countries but is underdeveloped in countries that share Morocco's climate. To meet the rising demands for aquaculture production, it is vital to embrace new digital strategies to manage the massive amount of data generated by the aquaculture environment. By employing Big Data methodologies, aquaculture activity is handled more effectively, resulting in increased production and decreased waste. This phase enables fish farmers and academics to obtain valuable data, increasing their productivity. Although Big Data approaches provide numerous benefits, they have yet to be substantially implemented in agriculture, particularly in fish farming. Numerous research projects investigate the use of Big Data in agriculture, but only some offer light on the applicability of these technologies to fish farming. In addition, no research has yet been undertaken for the Moroccan use case. This study aims to demonstrate the significance of investing in aquaculture powered by Big Data. This study provides data on the situation of aquaculture in Morocco in order to identify areas for improvement. The paper then describes the adoption of Big Data technology to intelligent fish farming and proposes a dedicated architecture to address the feasibility of the solution. In addition, methodologies for data collecting, data processing, and analytics are highlighted. This article illuminates the possibilities of Big Data in the aquaculture business. It demonstrates the technological and functional necessity of incorporating Big Data into traditional fish farming methods. Following this, a concept for an intelligent fish farming system based on Big Data technology is presented.

Optimum eicosapentaenoic acid + docosahexaenoic acid levels for farmed Atlantic salmon: closing the gap between science and commercial practice

“…Although this was demonstrated here for EPA + DHA levels, the BD approach could be utilized for other feed ingredients and functional additives, as well as other zootechnical and wellbeing indices. Moreover, as sensors and smart technologies expand to acquire more data related to the farm environment, feed quality, and productivity, more routes will open to model and forecast patterns related to growth, survival, and FCR, which are the most essential factors in aquaculture (Benjelloun et al, 2022), using BD technology and mathematical models. Such analytical methods based on expanded datasets also have the advantage of profiling and clustering farms according to their unique environmental (site, temperature, location) and operational (feed type and size, animal genetics) features to design the best ROI model.…”