Wheat Disease Detection: Bridging the Gap with Deep Learning Approaches

Wheat is a vital crop for global food security, yet its production faces significant threats from various diseases, leading to substantial yield losses. Traditional detection methods, such as manual scouting and laboratory analysis, are often labour-intensive and inefficient. The advent of deep learning techniques has revolutionized agricultural disease detection, offering more efficient and accurate solutions. We provide a comprehensive overview of common wheat diseases, methodologies, and datasets utilized in recent studies. A critical analysis of the literature reveals performance metrics of various deep learning models compared to traditional methods, Furthermore, the review suggests future research directions, emphasizing the integration of IoT technologies for real-time monitoring and the use of advanced sensors for improved detection accuracy. This synthesis aims to enhance strategies for effective disease management in wheat cultivation.