Past, present and future of deep plant leaf disease recognition: A survey

Agriculture is the foundation of life that faces numerous daily attacks from nature and living organisms. A major challenge for farmers is timely plant disease identification, which is crucial to prevent productivity losses and the production of poor-quality products. Researchers have recently been focusing on automating the plant leaf disease recognition process using computer vision and machine learning techniques. More importantly, the recent developments in deep learning have significantly advanced the field of plant leaf disease recognition. Regardless of these advancements, significant challenges remain in automatic leaf disease recognition, and researchers are continuing to seek better performance, in-field applicability, and compatibility with resource-constrained devices. This survey provides a comprehensive overview of real-world and laboratory datasets, feature extraction methods, deep learning frameworks, limitations, recommendations, and future directions for deep plant leaf disease recognition. It offers a detailed comparative analysis of various deep learning models applied to different datasets, preprocessing techniques, and data collection methods. This work also highlights the need for an ideal dataset and explores future directions like the Internet of Things integration, Explainable AI, and Smart Farming, which previous surveys have not covered. The primary aim of this survey is to assist researchers in understanding state-of-the-art plant leaf disease recognition techniques, support farmers in the field of plant pathology, address limitations, provide recommendations and outline future directions. • Critically compares existing surveys on plant leaf disease recognition. • Analyze 18 widely used datasets and ideal dataset characteristics. • Explores preprocessing, feature extraction, and classification to improve accuracy. • Review deep learning methods, their impact, and dataset-specific analysis. • Analyze limitations and offer suggestions for future work and applications.