Next generation of computer vision for plant disease monitoring in precision agriculture: A contemporary survey, taxonomy, experiments, and future direction

Abstract

Efficient and rational monitoring of plant health is an essential prerequisite for ensuring optimal crop production and resource management in the field of agriculture. Computer vision techniques have revolutionized visual disease monitoring with their exceptional visual recognition performance. However, despite the outstanding results, the widespread acceptance of these methods in agriculture practice is still in its early stages. This study presents a comprehensive survey of the next generation of computer vision models applied to plant disease monitoring in precision agriculture. Our study begins by tracing the evolution of agricultural computer vision research over the past decade, encompassing legacy methods such as convolutional neural networks (CNNs), progressing to newer techniques like vision transformers (ViTs), and culminating in cutting-edge vision multi-layer perceptrons (MLPs). Next, our study embraces both qualitative and quantitative approaches, supporting a profound review of literature and classifying methodologies and experimental approaches. A significant contribution lies in our comprehensive taxonomy, offering a fine-grained categorization of current computer vision models. This taxonomy meticulously highlights the potentials and limitations of these models while explaining their roles in improving plant disease management. Moreover, extensive experimental comparisons are conducted on PlantVillage dataset to evaluate the performance of state-of-the-art computer-vision models for plant recognition data. The obtained results are then utilized to draw insightful conclusions about the behavior of these models and provide guidance for selecting the most suitable one for specific tasks at hand. Additionally, we discuss open research avenues and future directions of computer-vision models in plant disease management including challenges related to the data scarcity, the computational efficiency, need for explainability, and multi-modal analysis.