International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 17, No. 6, 8 Dec. 2025
Cover page and Table of Contents: PDF (size: 869KB)
DFI-ADR: Fuzzy Logic-Driven Information Retrieval and Machine Learning for Environmental and Crop Prediction to Optimize Farming Decisions
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Author(s)
Surabhi Solanki
1,2,*
Seema Verma
3
Index Terms
Information Retrieval, Fuzzy Logic, Query Expansion, Recall, Precision, F1-Measure, Indrnn
Abstract
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.
Cite This Paper
Surabhi Solanki, Seema Verma, "DFI-ADR: Fuzzy Logic-Driven Information Retrieval and Machine Learning for Environmental and Crop Prediction to Optimize Farming Decisions", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.17, No.6, pp. 48-59, 2025. DOI:10.5815/ijieeb.2025.06.04
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