Boosted Query Expansion for Agricultural Decision Support: A Hybrid Framework Combining Case-Based Reasoning, Fuzzification, and Machine Learning

Abstract

This framework, “BQ-CBRS,” Hybrid Bigger Query-Case Based Reasoning System model, is the first of its kind to unite contextual embedding-based query expansion (using BERT), IndRNN-based semantic similarity scoring, the fuzzification of uncertain parameters, and XGBoost classification within one application to support precision agriculture. Some of the steps include query preprocessing, generating contextual embeddings utilizing a pre-trained method (for example, BERT), semantic similarity scoring using IndRNN, and expanding the query by adding top-ranked search terms. Fuzzification will acknowledge any uncertainty present in the data, while XGBoost will enhance the predictive power and efficacy of the present work. The proposed methodology consists of query preprocessing, contextual representations using pre-trained models (like BERT), calculating a similarity score through IndRNN, and expanding the query according to the top-scoring terms. Fuzzification will address the uncertainty in the data, and XGBoost will enhance prediction accuracy and efficiency. The Crop Recommendation Dataset consists of parameters, such as nitrogen, phosphorus, pH, temperature, and rainfall. The present model has low accuracy and low mean square error (MSE). Also, it improves over traditional approaches. The model will utilize precision agriculture technology to link historical cases and improve approaches for more effective resource management and advancing sustainable farming. This combination of symbolic reasoning and deep learning in the agriculture domain is novel, establishing a generalizable framework for intelligent decision support in dynamic and uncertain situations.