Disease diagnosis in banana leaves: a review on AI powered techniques

Banana leaf diseases pose a significant global threat to agricultural productivity and economic stability, substantially reducing the quality and quantity of yield. Given the critical role of banana leaves in the overall growth and development of banana plants, their susceptibility to a wide range of diseases represents a pressing concern. This review systematically explores recent advancements in diagnosing and classifying banana leaf diseases through Artificial Intelligence (AI)-based techniques. Key methodologies reviewed include image preprocessing, machine learning, deep learning, and transfer learning. Particular emphasis is placed on lightweight deep learning architectures, which offer the advantages of high diagnostic accuracy, rapid processing, and minimal computational requirements, making them suitable for deployment in resource-constrained environments. The presence of numerous banana cultivars, each exhibiting subtle variations in leaf morphology and pigmentation, further complicates the detection process, underscoring the need for adaptable and robust AI models. The review also highlights data acquisition, preprocessing strategies, and dataset weaknesses, along with evaluation metrics used to assess model performance. Finally, it identifies existing challenges and research gaps in current approaches with the brief case study by synthesizing these insights. The review provides a comprehensive understanding of AI-powered solutions for the effective detection and classification of banana leaf diseases and their potential practical applications in precision agriculture.