Water quality (WQ) is a key factor that affects harvest outcome from freshwater ponds. Irregular or aperiodic variations of different WQ variables can influence the growth, survival, and yield of aquatic livestock in the ponds. In this research, WQ and harvest data collected from an Australian prawn farm over a whole grow-out season is used to investigate how the variations of WQ influence the harvest outcome of prawns from the ponds. We present a set of approaches based on machine learning to: (i) understand the effect of five WQ variables in differentiating high and low performing ponds (in terms for harvest performance); and (ii) identify how the variations in these WQ variables over the grow-out season contributed to final harvest outcome (growth and yield). To develop the ponds classification approach, we apply eight different machine learning classifiers: neural networks, support vector machine, k-nearest neighbours, logistic regression, gaussian naïve bayes, decision tree, random forest, and AdaBoost. To identify the driving factors (in terms of variations of WQ) that affect growth and yield of aquatic livestock in ponds, we apply three feature selection methods: mutual information, correlation-based feature selection, and ReliefF. Results demonstrate that dissolved oxygen, salinity, and temperature are the three WQ variables that have the greatest influence on overall harvest performance of the ponds. Changes in dissolved oxygen and salinity in the last quarter of the grow-out season, and variations of temperature immediately after stocking contributed the most to differentiate the high and low performing ponds.

水质 (WQ) 是影响淡水池塘收获结果的关键因素。不同 WQ 变量的不规则或非周期性变化会影响池塘中水生牲畜的生长、存活和产量。在这项研究中，使用澳大利亚对虾养殖场在整个养成季节收集的 WQ 和收获数据来研究 WQ 的变化如何影响池塘对虾的收获结果。我们提出了一组基于机器学习的方法：(i) 了解五个 WQ 变量在区分高性能和低性能池塘方面的影响（就收获性能而言）；(ii) 确定这些 WQ 变量在养成季节的变化如何影响最终收获结果（生长和产量）。开发池塘分类方法，我们应用了八种不同的机器学习分类器：神经网络、支持向量机、k-最近邻、逻辑回归、高斯朴素贝叶斯、决策树、随机森林和 AdaBoost。为了确定影响池塘中水生牲畜生长和产量的驱动因素（就 WQ 的变化而言），我们应用了三种特征选择方法：互信息、基于相关性的特征选择和 ReliefF。结果表明 和救济F。结果表明 和救济F。结果表明溶解氧、盐度和温度是对池塘整体捕捞性能影响最大的三个 WQ 变量。养成季节最后一个季度中溶解氧和盐度的变化，以及放养后立即的温度变化对区分高性能和低性能池塘的贡献最大。