Analysis of Machine Learning Methods for Water Quality Evaluation of Penaeus Vannamei.

In global aquaculture, Penaeus vannamei stands out due to its immense economic importance. Water quality, being pivotal for its successful cultivation, demands precise evaluation techniques. This research undertook a meticulous systematic review and, leveraging data mining, crafted a rich dataset of 50,000 water quality samples pertinent to Penaeus vannamei. Diving deep into machine learning, we assessed four key algorithms: decision tree, Bayesian, k-nearest neighbor, and support vector machine, each tailored for intricate multi-feature multi-classification challenges. Of these, the Gaussian Parsimonious Bayes-based model was distinguished by its superior accuracy and efficiency. This study successfully applied machine learning techniques to develop a reliable and efficient water quality evaluation model for Penaeus vannamei farming, offering a scientific tool for the aquaculture industry and facilitating more efficient, scientifically informed farming management. This research contributes an innovative scientific approach and theoretical foundation for the sustainable growth of the aquaculture industry.