The amount of particles and organic matter in wash-waters and effluent from the processing of fruits and vegetables determines whether they need to be treated to fulfil regulatory standards for their intended use. This research proposes a novel technique in photovoltaic cell-based renewable energy in saline water analysis using the oxidation process and deep learning techniques. Here, the saline water oxidation is carried out based on photovoltaic cell-based renewable and saline water analysis is done using Markov fuzzy-based Q-radial function neural networks (MFQRFNN). The plan is entirely web-oriented to enable better control and effective monitoring of water consumption. This monitoring makes use of a communication system that collects data in the form of irregularly spaced time series. Experimental analysis has been carried out based on water salinity data in terms of accuracy, precision, recall, specificity, computational cost, and kappa coefficient.

水果和蔬菜加工过程中的洗涤水和废水中的颗粒和有机物含量决定了它们是否需要经过处理以满足其预期用途的监管标准。这项研究提出了一种利用氧化过程和深度学习技术在盐水分析中基于光伏电池的可再生能源的新技术。在这里，盐水氧化是基于基于光伏电池的可再生和盐水分析进行的，使用基于马尔可夫模糊的Q-径向函数神经网络 (MFQRFNN)。该计划完全以网络为导向，以便更好地控制和有效监测用水量。这种监测利用了一个以不规则间隔时间序列的形式收集数据的通信系统。基于水盐度数据在准确性、精确度、召回率、特异性、计算成本和kappa系数方面进行了实验分析。