Advancements in Wheat Disease Detection: A Comprehensive Survey of Techniques, Challenges

Diseases that affect plants are the main cause of production in addition to financial losses, and the analysis of plant diseases early is crucial to better crop production since it allows for the implementation of effective control measures. This survey paper provides a thorough exploration of recent advancements in wheat disease detection, offering a comprehensive overview of the various techniques and methodologies employed in this critical domain. The survey covers AI techniques use for disease detection and classification, types of disease in wheat, spectrum of detection methods, including traditional approaches and cutting-edge technologies such as machine learning, image processing. The paper discusses available tactics and outlines the variety of techniques available for wheat plant disease diagnosis. Challenges in wheat disease detection, such as variability in environmental conditions and the need for real-time monitoring, are discussed in depth. The paper concludes by highlighting emerging trends and proposing potential directions for future research in the ongoing quest to enhance wheat disease detection methods.