Literature DB >> 32564316

A review: application of remote sensing as a promising strategy for insect pests and diseases management.

Nesreen M Abd El-Ghany1, Shadia E Abd El-Aziz2, Shahira S Marei2.   

Abstract

The present review provides a perspective angle on the historical and cutting-edge strategies of remote sensing techniques and its applications, especially for insect pest and plant disease management. Remote sensing depends on measuring, recording, and processing the electromagnetic radiation reflected and emitted from the ground target. Remote sensing applications depend on the spectral behavior of living organisms. Today, remote sensing is used as an effective tool for the detection, forecasting, and management of insect pests and plant diseases on different fruit orchards and crops. The main objectives of these applications were to collate data that help in decision-making for insect pest management and decreasing the environmental pollution of chemical pesticides. Airborne remote sensing has been a promising and useful tool for insect pest management and weed detection. Furthermore, remote sensing using satellite information proved to be a promising tool in forecasting and monitoring the distribution of locust species. It has also been used to help farmers in the early detection of mite infestation in cotton fields using multi-spectral systems, which depend on color changes in canopy semblance over time. Remote sensing can provide fast and accurate forecasting of targeted insect pests and subsequently minimizing pest damage and the management costs.

Keywords:  Applications; Insect pests; Management; Plant disease; Plant protection; Remote sensing

Year:  2020        PMID: 32564316     DOI: 10.1007/s11356-020-09517-2

Source DB:  PubMed          Journal:  Environ Sci Pollut Res Int        ISSN: 0944-1344            Impact factor:   4.223


  5 in total

Review 1.  Fluorescence-Based Sensing of Pesticides Using Supramolecular Chemistry.

Authors:  Mindy Levine
Journal:  Front Chem       Date:  2021-04-16       Impact factor: 5.221

2.  Improved CNN Method for Crop Pest Identification Based on Transfer Learning.

Authors:  Yiwen Liu; Xian Zhang; Yanxia Gao; Taiguo Qu; Yuanquan Shi
Journal:  Comput Intell Neurosci       Date:  2022-03-16

3.  An intelligent monitoring system of diseases and pests on rice canopy.

Authors:  Suxuan Li; Zelin Feng; Baojun Yang; Hang Li; Fubing Liao; Yufan Gao; Shuhua Liu; Jian Tang; Qing Yao
Journal:  Front Plant Sci       Date:  2022-08-11       Impact factor: 6.627

4.  Characterization of Pharmaceutical Tablets Using UV Hyperspectral Imaging as a Rapid In-Line Analysis Tool.

Authors:  Mohammad Al Ktash; Mona Stefanakis; Barbara Boldrini; Edwin Ostertag; Marc Brecht
Journal:  Sensors (Basel)       Date:  2021-06-28       Impact factor: 3.576

Review 5.  Application of Remote Sensing Data for Locust Research and Management-A Review.

Authors:  Igor Klein; Natascha Oppelt; Claudia Kuenzer
Journal:  Insects       Date:  2021-03-09       Impact factor: 2.769

  5 in total

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