Utilizing Fuzzy Logic in Precision Agriculture: Techniques for Disease Detection and Management

Abstract

The agricultural sector is paramount in meeting global food demands, necessitating research efforts to enhance productivity, improve food quality, and optimize profitability. Central to this endeavour is equipping farmers with efficient and affordable information and control technologies. Plant disease identification is pivotal for effective disease management and enhancing product quality. Various image processing and soft computing methods are employed for the early detection and diagnosis of plant diseases. Fuzzy logic, adept at handling fuzzy image data, is extensively discussed in the paper concerning precision agriculture, highlighting its efficacy in addressing agricultural challenges. Farmers can improve the accuracy and efficiency of disease detection and management by employing fuzzy logic techniques in precision agriculture, leading to higher crop yields, reduced input costs, and sustainable agricultural practices. Utilizing fuzzy logic in precision agriculture for disease detection and management involves leveraging the flexibility and interpretability of fuzzy logic systems to handle the inherent uncertainties and imprecisions in agricultural data. This paper explores applying fuzzy logic techniques in precision agriculture for disease detection and management. We discuss the theoretical foundations of fuzzy logic and its practical implementation in agricultural systems. Various methodologies and strategies are examined, including fuzzy membership functions, rule-based systems, fuzzy inference systems, and data fusion techniques. Case studies and examples are provided to illustrate the effectiveness of fuzzy logic in disease detection and management. These include applications in crop monitoring using remote sensing data, dynamic thresholding for disease risk assessment, and feedback control systems for automated disease management.