Literature DB >> 31811713

Drones: Innovative Technology for Use in Precision Pest Management.

Fernando H Iost Filho1, Wieke B Heldens2, Zhaodan Kong3, Elvira S de Lange4.   

Abstract

Arthropod pest outbreaks are unpredictable and not uniformly distributed within fields. Early outbreak detection and treatment application are inherent to effective pest management, allowing management decisions to be implemented before pests are well-established and crop losses accrue. Pest monitoring is time-consuming and may be hampered by lack of reliable or cost-effective sampling techniques. Thus, we argue that an important research challenge associated with enhanced sustainability of pest management in modern agriculture is developing and promoting improved crop monitoring procedures. Biotic stress, such as herbivory by arthropod pests, elicits physiological defense responses in plants, leading to changes in leaf reflectance. Advanced imaging technologies can detect such changes, and can, therefore, be used as noninvasive crop monitoring methods. Furthermore, novel methods of treatment precision application are required. Both sensing and actuation technologies can be mounted on equipment moving through fields (e.g., irrigation equipment), on (un)manned driving vehicles, and on small drones. In this review, we focus specifically on use of small unmanned aerial robots, or small drones, in agricultural systems. Acquired and processed canopy reflectance data obtained with sensing drones could potentially be transmitted as a digital map to guide a second type of drone, actuation drones, to deliver solutions to the identified pest hotspots, such as precision releases of natural enemies and/or precision-sprays of pesticides. We emphasize how sustainable pest management in 21st-century agriculture will depend heavily on novel technologies, and how this trend will lead to a growing need for multi-disciplinary research collaborations between agronomists, ecologists, software programmers, and engineers.
© The Author(s) 2019. Published by Oxford University Press on behalf of Entomological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Keywords:  biological control; integrated pest management; precision agriculture; remote sensing; unmanned aerial system

Year:  2019        PMID: 31811713     DOI: 10.1093/jee/toz268

Source DB:  PubMed          Journal:  J Econ Entomol        ISSN: 0022-0493            Impact factor:   2.381


  8 in total

1.  Droplet distribution in cotton canopy using single-rotor and four-rotor unmanned aerial vehicles.

Authors:  Yanhua Meng; Yan Ma; Zhiguo Wang; Hongyan Hu
Journal:  PeerJ       Date:  2022-06-14       Impact factor: 3.061

2.  'Drone-Netting' for Sampling Live Insects.

Authors:  Helge Löcken; Ottmar W Fischer; Jürgen Selz; Michael Boppré
Journal:  J Insect Sci       Date:  2020-08-01       Impact factor: 1.857

3.  The development of autonomous unmanned aircraft systems for mosquito control.

Authors:  Gregory M Williams; Yi Wang; Devi S Suman; Isik Unlu; Randy Gaugler
Journal:  PLoS One       Date:  2020-09-18       Impact factor: 3.240

4.  Monitoring Bemisia tabaci (Gennadius) (Hemiptera: Aleyrodidae) Infestation in Soybean by Proximal Sensing.

Authors:  Pedro P S Barros; Inana X Schutze; Fernando H Iost Filho; Pedro T Yamamoto; Peterson R Fiorio; José A M Demattê
Journal:  Insects       Date:  2021-01-09       Impact factor: 2.769

5.  Inferring Agronomical Insights for Wheat Canopy Using Image-Based Curve Fit K-Means Segmentation Algorithm and Statistical Analysis.

Authors:  Ankita Gupta; Lakhwinder Kaur; Gurmeet Kaur
Journal:  Int J Genomics       Date:  2022-01-31       Impact factor: 2.326

6.  Data-driven vermiculite distribution modelling for UAV-based precision pest management.

Authors:  Na Ma; Anil Mantri; Graham Bough; Ayush Patnaik; Siddhesh Yadav; Christian Nansen; Zhaodan Kong
Journal:  Front Robot AI       Date:  2022-08-10

7.  Deep learning for automated detection of Drosophila suzukii: potential for UAV-based monitoring.

Authors:  Peter Pj Roosjen; Benjamin Kellenberger; Lammert Kooistra; David R Green; Johannes Fahrentrapp
Journal:  Pest Manag Sci       Date:  2020-04-20       Impact factor: 4.845

8.  Tracking Red Palm Mite Damage in the Western Hemisphere Invasion with Landsat Remote Sensing Data.

Authors:  Jose Carlos Verle Rodrigues; Michael H Cosh; E Raymond Hunt; Gilberto J de Moraes; Geovanny Barroso; William A White; Ronald Ochoa
Journal:  Insects       Date:  2020-09-11       Impact factor: 2.769

  8 in total

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