Monitoring plant diseases and pests through remote sensing technology: A review
By Jingcheng Zhang, Yanbo Huang, Ruiliang Pu, Pablo González-Moreno, Lin Yuan, Kaihua Wu, Wenjiang Huang. Computers and Electronics in Agriculture, 2019
512 citations recorded by the source corpus.
Abstract 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.
Subfields: Ecology, Plant Science
View Paper: https://www.semanticscholar.org/paper/3e7c34d19d25886c142897046550dea2a3be8626
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Ecology
In Plant Science
Citations in the corpus
- Applications of Remote Sensing in Precision Agriculture: A Review (2020, background): “Although studies have been conducted for plant disease classification [251], further efforts are needed to develop more accurate, automated and reproducible methodologies for disease detection under diverse climatic and real-world field conditions [40].”