ADVANCING SOIL NUTRIENT MANAGEMENT IN AGRICULTURE WITH INTEGRATING MACHINE LEARNING AND FUZZY LOGIC APPROACHES

Abstract

Agriculture faces multifaceted challenges that affect food security, sustainability, and economic growth. Soils
serve as the foundation for food production. However, indiscriminate use of fertilizers has led to soil pollution
and degradation, necessitating integrated nutrient management practices. Machine learning (ML) emerges as a
transformative technology in agriculture, offering solutions across various domains. Fuzzy logic, with its ability
to handle uncertainty and imprecision, complements machine learning in agricultural decision support systems.
This paper explores the utilization of fuzzy logic for soil nutrient categorization and decision-making, along with
the analysis of machine learning models for predicting soil fertility and crop yield and also examines the
recommendation of suitable crops and fertilizers based on soil characteristics. These models leverage diverse
algorithms such as K- Nearest Neighbours, Random Forest, Naive Bayes, Support Vector Machine, Decision
Trees and ensemble classifiers to offer accurate predictions and recommendations. The integration of ML and
fuzzy logic in agriculture represents a potential approach to tackling agricultural challenges, advancing
sustainable soil management practices, and elevating crop productivity.