This paper proposes DFI-ADR (Dynamic Fuzzy Information System with Agriculture Decision Retrieval) aimed at improving agricultural decision-making through case-based reasoning and precise information retrieval. This approach uses fuzzy logic and machine learning techniques, such as IndRNN, to compute similarity scores between historical agricultural cases and new queries. This enables dynamic classification of cases as "distinct," "similar," or "highly comparable" based on fuzzy membership values. These values significantly enhance the accuracy of decisions related to agricultural factors like crop yield, soil quality, and irrigation. The methodology outperforms traditional methods in terms of accuracy, recall, and precision, proving highly effective for agricultural analysis and decision-making. In experiments with the Agriculture Dataset Karnataka, DFI-ADR achieved an accuracy of 95%, a precision of 100%, and an F1-score of 94.74%, significantly outperforming traditional methods by a margin of 10-15% across these metrics. These values demonstrate its effectiveness for agricultural analysis and decision-making.

This paper presents a comprehensive survey of QE techniques in IR. Core techniques, employed data sources, and methodologies used in the process of query expansion are discussed. The output study highlights four main steps concerned with expanding queries: steps related to preprocessing of data sources and term extraction, calculation of weights and ranking of terms, selection of terms, and finally expansion. The most important findings are that only effective text normalization and removal of stopwords provide a real platform for performing QE. The introduction of contextually relevant terms significantly enhanced relevance feedback and thesaurus-based WordNet expansion techniques. They have been shown to significantly improve retrieval effectiveness as has been realized from various experiments conducted over years now. It also uses the manual query expansion techniques and discusses several automated ways in order to improve retrieval effectiveness. This work, by reviewing the related literature and methodologies, gives an overview of how the techniques of query expansion have been evolving with time and achieved better results in IR systems. The survey offers a valuable resource for researchers and practitioners in information retrieval, shedding light on the advancements, challenges, and future directions in query expansion research.