Water quality monitoring plays a significant part in the transition towards intelligent and smart agriculture and provides an easy transition to automated monitoring of crucial components of human daily needs as new technologies are continuously developed and adopted in agricultural and human daily life (water). For the monitoring and management of water quality, this effort, however, requires reliable models with accurate and thorough datasets. Analyzing water quality monitoring models by utilizing sensors that gather water properties during live experiments is possible due to the necessity for precision in modeling. To convey numerous conclusions regarding the concerns, issues, difficulties, and research gaps that have existed throughout the past five years (2018–2022), this review article thoroughly examines the water quality literature. To find trustworthy peer-reviewed publications, several digital databases were searched and examined, including IEEE Xplore^®, ScienceDirect, Scopus, and Web of Science. Only 50 articles out of the 946 papers obtained, were used in the study of the water quality monitoring research area. There are more rules for article inclusion in the second stage of the filtration process. Utilizing a real-time data acquisition system, the criteria for inclusion for the second phase of filtration looked at the implementation of water quality monitoring and characterization procedures. Reviews and experimental studies comprised most of the articles, which were divided into three categories. To organize the literature into articles with similar types of experimental conditions, a taxonomy of the three literature was created. Topics for recommendations are also provided to facilitate and speed up the pace of advancement in this field of study. By conducting a thorough analysis of the earlier suggested methodologies, research gaps are made clear. The investigation largely pointed out the problems in the accuracy of the models, the development of data-gathering systems, and the types of data used in the proposed frameworks. Finally, by examining critical topics required for the development of this research area, research directions toward smart water quality are presented.

Ever since COVID-19 was declared a pandemic, governments around the world have implemented numerous phases of lockdown measures to curb the spread of the virus. These lockdown tactics manifest themselves in the form of widespread fear and panic driven by social media discussions. Given that individuals hold diverse opinions about these lockdown measures during and after their completion, positive and negative lockdown-related discussions should be differentiated to further understand the major related issues and to make appropriate messaging and policy choices in the future. We conduct a sentiment analysis (SA) of COVID-19-lockdown-related tweets by using different machine learning (ML) classifiers and then evaluate their performance before and after using the synthetic minority oversampling technique (SMOTE). This research is performed in five phases, starting with data collection and followed by pre-processing the dataset, preparing the dataset by annotation, applying SMOTE and using ML classifiers. We observe an improvement in accuracy ( ) as confirmed by the Matthew correlation coefficient ( ) across most classifiers, except for the k-nearest neighbour (KNN), whose Acc decreased from 0.82 to 0.59 and MCC decreased from 0.544 to 0.279 before and after SMOTE was applied. Despite the potential of SMOTE with some classifiers, this technique cannot be considered an ultimate solution, especially with other classifiers and datasets. The study provides insights into the need to evaluate and benchmark the integration of data balancing approaches with ML classifiers in addition to considering additional metrics, such as MCC, for binary classification problems, especially in SA.