Deep Learning for Plant Disease Detection: Trends and Challenges

Abstract

Advancements in computer vision and deep learning have significantly enhanced plant disease detection capabilities. This paper synthesizes recent developments, examining a range of methodologies from classical image processing to contemporary deep learning architectures, including convolutional neural networks, ensemble methods, and segmentation-based approaches. Our analysis of over 50 studies reveals that, while deep learning models demonstrate high accuracy in controlled settings, their performance often diminishes under real-world conditions due to factors such as data imbalance, limited dataset diversity, and computational constraints. We provide a comprehensive taxonomy categorizing these approaches by model architecture, task type, and data source, and critically evaluate their strengths and limitations. Furthermore, we identify key challenges, including the integration of multimodal data, model interpretability, and the need for robust real-time deployment on mobile platforms. The paper concludes with recommendations for future research directions aimed at bridging the gap between laboratory success and practical agricultural applications, ultimately contributing to improved crop management and sustainability.