Information Processing and Retrieval on WSNs for Plant Monitoring Using Natural Language

Technology has improved so that everyone can obtain the information they need from a single prompt. Most data is contained in a database, and exploring the data we need in these databases requires the language and the database, which most users do not know. We created an NLP system that allows farmers to access their databases’ information with simple prompts and queries. By entering natural language queries into the system, they are converted to structured language queries to access the database information. We used pre-processing techniques such as tokenization, lemmatization, parts of speech tagging, and parsing on the natural language, and a rule-based algorithm was created to translate the query. 106 queries from random respondents were used to test the algorithm. The developed system showed an accuracy of 91.51%. We converted simple queries to structured language queries and provided output from the database.