Monitoring Plant Diseases and Pests through Remote Sensing Technology: A Review

Document Type


Publication Date



Remote sensing, Plant diseases and pests, Features, Models, Algorithms

Digital Object Identifier (DOI)


Plant diseases and pests endanger agriculture and forestry significantly around the world. The implementation of non-contact, highly-efficient, and affordable methods for detecting and monitoring plant diseases and pests over vast areas could greatly facilitate plant protection. In this respect, different forms of remote sensing methods have been introduced for detecting and monitoring plant diseases and pests in many ways. This review outlines the state-of-the-art research achievements in relation to sensing technologies, feature extraction, and monitoring algorithms that have been conducted at multiple scales. Based on their characteristics and maturity in detecting and monitoring plant diseases and pests, sensing systems are classified into groups that include: visible & near-infrared spectral sensors (VIS-NIR); fluorescence and thermal sensors; and synthetic aperture radar (SAR) and light detection and ranging (Lidar) systems. Based on the data acquired from these remote sensing systems and sensitivity analysis, a variety of remote sensing features are proposed and identified as surrogates in the detection and monitoring processes. They include (1) optical, fluorescence, and thermal parameters; (2) image-based landscape features; and (3) features associated with habitat suitability. We also review the algorithms that link the remote sensing features with the occurrence of plant diseases and pests for identifying, differentiating and determining severity of diseases and pests over large areas. The algorithms including statistical discriminant analyses, machine learning algorithms, regression-based models and spectral unmixing algorithms using data collected at a single time or multiple times. Finally, according to the review, we provide a general framework to facilitate the monitoring of an unknown disease or pest highlighting future challenges and trends.

Was this content written or created while at USF?


Citation / Publisher Attribution

Computers and Electronics in Agriculture, v. 165, art. 104943