Literature DB >> 30558964

Perspectives for Remote Sensing with Unmanned Aerial Vehicles in Precision Agriculture.

Wouter H Maes1, Kathy Steppe2.   

Abstract

Remote sensing with unmanned aerial vehicles (UAVs) is a game-changer in precision agriculture. It offers unprecedented spectral, spatial, and temporal resolution, but can also provide detailed vegetation height data and multiangular observations. In this article, we review the progress of remote sensing with UAVs in drought stress, in weed and pathogen detection, in nutrient status and growth vigor assessment, and in yield prediction. To transfer this knowledge to everyday practice of precision agriculture, future research should focus on exploiting the complementarity of hyperspectral or multispectral data with thermal data, on integrating observations into robust transfer or growth models rather than linear regression models, and on combining UAV products with other spatially explicit information.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  UAV; agriculture; drone; hyperspectral; multispectral; thermal

Mesh:

Year:  2018        PMID: 30558964     DOI: 10.1016/j.tplants.2018.11.007

Source DB:  PubMed          Journal:  Trends Plant Sci        ISSN: 1360-1385            Impact factor:   18.313


  30 in total

Review 1.  Recent Advances in Plant Nanoscience.

Authors:  Qi Zhang; Yibin Ying; Jianfeng Ping
Journal:  Adv Sci (Weinh)       Date:  2021-11-10       Impact factor: 16.806

2.  Agricultural plant cataloging and establishment of a data framework from UAV-based crop images by computer vision.

Authors:  Maurice Günder; Facundo R Ispizua Yamati; Jana Kierdorf; Ribana Roscher; Anne-Katrin Mahlein; Christian Bauckhage
Journal:  Gigascience       Date:  2022-06-17       Impact factor: 7.658

3.  Novel Vegetation Indices to Identify Broccoli Plants Infected With Xanthomonas campestris pv. campestris.

Authors:  Mónica Pineda; María Luisa Pérez-Bueno; Matilde Barón
Journal:  Front Plant Sci       Date:  2022-06-23       Impact factor: 6.627

4.  Object-Based Image Analysis Applied to Low Altitude Aerial Imagery for Potato Plant Trait Retrieval and Pathogen Detection.

Authors:  Jasper Siebring; João Valente; Marston Heracles Domingues Franceschini; Jan Kamp; Lammert Kooistra
Journal:  Sensors (Basel)       Date:  2019-12-12       Impact factor: 3.576

5.  Airborne Visual Detection and Tracking of Cooperative UAVs Exploiting Deep Learning.

Authors:  Roberto Opromolla; Giuseppe Inchingolo; Giancarmine Fasano
Journal:  Sensors (Basel)       Date:  2019-10-07       Impact factor: 3.576

6.  Millimeter-Level Plant Disease Detection From Aerial Photographs via Deep Learning and Crowdsourced Data.

Authors:  Tyr Wiesner-Hanks; Harvey Wu; Ethan Stewart; Chad DeChant; Nicholas Kaczmar; Hod Lipson; Michael A Gore; Rebecca J Nelson
Journal:  Front Plant Sci       Date:  2019-12-12       Impact factor: 5.753

7.  Development of a Light-Weight Unmanned Aerial Vehicle for Precision Agriculture.

Authors:  Uchechi F Ukaegbu; Lagouge K Tartibu; Modestus O Okwu; Isaac O Olayode
Journal:  Sensors (Basel)       Date:  2021-06-28       Impact factor: 3.576

8.  Phenotyping of Plant Biomass and Performance Traits Using Remote Sensing Techniques in Pea (Pisum sativum, L.).

Authors:  Juan José Quirós Vargas; Chongyuan Zhang; Jamin A Smitchger; Rebecca J McGee; Sindhuja Sankaran
Journal:  Sensors (Basel)       Date:  2019-04-30       Impact factor: 3.576

Review 9.  Progress and development on biological information of crop phenotype research applied to real-time variable-rate fertilization.

Authors:  Yinyan Shi; Yang Zhu; Xiaochan Wang; Xin Sun; Yangfen Ding; Wexing Cao; Zhichao Hu
Journal:  Plant Methods       Date:  2020-02-03       Impact factor: 4.993

10.  Maize Crop Coefficient Estimated from UAV-Measured Multispectral Vegetation Indices.

Authors:  Yu Zhang; Wenting Han; Xiaotao Niu; Guang Li
Journal:  Sensors (Basel)       Date:  2019-11-29       Impact factor: 3.576

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