1 © MAT Journals 2024. All Rights Reserved Journal of Statistics and Mathematical Engineering e-ISSN: 2581-7647, Vol. 10, Issue 1 (January – April, 2024) pp: (1-7) A Comprehensive Survey of Fuzzy Logic Utilization in Different Agricultural Sectors Mukesh Kumar Sinha 1* , Rajesh Kumar Tiwari 2 1 Research Scholar, 2 Associate Professor, Department of Mathematics, Binod Bihari Mahto Koyalanchal University, Dhanbad, Jharkhand, India * Corresponding Author: sinhamukesh.dazy@gmail.com Received Date: February 20, 2024; Published Date: March 01, 2024 Abstract Fuzzy logic (FL) has emerged as a pivotal component within the realm of Expert Systems, demonstrating its efficacy in addressing real-life challenges that had previously eluded resolution. Its versatile applications span a multitude of domains, with this paper specifically delving into the successful utilization of fuzzy logic methods to tackle various agricultural issues. This comprehensive review explores instances where fuzzy logic has been seamlessly integrated into expert systems to provide innovative solutions within the field of agricultural sciences. The examined applications encompass a spectrum of challenges encountered in agriculture, showcasing the adaptability and effectiveness of fuzzy logic in addressing complex issues. This paper serves not only as an insightful examination of existing applications but also as a valuable contribution to the literature survey, laying the groundwork for future research endeavours. Particularly, it provides a foundational reference for those undertaking research aimed at developing expert systems tailored for specific crops in designated regions of our country. As a part of the broader landscape, this study acts as a cornerstone, offering a starting point for further investigations and advancements in the intersection of fuzzy logic and agricultural sciences. Keywords- Agriculture, Artificial Intelligence (AI), Expert system, Fuzzy logic, Fuzzy rules, Soft computing INTRODUCTION One area of artificial intelligence is expert systems designed to replicate human decision- making processes. These programs are typically tailored to specific domains, addressing particular problems. The knowledge base is a crucial component of an expert system, containing the information, rules, facts, and necessary for problem-solving and decision-making, essential for the system's functionality, and contains facts, rules, and heuristics organized in if-then structures, known as "production rules." The knowledge engineer, akin to a programmer, transforms intricate knowledge into the knowledge base. The inference engine, a crucial module, deduces new knowledge from the existing base, utilizing a reasoning mechanism to locate relevant information. The user interface serves as the communication window between the end-user and the expert system. Numerous expert systems have emerged in diverse fields such as medicine (e.g., MYCIN), civil engineering (PREDICTE), automobile engineering (ALTREX), and mineral exploration (PROSPECTOR). Agriculture has also seen the development of expert systems aimed at maximizing yields and minimizing losses. Rule-based systems, often integrated with fuzzy logic, have proven effective in achieving optimal agricultural yields. Leveraging the benefits of fuzzy logic has proven instrumental in handling sensor data for our decision-making processes. Fuzzy logic becomes crucial when technology needs to make decisions based on partial or variable values, deviating from the traditional Boolean logic of true/false or 1/0. Integrating fuzzy logic into this paper has significantly enhanced its efficiency navigate and analysing data with a more nuanced approach.