Data cleaning is a process that precedes data mining. Particularly, in our dataset on pesticidal plant use, several types of anomalies were identified, ranging from incorrect values to a lack of data susceptible of causing users to draw wrong conclusions during its exploration. Literature presents three methods based on Formal Concept Analysis (FCA), i.e. implication rules computation, association rules computation, and attribute exploration, that may allow the detection and correction of anomalies. This paper evaluates 30 FCA-based software and their apposite features to the development of an anomaly detection and correction method applicable to our dataset. Results show that only ConExp and its reimplementations provide all three methods. Since the data model on plant use is relational but ConExp only allows formal contexts as input, this paper concludes on the importance of integrating Relational Concept Analysis (RCA) with ConExp in future work.