A Review on Plant Disease Detection Using Hyperspectral Imaging
Rakiba Rayhana, Zhenyu Ma, Zheng Liu, Gaozhi Xiao, Yuefeng Ruan, Jatinder S. Sangha
Abstract
Agriculture production is one of the fundamental contributors to a nation's economic development. Every year, plant diseases result in significant crop losses that threaten the global food supply chain. Early estimation of plant diseases could play an essential role in safeguarding crops and fostering economic growth. Recently, hyperspectral imaging techniques have emerged as powerful tools for early disease detection, as they have demonstrated capabilities to detect plant diseases from tissue to canopy levels. This article provides an extensive overview of the principles, types, and operating platforms of hyperspectral image sensors. Furthermore, this article delves into the specifics of these sensors' application in plant disease detection, including disease identification, classification, severity analysis, and understanding genetic resistance. In addition, this article addresses the current challenges in the field and suggests potential solutions to mitigate these pressing issues. Finally, this article outlines the promising future trends and directions of hyperspectral imaging in plant disease detection and analysis. With continuous improvement and application, these imaging techniques have great potential to revolutionize plant disease management, thereby enhancing agricultural productivity and ensuring food security.