Trends, challenges and future directions in deep learning for citrus leaf disease recognition: a survey

Abstract

Citrus farming is a vital component of global agriculture that faces continuous challenges from pathogens and environmental stressors. A major difficulty for farmers is the early and accurate identification of citrus leaf diseases, which is essential to prevent yield loss and maintain fruit quality. In recent years, researchers have focused on automating the citrus disease recognition process using computer vision and machine learning techniques. More importantly, the advancements in deep learning have substantially accelerated progress in citrus leaf disease recognition by enabling robust and scalable feature extraction from complex imagery. Despite these advancements, considerable challenges remain in automatic citrus disease detection, and researchers continue to strive for improved generalisation, in-field adaptability, and suitability for low-power agricultural devices. Distinguishing itself from existing reviews, this survey provides a unique, taxonomically aligned evaluation that addresses the visual mimicry between pathogenic symptoms and pest-induced damage. This survey provides a comprehensive overview of benchmark citrus leaf disease datasets, preprocessing methods, deep learning architectures, limitations, recommendations, and future research directions. It offers a detailed comparative analysis of various deep learning models categorised by task type and acquisition environment, highlighting their performance across different datasets, feature extraction techniques, and imaging conditions. Furthermore, this study outlines a novel strategic roadmap for integrating Generative AI and Vision-Language Models (VLMs) to enhance diagnostic interpretability. The primary objective of this survey is to aid researchers in understanding state-of-the-art deep learning-based methods for recognising citrus leaf diseases, support farmers and agronomists in early disease management, address current challenges, provide recommendations and outline future directions for sustainable citrus production.